Statistical Modeling and Prediction for Tourism Economy Using Dendritic Neural Network

PubMed Central

Yu, Ying; Wang, Yirui; Tang, Zheng

2017-01-01

With the impact of global internationalization, tourism economy has also been a rapid development. The increasing interest aroused by more advanced forecasting methods leads us to innovate forecasting methods. In this paper, the seasonal trend autoregressive integrated moving averages with dendritic neural network model (SA-D model) is proposed to perform the tourism demand forecasting. First, we use the seasonal trend autoregressive integrated moving averages model (SARIMA model) to exclude the long-term linear trend and then train the residual data by the dendritic neural network model and make a short-term prediction. As the result showed in this paper, the SA-D model can achieve considerably better predictive performances. In order to demonstrate the effectiveness of the SA-D model, we also use the data that other authors used in the other models and compare the results. It also proved that the SA-D model achieved good predictive performances in terms of the normalized mean square error, absolute percentage of error, and correlation coefficient. PMID:28246527

Statistical Modeling and Prediction for Tourism Economy Using Dendritic Neural Network.

PubMed

Yu, Ying; Wang, Yirui; Gao, Shangce; Tang, Zheng

2017-01-01

With the impact of global internationalization, tourism economy has also been a rapid development. The increasing interest aroused by more advanced forecasting methods leads us to innovate forecasting methods. In this paper, the seasonal trend autoregressive integrated moving averages with dendritic neural network model (SA-D model) is proposed to perform the tourism demand forecasting. First, we use the seasonal trend autoregressive integrated moving averages model (SARIMA model) to exclude the long-term linear trend and then train the residual data by the dendritic neural network model and make a short-term prediction. As the result showed in this paper, the SA-D model can achieve considerably better predictive performances. In order to demonstrate the effectiveness of the SA-D model, we also use the data that other authors used in the other models and compare the results. It also proved that the SA-D model achieved good predictive performances in terms of the normalized mean square error, absolute percentage of error, and correlation coefficient.

Water balance models in one-month-ahead streamflow forecasting

USGS Publications Warehouse

Alley, William M.

1985-01-01

Techniques are tested that incorporate information from water balance models in making 1-month-ahead streamflow forecasts in New Jersey. The results are compared to those based on simple autoregressive time series models. The relative performance of the models is dependent on the month of the year in question. The water balance models are most useful for forecasts of April and May flows. For the stations in northern New Jersey, the April and May forecasts were made in order of decreasing reliability using the water-balance-based approaches, using the historical monthly means, and using simple autoregressive models. The water balance models were useful to a lesser extent for forecasts during the fall months. For the rest of the year the improvements in forecasts over those obtained using the simpler autoregressive models were either very small or the simpler models provided better forecasts. When using the water balance models, monthly corrections for bias are found to improve minimum mean-square-error forecasts as well as to improve estimates of the forecast conditional distributions.

Hybrid methodology for tuberculosis incidence time-series forecasting based on ARIMA and a NAR neural network.

PubMed

Wang, K W; Deng, C; Li, J P; Zhang, Y Y; Li, X Y; Wu, M C

2017-04-01

Tuberculosis (TB) affects people globally and is being reconsidered as a serious public health problem in China. Reliable forecasting is useful for the prevention and control of TB. This study proposes a hybrid model combining autoregressive integrated moving average (ARIMA) with a nonlinear autoregressive (NAR) neural network for forecasting the incidence of TB from January 2007 to March 2016. Prediction performance was compared between the hybrid model and the ARIMA model. The best-fit hybrid model was combined with an ARIMA (3,1,0) × (0,1,1)12 and NAR neural network with four delays and 12 neurons in the hidden layer. The ARIMA-NAR hybrid model, which exhibited lower mean square error, mean absolute error, and mean absolute percentage error of 0·2209, 0·1373, and 0·0406, respectively, in the modelling performance, could produce more accurate forecasting of TB incidence compared to the ARIMA model. This study shows that developing and applying the ARIMA-NAR hybrid model is an effective method to fit the linear and nonlinear patterns of time-series data, and this model could be helpful in the prevention and control of TB.

Theoretical results on fractionally integrated exponential generalized autoregressive conditional heteroskedastic processes

NASA Astrophysics Data System (ADS)

Lopes, Sílvia R. C.; Prass, Taiane S.

2014-05-01

Here we present a theoretical study on the main properties of Fractionally Integrated Exponential Generalized Autoregressive Conditional Heteroskedastic (FIEGARCH) processes. We analyze the conditions for the existence, the invertibility, the stationarity and the ergodicity of these processes. We prove that, if { is a FIEGARCH(p,d,q) process then, under mild conditions, { is an ARFIMA(q,d,0) with correlated innovations, that is, an autoregressive fractionally integrated moving average process. The convergence order for the polynomial coefficients that describes the volatility is presented and results related to the spectral representation and to the covariance structure of both processes { and { are discussed. Expressions for the kurtosis and the asymmetry measures for any stationary FIEGARCH(p,d,q) process are also derived. The h-step ahead forecast for the processes {, { and { are given with their respective mean square error of forecast. The work also presents a Monte Carlo simulation study showing how to generate, estimate and forecast based on six different FIEGARCH models. The forecasting performance of six models belonging to the class of autoregressive conditional heteroskedastic models (namely, ARCH-type models) and radial basis models is compared through an empirical application to Brazilian stock market exchange index.

An iteratively reweighted least-squares approach to adaptive robust adjustment of parameters in linear regression models with autoregressive and t-distributed deviations

NASA Astrophysics Data System (ADS)

Kargoll, Boris; Omidalizarandi, Mohammad; Loth, Ina; Paffenholz, Jens-André; Alkhatib, Hamza

2018-03-01

In this paper, we investigate a linear regression time series model of possibly outlier-afflicted observations and autocorrelated random deviations. This colored noise is represented by a covariance-stationary autoregressive (AR) process, in which the independent error components follow a scaled (Student's) t-distribution. This error model allows for the stochastic modeling of multiple outliers and for an adaptive robust maximum likelihood (ML) estimation of the unknown regression and AR coefficients, the scale parameter, and the degree of freedom of the t-distribution. This approach is meant to be an extension of known estimators, which tend to focus only on the regression model, or on the AR error model, or on normally distributed errors. For the purpose of ML estimation, we derive an expectation conditional maximization either algorithm, which leads to an easy-to-implement version of iteratively reweighted least squares. The estimation performance of the algorithm is evaluated via Monte Carlo simulations for a Fourier as well as a spline model in connection with AR colored noise models of different orders and with three different sampling distributions generating the white noise components. We apply the algorithm to a vibration dataset recorded by a high-accuracy, single-axis accelerometer, focusing on the evaluation of the estimated AR colored noise model.

KARMA4

DOE Office of Scientific and Technical Information (OSTI.GOV)

Khalil, Mohammad; Salloum, Maher; Lee, Jina

2017-07-10

KARMA4 is a C++ library for autoregressive moving average (ARMA) modeling and forecasting of time-series data while incorporating both process and observation error. KARMA4 is designed for fitting and forecasting of time-series data for predictive purposes.

Central Procurement Workload Projection Model

DTIC Science & Technology

1981-02-01

generated by the P&P Directorates such as procurement actions (PA’s) are pursued. Specifi- cally, Box-Jenkins Autoregressive Integrated Moving Average...Breakout of PA’s to over and under $10,000 23 IV. FINDINGS AND RECOMMENDATIONS 24 A. General 24 B. Findings 24 C. Recommendations 25...the model will predict the actual values and hence the error will be zero . Therefore, after forecasting 3 quarters into the future no error

Evaluation of statistical models for forecast errors from the HBV model

NASA Astrophysics Data System (ADS)

Engeland, Kolbjørn; Renard, Benjamin; Steinsland, Ingelin; Kolberg, Sjur

2010-04-01

SummaryThree statistical models for the forecast errors for inflow into the Langvatn reservoir in Northern Norway have been constructed and tested according to the agreement between (i) the forecast distribution and the observations and (ii) median values of the forecast distribution and the observations. For the first model observed and forecasted inflows were transformed by the Box-Cox transformation before a first order auto-regressive model was constructed for the forecast errors. The parameters were conditioned on weather classes. In the second model the Normal Quantile Transformation (NQT) was applied on observed and forecasted inflows before a similar first order auto-regressive model was constructed for the forecast errors. For the third model positive and negative errors were modeled separately. The errors were first NQT-transformed before conditioning the mean error values on climate, forecasted inflow and yesterday's error. To test the three models we applied three criterions: we wanted (a) the forecast distribution to be reliable; (b) the forecast intervals to be narrow; (c) the median values of the forecast distribution to be close to the observed values. Models 1 and 2 gave almost identical results. The median values improved the forecast with Nash-Sutcliffe R eff increasing from 0.77 for the original forecast to 0.87 for the corrected forecasts. Models 1 and 2 over-estimated the forecast intervals but gave the narrowest intervals. Their main drawback was that the distributions are less reliable than Model 3. For Model 3 the median values did not fit well since the auto-correlation was not accounted for. Since Model 3 did not benefit from the potential variance reduction that lies in bias estimation and removal it gave on average wider forecasts intervals than the two other models. At the same time Model 3 on average slightly under-estimated the forecast intervals, probably explained by the use of average measures to evaluate the fit.

A Novel Modeling Method for Aircraft Engine Using Nonlinear Autoregressive Exogenous (NARX) Models Based on Wavelet Neural Networks

NASA Astrophysics Data System (ADS)

Yu, Bing; Shu, Wenjun; Cao, Can

2018-05-01

A novel modeling method for aircraft engine using nonlinear autoregressive exogenous (NARX) models based on wavelet neural networks is proposed. The identification principle and process based on wavelet neural networks are studied, and the modeling scheme based on NARX is proposed. Then, the time series data sets from three types of aircraft engines are utilized to build the corresponding NARX models, and these NARX models are validated by the simulation. The results show that all the best NARX models can capture the original aircraft engine's dynamic characteristic well with the high accuracy. For every type of engine, the relative identification errors of its best NARX model and the component level model are no more than 3.5 % and most of them are within 1 %.

Level shift two-components autoregressive conditional heteroscedasticity modelling for WTI crude oil market

NASA Astrophysics Data System (ADS)

Sin, Kuek Jia; Cheong, Chin Wen; Hooi, Tan Siow

2017-04-01

This study aims to investigate the crude oil volatility using a two components autoregressive conditional heteroscedasticity (ARCH) model with the inclusion of abrupt jump feature. The model is able to capture abrupt jumps, news impact, clustering volatility, long persistence volatility and heavy-tailed distributed error which are commonly observed in the crude oil time series. For the empirical study, we have selected the WTI crude oil index from year 2000 to 2016. The results found that by including the multiple-abrupt jumps in ARCH model, there are significant improvements of estimation evaluations as compared with the standard ARCH models. The outcomes of this study can provide useful information for risk management and portfolio analysis in the crude oil markets.

An improved portmanteau test for autocorrelated errors in interrupted time-series regression models.

PubMed

Huitema, Bradley E; McKean, Joseph W

2007-08-01

A new portmanteau test for autocorrelation among the errors of interrupted time-series regression models is proposed. Simulation results demonstrate that the inferential properties of the proposed Q(H-M) test statistic are considerably more satisfactory than those of the well known Ljung-Box test and moderately better than those of the Box-Pierce test. These conclusions generally hold for a wide variety of autoregressive (AR), moving averages (MA), and ARMA error processes that are associated with time-series regression models of the form described in Huitema and McKean (2000a, 2000b).

Comparison of Two Hybrid Models for Forecasting the Incidence of Hemorrhagic Fever with Renal Syndrome in Jiangsu Province, China.

PubMed

Wu, Wei; Guo, Junqiao; An, Shuyi; Guan, Peng; Ren, Yangwu; Xia, Linzi; Zhou, Baosen

2015-01-01

Cases of hemorrhagic fever with renal syndrome (HFRS) are widely distributed in eastern Asia, especially in China, Russia, and Korea. It is proved to be a difficult task to eliminate HFRS completely because of the diverse animal reservoirs and effects of global warming. Reliable forecasting is useful for the prevention and control of HFRS. Two hybrid models, one composed of nonlinear autoregressive neural network (NARNN) and autoregressive integrated moving average (ARIMA) the other composed of generalized regression neural network (GRNN) and ARIMA were constructed to predict the incidence of HFRS in the future one year. Performances of the two hybrid models were compared with ARIMA model. The ARIMA, ARIMA-NARNN ARIMA-GRNN model fitted and predicted the seasonal fluctuation well. Among the three models, the mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of ARIMA-NARNN hybrid model was the lowest both in modeling stage and forecasting stage. As for the ARIMA-GRNN hybrid model, the MSE, MAE and MAPE of modeling performance and the MSE and MAE of forecasting performance were less than the ARIMA model, but the MAPE of forecasting performance did not improve. Developing and applying the ARIMA-NARNN hybrid model is an effective method to make us better understand the epidemic characteristics of HFRS and could be helpful to the prevention and control of HFRS.

Autoregressive Modeling of Drift and Random Error to Characterize a Continuous Intravascular Glucose Monitoring Sensor.

PubMed

Zhou, Tony; Dickson, Jennifer L; Geoffrey Chase, J

2018-01-01

Continuous glucose monitoring (CGM) devices have been effective in managing diabetes and offer potential benefits for use in the intensive care unit (ICU). Use of CGM devices in the ICU has been limited, primarily due to the higher point accuracy errors over currently used traditional intermittent blood glucose (BG) measures. General models of CGM errors, including drift and random errors, are lacking, but would enable better design of protocols to utilize these devices. This article presents an autoregressive (AR) based modeling method that separately characterizes the drift and random noise of the GlySure CGM sensor (GlySure Limited, Oxfordshire, UK). Clinical sensor data (n = 33) and reference measurements were used to generate 2 AR models to describe sensor drift and noise. These models were used to generate 100 Monte Carlo simulations based on reference blood glucose measurements. These were then compared to the original CGM clinical data using mean absolute relative difference (MARD) and a Trend Compass. The point accuracy MARD was very similar between simulated and clinical data (9.6% vs 9.9%). A Trend Compass was used to assess trend accuracy, and found simulated and clinical sensor profiles were similar (simulated trend index 11.4° vs clinical trend index 10.9°). The model and method accurately represents cohort sensor behavior over patients, providing a general modeling approach to any such sensor by separately characterizing each type of error that can arise in the data. Overall, it enables better protocol design based on accurate expected CGM sensor behavior, as well as enabling the analysis of what level of each type of sensor error would be necessary to obtain desired glycemic control safety and performance with a given protocol.

Estimating long-run equilibrium real exchange rates: short-lived shocks with long-lived impacts on Pakistan.

PubMed

Zardad, Asma; Mohsin, Asma; Zaman, Khalid

2013-12-01

The purpose of this study is to investigate the factors that affect real exchange rate volatility for Pakistan through the co-integration and error correction model over a 30-year time period, i.e. between 1980 and 2010. The study employed the autoregressive conditional heteroskedasticity (ARCH), generalized autoregressive conditional heteroskedasticity (GARCH) and Vector Error Correction model (VECM) to estimate the changes in the volatility of real exchange rate series, while an error correction model was used to determine the short-run dynamics of the system. The study is limited to a few variables i.e., productivity differential (i.e., real GDP per capita relative to main trading partner); terms of trade; trade openness and government expenditures in order to manage robust data. The result indicates that real effective exchange rate (REER) has been volatile around its equilibrium level; while, the speed of adjustment is relatively slow. VECM results confirm long run convergence of real exchange rate towards its equilibrium level. Results from ARCH and GARCH estimation shows that real shocks volatility persists, so that shocks die out rather slowly, and lasting misalignment seems to have occurred.

A Hybrid Model for Predicting the Prevalence of Schistosomiasis in Humans of Qianjiang City, China

PubMed Central

Wang, Ying; Lu, Zhouqin; Tian, Lihong; Tan, Li; Shi, Yun; Nie, Shaofa; Liu, Li

2014-01-01

Backgrounds/Objective Schistosomiasis is still a major public health problem in China, despite the fact that the government has implemented a series of strategies to prevent and control the spread of the parasitic disease. Advanced warning and reliable forecasting can help policymakers to adjust and implement strategies more effectively, which will lead to the control and elimination of schistosomiasis. Our aim is to explore the application of a hybrid forecasting model to track the trends of the prevalence of schistosomiasis in humans, which provides a methodological basis for predicting and detecting schistosomiasis infection in endemic areas. Methods A hybrid approach combining the autoregressive integrated moving average (ARIMA) model and the nonlinear autoregressive neural network (NARNN) model to forecast the prevalence of schistosomiasis in the future four years. Forecasting performance was compared between the hybrid ARIMA-NARNN model, and the single ARIMA or the single NARNN model. Results The modelling mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of the ARIMA-NARNN model was 0.1869×10−4, 0.0029, 0.0419 with a corresponding testing error of 0.9375×10−4, 0.0081, 0.9064, respectively. These error values generated with the hybrid model were all lower than those obtained from the single ARIMA or NARNN model. The forecasting values were 0.75%, 0.80%, 0.76% and 0.77% in the future four years, which demonstrated a no-downward trend. Conclusion The hybrid model has high quality prediction accuracy in the prevalence of schistosomiasis, which provides a methodological basis for future schistosomiasis monitoring and control strategies in the study area. It is worth attempting to utilize the hybrid detection scheme in other schistosomiasis-endemic areas including other infectious diseases. PMID:25119882

Increased performance in the short-term water demand forecasting through the use of a parallel adaptive weighting strategy

NASA Astrophysics Data System (ADS)

Sardinha-Lourenço, A.; Andrade-Campos, A.; Antunes, A.; Oliveira, M. S.

2018-03-01

Recent research on water demand short-term forecasting has shown that models using univariate time series based on historical data are useful and can be combined with other prediction methods to reduce errors. The behavior of water demands in drinking water distribution networks focuses on their repetitive nature and, under meteorological conditions and similar consumers, allows the development of a heuristic forecast model that, in turn, combined with other autoregressive models, can provide reliable forecasts. In this study, a parallel adaptive weighting strategy of water consumption forecast for the next 24-48 h, using univariate time series of potable water consumption, is proposed. Two Portuguese potable water distribution networks are used as case studies where the only input data are the consumption of water and the national calendar. For the development of the strategy, the Autoregressive Integrated Moving Average (ARIMA) method and a short-term forecast heuristic algorithm are used. Simulations with the model showed that, when using a parallel adaptive weighting strategy, the prediction error can be reduced by 15.96% and the average error by 9.20%. This reduction is important in the control and management of water supply systems. The proposed methodology can be extended to other forecast methods, especially when it comes to the availability of multiple forecast models.

Parameter prediction based on Improved Process neural network and ARMA error compensation in Evaporation Process

NASA Astrophysics Data System (ADS)

Qian, Xiaoshan

2018-01-01

The traditional model of evaporation process parameters have continuity and cumulative characteristics of the prediction error larger issues, based on the basis of the process proposed an adaptive particle swarm neural network forecasting method parameters established on the autoregressive moving average (ARMA) error correction procedure compensated prediction model to predict the results of the neural network to improve prediction accuracy. Taking a alumina plant evaporation process to analyze production data validation, and compared with the traditional model, the new model prediction accuracy greatly improved, can be used to predict the dynamic process of evaporation of sodium aluminate solution components.

[Model of multiple seasonal autoregressive integrated moving average model and its application in prediction of the hand-foot-mouth disease incidence in Changsha].

PubMed

Tan, Ting; Chen, Lizhang; Liu, Fuqiang

2014-11-01

To establish multiple seasonal autoregressive integrated moving average model (ARIMA) according to the hand-foot-mouth disease incidence in Changsha, and to explore the feasibility of the multiple seasonal ARIMA in predicting the hand-foot-mouth disease incidence. EVIEWS 6.0 was used to establish multiple seasonal ARIMA according to the hand-foot- mouth disease incidence from May 2008 to August 2013 in Changsha, and the data of the hand- foot-mouth disease incidence from September 2013 to February 2014 were served as the examined samples of the multiple seasonal ARIMA, then the errors were compared between the forecasted incidence and the real value. Finally, the incidence of hand-foot-mouth disease from March 2014 to August 2014 was predicted by the model. After the data sequence was handled by smooth sequence, model identification and model diagnosis, the multiple seasonal ARIMA (1, 0, 1)×(0, 1, 1)12 was established. The R2 value of the model fitting degree was 0.81, the root mean square prediction error was 8.29 and the mean absolute error was 5.83. The multiple seasonal ARIMA is a good prediction model, and the fitting degree is good. It can provide reference for the prevention and control work in hand-foot-mouth disease.

On the Feed-back Mechanism of Chinese Stock Markets

NASA Astrophysics Data System (ADS)

Lu, Shu Quan; Ito, Takao; Zhang, Jianbo

Feed-back models in the stock markets research imply an adjustment process toward investors' expectation for current information and past experiences. Error-correction and cointegration are often used to evaluate the long-run relation. The Efficient Capital Market Hypothesis, which had ignored the effect of the accumulation of information, cannot explain some anomalies such as bubbles and partial predictability in the stock markets. In order to investigate the feed-back mechanism and to determine an effective model, we use daily data of the stock index of two Chinese stock markets with the expectational model, which is one kind of geometric lag models. Tests and estimations of error-correction show that long-run equilibrium seems to be seldom achieved in Chinese stock markets. Our result clearly shows the common coefficient of expectations and fourth-order autoregressive disturbance exist in the two Chinese stock markets. Furthermore, we find the same coefficient of expectations has an autoregressive effect on disturbances in the two Chinese stock markets. Therefore the presence of such feed-back is also supported in Chinese stock markets.

Application of a Combined Model with Autoregressive Integrated Moving Average (ARIMA) and Generalized Regression Neural Network (GRNN) in Forecasting Hepatitis Incidence in Heng County, China

PubMed Central

Liang, Hao; Gao, Lian; Liang, Bingyu; Huang, Jiegang; Zang, Ning; Liao, Yanyan; Yu, Jun; Lai, Jingzhen; Qin, Fengxiang; Su, Jinming; Ye, Li; Chen, Hui

2016-01-01

Background Hepatitis is a serious public health problem with increasing cases and property damage in Heng County. It is necessary to develop a model to predict the hepatitis epidemic that could be useful for preventing this disease. Methods The autoregressive integrated moving average (ARIMA) model and the generalized regression neural network (GRNN) model were used to fit the incidence data from the Heng County CDC (Center for Disease Control and Prevention) from January 2005 to December 2012. Then, the ARIMA-GRNN hybrid model was developed. The incidence data from January 2013 to December 2013 were used to validate the models. Several parameters, including mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE) and mean square error (MSE), were used to compare the performance among the three models. Results The morbidity of hepatitis from Jan 2005 to Dec 2012 has seasonal variation and slightly rising trend. The ARIMA(0,1,2)(1,1,1)12 model was the most appropriate one with the residual test showing a white noise sequence. The smoothing factor of the basic GRNN model and the combined model was 1.8 and 0.07, respectively. The four parameters of the hybrid model were lower than those of the two single models in the validation. The parameters values of the GRNN model were the lowest in the fitting of the three models. Conclusions The hybrid ARIMA-GRNN model showed better hepatitis incidence forecasting in Heng County than the single ARIMA model and the basic GRNN model. It is a potential decision-supportive tool for controlling hepatitis in Heng County. PMID:27258555

Comparison of Two Hybrid Models for Forecasting the Incidence of Hemorrhagic Fever with Renal Syndrome in Jiangsu Province, China

PubMed Central

Wu, Wei; Guo, Junqiao; An, Shuyi; Guan, Peng; Ren, Yangwu; Xia, Linzi; Zhou, Baosen

2015-01-01

Background Cases of hemorrhagic fever with renal syndrome (HFRS) are widely distributed in eastern Asia, especially in China, Russia, and Korea. It is proved to be a difficult task to eliminate HFRS completely because of the diverse animal reservoirs and effects of global warming. Reliable forecasting is useful for the prevention and control of HFRS. Methods Two hybrid models, one composed of nonlinear autoregressive neural network (NARNN) and autoregressive integrated moving average (ARIMA) the other composed of generalized regression neural network (GRNN) and ARIMA were constructed to predict the incidence of HFRS in the future one year. Performances of the two hybrid models were compared with ARIMA model. Results The ARIMA, ARIMA-NARNN ARIMA-GRNN model fitted and predicted the seasonal fluctuation well. Among the three models, the mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of ARIMA-NARNN hybrid model was the lowest both in modeling stage and forecasting stage. As for the ARIMA-GRNN hybrid model, the MSE, MAE and MAPE of modeling performance and the MSE and MAE of forecasting performance were less than the ARIMA model, but the MAPE of forecasting performance did not improve. Conclusion Developing and applying the ARIMA-NARNN hybrid model is an effective method to make us better understand the epidemic characteristics of HFRS and could be helpful to the prevention and control of HFRS. PMID:26270814

A Comparison of Forecast Error Generators for Modeling Wind and Load Uncertainty

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lu, Ning; Diao, Ruisheng; Hafen, Ryan P.

2013-12-18

This paper presents four algorithms to generate random forecast error time series, including a truncated-normal distribution model, a state-space based Markov model, a seasonal autoregressive moving average (ARMA) model, and a stochastic-optimization based model. The error time series are used to create real-time (RT), hour-ahead (HA), and day-ahead (DA) wind and load forecast time series that statistically match historically observed forecasting data sets, used for variable generation integration studies. A comparison is made using historical DA load forecast and actual load values to generate new sets of DA forecasts with similar stoical forecast error characteristics. This paper discusses and comparesmore » the capabilities of each algorithm to preserve the characteristics of the historical forecast data sets.« less

TaiWan Ionospheric Model (TWIM) prediction based on time series autoregressive analysis

NASA Astrophysics Data System (ADS)

Tsai, L. C.; Macalalad, Ernest P.; Liu, C. H.

2014-10-01

As described in a previous paper, a three-dimensional ionospheric electron density (Ne) model has been constructed from vertical Ne profiles retrieved from the FormoSat3/Constellation Observing System for Meteorology, Ionosphere, and Climate GPS radio occultation measurements and worldwide ionosonde foF2 and foE data and named the TaiWan Ionospheric Model (TWIM). The TWIM exhibits vertically fitted α-Chapman-type layers with distinct F2, F1, E, and D layers, and surface spherical harmonic approaches for the fitted layer parameters including peak density, peak density height, and scale height. To improve the TWIM into a real-time model, we have developed a time series autoregressive model to forecast short-term TWIM coefficients. The time series of TWIM coefficients are considered as realizations of stationary stochastic processes within a processing window of 30 days. These autocorrelation coefficients are used to derive the autoregressive parameters and then forecast the TWIM coefficients, based on the least squares method and Lagrange multiplier technique. The forecast root-mean-square relative TWIM coefficient errors are generally <30% for 1 day predictions. The forecast TWIM values of foE and foF2 values are also compared and evaluated using worldwide ionosonde data.

Simulation And Forecasting of Daily Pm10 Concentrations Using Autoregressive Models In Kagithane Creek Valley, Istanbul

NASA Astrophysics Data System (ADS)

Ağaç, Kübra; Koçak, Kasım; Deniz, Ali

2015-04-01

A time series approach using autoregressive model (AR), moving average model (MA) and seasonal autoregressive integrated moving average model (SARIMA) were used in this study to simulate and forecast daily PM10 concentrations in Kagithane Creek Valley, Istanbul. Hourly PM10 concentrations have been measured in Kagithane Creek Valley between 2010 and 2014 periods. Bosphorus divides the city in two parts as European and Asian parts. The historical part of the city takes place in Golden Horn. Our study area Kagithane Creek Valley is connected with this historical part. The study area is highly polluted because of its topographical structure and industrial activities. Also population density is extremely high in this site. The dispersion conditions are highly poor in this creek valley so it is necessary to calculate PM10 levels for air quality and human health. For given period there were some missing PM10 concentration values so to make an accurate calculations and to obtain exact results gap filling method was applied by Singular Spectrum Analysis (SSA). SSA is a new and efficient method for gap filling and it is an state-of-art modeling. SSA-MTM Toolkit was used for our study. SSA is considered as a noise reduction algorithm because it decomposes an original time series to trend (if exists), oscillatory and noise components by way of a singular value decomposition. The basic SSA algorithm has stages of decomposition and reconstruction. For given period daily and monthly PM10 concentrations were calculated and episodic periods are determined. Long term and short term PM10 concentrations were analyzed according to European Union (EU) standards. For simulation and forecasting of high level PM10 concentrations, meteorological data (wind speed, pressure and temperature) were used to see the relationship between daily PM10 concentrations. Fast Fourier Transformation (FFT) was also applied to the data to see the periodicity and according to these periods models were built in MATLAB an Eviews programmes. Because of the seasonality of PM10 data SARIMA model was also used. The order of autoregression model was determined according to AIC and BIC criteria. The model performances were evaluated from Fractional Bias, Normalized Mean Square Error (NMSE) and Mean Absolute Percentage Error (MAPE). As expected, the results were encouraging. Keywords: PM10, Autoregression, Forecast Acknowledgement The authors would like to acknowledge the financial support by the Scientific and Technological Research Council of Turkey (TUBITAK, project no:112Y319).

Nonlinear autoregressive neural networks with external inputs for forecasting of typhoon inundation level.

PubMed

Ouyang, Huei-Tau

2017-08-01

Accurate inundation level forecasting during typhoon invasion is crucial for organizing response actions such as the evacuation of people from areas that could potentially flood. This paper explores the ability of nonlinear autoregressive neural networks with exogenous inputs (NARX) to predict inundation levels induced by typhoons. Two types of NARX architecture were employed: series-parallel (NARX-S) and parallel (NARX-P). Based on cross-correlation analysis of rainfall and water-level data from historical typhoon records, 10 NARX models (five of each architecture type) were constructed. The forecasting ability of each model was assessed by considering coefficient of efficiency (CE), relative time shift error (RTS), and peak water-level error (PE). The results revealed that high CE performance could be achieved by employing more model input variables. Comparisons of the two types of model demonstrated that the NARX-S models outperformed the NARX-P models in terms of CE and RTS, whereas both performed exceptionally in terms of PE and without significant difference. The NARX-S and NARX-P models with the highest overall performance were identified and their predictions were compared with those of traditional ARX-based models. The NARX-S model outperformed the ARX-based models in all three indexes, whereas the NARX-P model exhibited comparable CE performance and superior RTS and PE performance.

Modeling methodology for MLS range navigation system errors using flight test data

NASA Technical Reports Server (NTRS)

Karmali, M. S.; Phatak, A. V.

1982-01-01

Flight test data was used to develop a methodology for modeling MLS range navigation system errors. The data used corresponded to the constant velocity and glideslope approach segment of a helicopter landing trajectory. The MLS range measurement was assumed to consist of low frequency and random high frequency components. The random high frequency component was extracted from the MLS range measurements. This was done by appropriate filtering of the range residual generated from a linearization of the range profile for the final approach segment. This range navigation system error was then modeled as an autoregressive moving average (ARMA) process. Maximum likelihood techniques were used to identify the parameters of the ARMA process.

Conventional and advanced time series estimation: application to the Australian and New Zealand Intensive Care Society (ANZICS) adult patient database, 1993-2006.

PubMed

Moran, John L; Solomon, Patricia J

2011-02-01

Time series analysis has seen limited application in the biomedical Literature. The utility of conventional and advanced time series estimators was explored for intensive care unit (ICU) outcome series. Monthly mean time series, 1993-2006, for hospital mortality, severity-of-illness score (APACHE III), ventilation fraction and patient type (medical and surgical), were generated from the Australia and New Zealand Intensive Care Society adult patient database. Analyses encompassed geographical seasonal mortality patterns, series structural time changes, mortality series volatility using autoregressive moving average and Generalized Autoregressive Conditional Heteroscedasticity models in which predicted variances are updated adaptively, and bivariate and multivariate (vector error correction models) cointegrating relationships between series. The mortality series exhibited marked seasonality, declining mortality trend and substantial autocorrelation beyond 24 lags. Mortality increased in winter months (July-August); the medical series featured annual cycling, whereas the surgical demonstrated long and short (3-4 months) cycling. Series structural breaks were apparent in January 1995 and December 2002. The covariance stationary first-differenced mortality series was consistent with a seasonal autoregressive moving average process; the observed conditional-variance volatility (1993-1995) and residual Autoregressive Conditional Heteroscedasticity effects entailed a Generalized Autoregressive Conditional Heteroscedasticity model, preferred by information criterion and mean model forecast performance. Bivariate cointegration, indicating long-term equilibrium relationships, was established between mortality and severity-of-illness scores at the database level and for categories of ICUs. Multivariate cointegration was demonstrated for {log APACHE III score, log ICU length of stay, ICU mortality and ventilation fraction}. A system approach to understanding series time-dependence may be established using conventional and advanced econometric time series estimators. © 2010 Blackwell Publishing Ltd.

The relationship between carbon dioxide and agriculture in Ghana: a comparison of VECM and ARDL model.

PubMed

Asumadu-Sarkodie, Samuel; Owusu, Phebe Asantewaa

2016-06-01

In this paper, the relationship between carbon dioxide and agriculture in Ghana was investigated by comparing a Vector Error Correction Model (VECM) and Autoregressive Distributed Lag (ARDL) Model. Ten study variables spanning from 1961 to 2012 were employed from the Food Agricultural Organization. Results from the study show that carbon dioxide emissions affect the percentage annual change of agricultural area, coarse grain production, cocoa bean production, fruit production, vegetable production, and the total livestock per hectare of the agricultural area. The vector error correction model and the autoregressive distributed lag model show evidence of a causal relationship between carbon dioxide emissions and agriculture; however, the relationship decreases periodically which may die over-time. All the endogenous variables except total primary vegetable production lead to carbon dioxide emissions, which may be due to poor agricultural practices to meet the growing food demand in Ghana. The autoregressive distributed lag bounds test shows evidence of a long-run equilibrium relationship between the percentage annual change of agricultural area, cocoa bean production, total livestock per hectare of agricultural area, total pulses production, total primary vegetable production, and carbon dioxide emissions. It is important to end hunger and ensure people have access to safe and nutritious food, especially the poor, orphans, pregnant women, and children under-5 years in order to reduce maternal and infant mortalities. Nevertheless, it is also important that the Government of Ghana institutes agricultural policies that focus on promoting a sustainable agriculture using environmental friendly agricultural practices. The study recommends an integration of climate change measures into Ghana's national strategies, policies and planning in order to strengthen the country's effort to achieving a sustainable environment.

Autoregressive modelling of species richness in the Brazilian Cerrado.

PubMed

Vieira, C M; Blamires, D; Diniz-Filho, J A F; Bini, L M; Rangel, T F L V B

2008-05-01

Spatial autocorrelation is the lack of independence between pairs of observations at given distances within a geographical space, a phenomenon commonly found in ecological data. Taking into account spatial autocorrelation when evaluating problems in geographical ecology, including gradients in species richness, is important to describe both the spatial structure in data and to correct the bias in Type I errors of standard statistical analyses. However, to effectively solve these problems it is necessary to establish the best way to incorporate the spatial structure to be used in the models. In this paper, we applied autoregressive models based on different types of connections and distances between 181 cells covering the Cerrado region of Central Brazil to study the spatial variation in mammal and bird species richness across the biome. Spatial structure was stronger for birds than for mammals, with R(2) values ranging from 0.77 to 0.94 for mammals and from 0.77 to 0.97 for birds, for models based on different definitions of spatial structures. According to the Akaike Information Criterion (AIC), the best autoregressive model was obtained by using the rook connection. In general, these results furnish guidelines for future modelling of species richness patterns in relation to environmental predictors and other variables expressing human occupation in the biome.

Road traffic accidents prediction modelling: An analysis of Anambra State, Nigeria.

PubMed

Ihueze, Chukwutoo C; Onwurah, Uchendu O

2018-03-01

One of the major problems in the world today is the rate of road traffic crashes and deaths on our roads. Majority of these deaths occur in low-and-middle income countries including Nigeria. This study analyzed road traffic crashes in Anambra State, Nigeria with the intention of developing accurate predictive models for forecasting crash frequency in the State using autoregressive integrated moving average (ARIMA) and autoregressive integrated moving average with explanatory variables (ARIMAX) modelling techniques. The result showed that ARIMAX model outperformed the ARIMA (1,1,1) model generated when their performances were compared using the lower Bayesian information criterion, mean absolute percentage error, root mean square error; and higher coefficient of determination (R-Squared) as accuracy measures. The findings of this study reveal that incorporating human, vehicle and environmental related factors in time series analysis of crash dataset produces a more robust predictive model than solely using aggregated crash count. This study contributes to the body of knowledge on road traffic safety and provides an approach to forecasting using many human, vehicle and environmental factors. The recommendations made in this study if applied will help in reducing the number of road traffic crashes in Nigeria. Copyright © 2017 Elsevier Ltd. All rights reserved.

Ultra-Short-Term Wind Power Prediction Using a Hybrid Model

NASA Astrophysics Data System (ADS)

Mohammed, E.; Wang, S.; Yu, J.

2017-05-01

This paper aims to develop and apply a hybrid model of two data analytical methods, multiple linear regressions and least square (MLR&LS), for ultra-short-term wind power prediction (WPP), for example taking, Northeast China electricity demand. The data was obtained from the historical records of wind power from an offshore region, and from a wind farm of the wind power plant in the areas. The WPP achieved in two stages: first, the ratios of wind power were forecasted using the proposed hybrid method, and then the transformation of these ratios of wind power to obtain forecasted values. The hybrid model combines the persistence methods, MLR and LS. The proposed method included two prediction types, multi-point prediction and single-point prediction. WPP is tested by applying different models such as autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA) and artificial neural network (ANN). By comparing results of the above models, the validity of the proposed hybrid model is confirmed in terms of error and correlation coefficient. Comparison of results confirmed that the proposed method works effectively. Additional, forecasting errors were also computed and compared, to improve understanding of how to depict highly variable WPP and the correlations between actual and predicted wind power.

Long-Term Prediction of Emergency Department Revenue and Visitor Volume Using Autoregressive Integrated Moving Average Model

PubMed Central

Chen, Chieh-Fan; Ho, Wen-Hsien; Chou, Huei-Yin; Yang, Shu-Mei; Chen, I-Te; Shi, Hon-Yi

2011-01-01

This study analyzed meteorological, clinical and economic factors in terms of their effects on monthly ED revenue and visitor volume. Monthly data from January 1, 2005 to September 30, 2009 were analyzed. Spearman correlation and cross-correlation analyses were performed to identify the correlation between each independent variable, ED revenue, and visitor volume. Autoregressive integrated moving average (ARIMA) model was used to quantify the relationship between each independent variable, ED revenue, and visitor volume. The accuracies were evaluated by comparing model forecasts to actual values with mean absolute percentage of error. Sensitivity of prediction errors to model training time was also evaluated. The ARIMA models indicated that mean maximum temperature, relative humidity, rainfall, non-trauma, and trauma visits may correlate positively with ED revenue, but mean minimum temperature may correlate negatively with ED revenue. Moreover, mean minimum temperature and stock market index fluctuation may correlate positively with trauma visitor volume. Mean maximum temperature, relative humidity and stock market index fluctuation may correlate positively with non-trauma visitor volume. Mean maximum temperature and relative humidity may correlate positively with pediatric visitor volume, but mean minimum temperature may correlate negatively with pediatric visitor volume. The model also performed well in forecasting revenue and visitor volume. PMID:22203886

Long-term prediction of emergency department revenue and visitor volume using autoregressive integrated moving average model.

PubMed

Chen, Chieh-Fan; Ho, Wen-Hsien; Chou, Huei-Yin; Yang, Shu-Mei; Chen, I-Te; Shi, Hon-Yi

2011-01-01

This study analyzed meteorological, clinical and economic factors in terms of their effects on monthly ED revenue and visitor volume. Monthly data from January 1, 2005 to September 30, 2009 were analyzed. Spearman correlation and cross-correlation analyses were performed to identify the correlation between each independent variable, ED revenue, and visitor volume. Autoregressive integrated moving average (ARIMA) model was used to quantify the relationship between each independent variable, ED revenue, and visitor volume. The accuracies were evaluated by comparing model forecasts to actual values with mean absolute percentage of error. Sensitivity of prediction errors to model training time was also evaluated. The ARIMA models indicated that mean maximum temperature, relative humidity, rainfall, non-trauma, and trauma visits may correlate positively with ED revenue, but mean minimum temperature may correlate negatively with ED revenue. Moreover, mean minimum temperature and stock market index fluctuation may correlate positively with trauma visitor volume. Mean maximum temperature, relative humidity and stock market index fluctuation may correlate positively with non-trauma visitor volume. Mean maximum temperature and relative humidity may correlate positively with pediatric visitor volume, but mean minimum temperature may correlate negatively with pediatric visitor volume. The model also performed well in forecasting revenue and visitor volume.

Time series model for forecasting the number of new admission inpatients.

PubMed

Zhou, Lingling; Zhao, Ping; Wu, Dongdong; Cheng, Cheng; Huang, Hao

2018-06-15

Hospital crowding is a rising problem, effective predicting and detecting managment can helpful to reduce crowding. Our team has successfully proposed a hybrid model combining both the autoregressive integrated moving average (ARIMA) and the nonlinear autoregressive neural network (NARNN) models in the schistosomiasis and hand, foot, and mouth disease forecasting study. In this paper, our aim is to explore the application of the hybrid ARIMA-NARNN model to track the trends of the new admission inpatients, which provides a methodological basis for reducing crowding. We used the single seasonal ARIMA (SARIMA), NARNN and the hybrid SARIMA-NARNN model to fit and forecast the monthly and daily number of new admission inpatients. The root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were used to compare the forecasting performance among the three models. The modeling time range of monthly data included was from January 2010 to June 2016, July to October 2016 as the corresponding testing data set. The daily modeling data set was from January 4 to September 4, 2016, while the testing time range included was from September 5 to October 2, 2016. For the monthly data, the modeling RMSE and the testing RMSE, MAE and MAPE of SARIMA-NARNN model were less than those obtained from the single SARIMA or NARNN model, but the MAE and MAPE of modeling performance of SARIMA-NARNN model did not improve. For the daily data, all RMSE, MAE and MAPE of NARNN model were the lowest both in modeling stage and testing stage. Hybrid model does not necessarily outperform its constituents' performances. It is worth attempting to explore the reliable model to forecast the number of new admission inpatients from different data.

Comparison of estimators of standard deviation for hydrologic time series

USGS Publications Warehouse

Tasker, Gary D.; Gilroy, Edward J.

1982-01-01

Unbiasing factors as a function of serial correlation, ρ, and sample size, n for the sample standard deviation of a lag one autoregressive model were generated by random number simulation. Monte Carlo experiments were used to compare the performance of several alternative methods for estimating the standard deviation σ of a lag one autoregressive model in terms of bias, root mean square error, probability of underestimation, and expected opportunity design loss. Three methods provided estimates of σ which were much less biased but had greater mean square errors than the usual estimate of σ: s = (1/(n - 1) ∑ (xi −x¯)2)½. The three methods may be briefly characterized as (1) a method using a maximum likelihood estimate of the unbiasing factor, (2) a method using an empirical Bayes estimate of the unbiasing factor, and (3) a robust nonparametric estimate of σ suggested by Quenouille. Because s tends to underestimate σ, its use as an estimate of a model parameter results in a tendency to underdesign. If underdesign losses are considered more serious than overdesign losses, then the choice of one of the less biased methods may be wise.

Analysing the accuracy of machine learning techniques to develop an integrated influent time series model: case study of a sewage treatment plant, Malaysia.

PubMed

Ansari, Mozafar; Othman, Faridah; Abunama, Taher; El-Shafie, Ahmed

2018-04-01

The function of a sewage treatment plant is to treat the sewage to acceptable standards before being discharged into the receiving waters. To design and operate such plants, it is necessary to measure and predict the influent flow rate. In this research, the influent flow rate of a sewage treatment plant (STP) was modelled and predicted by autoregressive integrated moving average (ARIMA), nonlinear autoregressive network (NAR) and support vector machine (SVM) regression time series algorithms. To evaluate the models' accuracy, the root mean square error (RMSE) and coefficient of determination (R 2 ) were calculated as initial assessment measures, while relative error (RE), peak flow criterion (PFC) and low flow criterion (LFC) were calculated as final evaluation measures to demonstrate the detailed accuracy of the selected models. An integrated model was developed based on the individual models' prediction ability for low, average and peak flow. An initial assessment of the results showed that the ARIMA model was the least accurate and the NAR model was the most accurate. The RE results also prove that the SVM model's frequency of errors above 10% or below - 10% was greater than the NAR model's. The influent was also forecasted up to 44 weeks ahead by both models. The graphical results indicate that the NAR model made better predictions than the SVM model. The final evaluation of NAR and SVM demonstrated that SVM made better predictions at peak flow and NAR fit well for low and average inflow ranges. The integrated model developed includes the NAR model for low and average influent and the SVM model for peak inflow.

Projecting county pulpwood production with historical production and macro-economic variables

Treesearch

Consuelo Brandeis; Dayton M. Lambert

2014-01-01

We explored forecasting of county roundwood pulpwood produc-tion with county-vector autoregressive (CVAR) and spatial panelvector autoregressive (SPVAR) methods. The analysis used timberproducts output data for the state of Florida, together with a set ofmacro-economic variables. Overall, we found the SPVAR specifica-tion produced forecasts with lower error rates...

Multilevel Models for Intensive Longitudinal Data with Heterogeneous Autoregressive Errors: The Effect of Misspecification and Correction with Cholesky Transformation

PubMed Central

Jahng, Seungmin; Wood, Phillip K.

2017-01-01

Intensive longitudinal studies, such as ecological momentary assessment studies using electronic diaries, are gaining popularity across many areas of psychology. Multilevel models (MLMs) are most widely used analytical tools for intensive longitudinal data (ILD). Although ILD often have individually distinct patterns of serial correlation of measures over time, inferences of the fixed effects, and random components in MLMs are made under the assumption that all variance and autocovariance components are homogenous across individuals. In the present study, we introduced a multilevel model with Cholesky transformation to model ILD with individually heterogeneous covariance structure. In addition, the performance of the transformation method and the effects of misspecification of heterogeneous covariance structure were investigated through a Monte Carlo simulation. We found that, if individually heterogeneous covariances are incorrectly assumed as homogenous independent or homogenous autoregressive, MLMs produce highly biased estimates of the variance of random intercepts and the standard errors of the fixed intercept and the fixed effect of a level 2 covariate when the average autocorrelation is high. For intensive longitudinal data with individual specific residual covariance, the suggested transformation method showed lower bias in those estimates than the misspecified models when the number of repeated observations within individuals is 50 or more. PMID:28286490

Joint Seasonal ARMA Approach for Modeling of Load Forecast Errors in Planning Studies

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hafen, Ryan P.; Samaan, Nader A.; Makarov, Yuri V.

2014-04-14

To make informed and robust decisions in the probabilistic power system operation and planning process, it is critical to conduct multiple simulations of the generated combinations of wind and load parameters and their forecast errors to handle the variability and uncertainty of these time series. In order for the simulation results to be trustworthy, the simulated series must preserve the salient statistical characteristics of the real series. In this paper, we analyze day-ahead load forecast error data from multiple balancing authority locations and characterize statistical properties such as mean, standard deviation, autocorrelation, correlation between series, time-of-day bias, and time-of-day autocorrelation.more » We then construct and validate a seasonal autoregressive moving average (ARMA) model to model these characteristics, and use the model to jointly simulate day-ahead load forecast error series for all BAs.« less

Medium term municipal solid waste generation prediction by autoregressive integrated moving average

NASA Astrophysics Data System (ADS)

Younes, Mohammad K.; Nopiah, Z. M.; Basri, Noor Ezlin A.; Basri, Hassan

2014-09-01

Generally, solid waste handling and management are performed by municipality or local authority. In most of developing countries, local authorities suffer from serious solid waste management (SWM) problems and insufficient data and strategic planning. Thus it is important to develop robust solid waste generation forecasting model. It helps to proper manage the generated solid waste and to develop future plan based on relatively accurate figures. In Malaysia, solid waste generation rate increases rapidly due to the population growth and new consumption trends that characterize the modern life style. This paper aims to develop monthly solid waste forecasting model using Autoregressive Integrated Moving Average (ARIMA), such model is applicable even though there is lack of data and will help the municipality properly establish the annual service plan. The results show that ARIMA (6,1,0) model predicts monthly municipal solid waste generation with root mean square error equals to 0.0952 and the model forecast residuals are within accepted 95% confident interval.

Using state-space models to predict the abundance of juvenile and adult sea lice on Atlantic salmon.

PubMed

Elghafghuf, Adel; Vanderstichel, Raphael; St-Hilaire, Sophie; Stryhn, Henrik

2018-04-11

Sea lice are marine parasites affecting salmon farms, and are considered one of the most costly pests of the salmon aquaculture industry. Infestations of sea lice on farms significantly increase opportunities for the parasite to spread in the surrounding ecosystem, making control of this pest a challenging issue for salmon producers. The complexity of controlling sea lice on salmon farms requires frequent monitoring of the abundance of different sea lice stages over time. Industry-based data sets of counts of lice are amenable to multivariate time-series data analyses. In this study, two sets of multivariate autoregressive state-space models were applied to Chilean sea lice data from six Atlantic salmon production cycles on five isolated farms (at least 20 km seaway distance away from other known active farms), to evaluate the utility of these models for predicting sea lice abundance over time on farms. The models were constructed with different parameter configurations, and the analysis demonstrated large heterogeneity between production cycles for the autoregressive parameter, the effects of chemotherapeutant bath treatments, and the process-error variance. A model allowing for different parameters across production cycles had the best fit and the smallest overall prediction errors. However, pooling information across cycles for the drift and observation error parameters did not substantially affect model performance, thus reducing the number of necessary parameters in the model. Bath treatments had strong but variable effects for reducing sea lice burdens, and these effects were stronger for adult lice than juvenile lice. Our multivariate state-space models were able to handle different sea lice stages and provide predictions for sea lice abundance with reasonable accuracy up to five weeks out. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Hypoglycemia early alarm systems based on recursive autoregressive partial least squares models.

PubMed

Bayrak, Elif Seyma; Turksoy, Kamuran; Cinar, Ali; Quinn, Lauretta; Littlejohn, Elizabeth; Rollins, Derrick

2013-01-01

Hypoglycemia caused by intensive insulin therapy is a major challenge for artificial pancreas systems. Early detection and prevention of potential hypoglycemia are essential for the acceptance of fully automated artificial pancreas systems. Many of the proposed alarm systems are based on interpretation of recent values or trends in glucose values. In the present study, subject-specific linear models are introduced to capture glucose variations and predict future blood glucose concentrations. These models can be used in early alarm systems of potential hypoglycemia. A recursive autoregressive partial least squares (RARPLS) algorithm is used to model the continuous glucose monitoring sensor data and predict future glucose concentrations for use in hypoglycemia alarm systems. The partial least squares models constructed are updated recursively at each sampling step with a moving window. An early hypoglycemia alarm algorithm using these models is proposed and evaluated. Glucose prediction models based on real-time filtered data has a root mean squared error of 7.79 and a sum of squares of glucose prediction error of 7.35% for six-step-ahead (30 min) glucose predictions. The early alarm systems based on RARPLS shows good performance. A sensitivity of 86% and a false alarm rate of 0.42 false positive/day are obtained for the early alarm system based on six-step-ahead predicted glucose values with an average early detection time of 25.25 min. The RARPLS models developed provide satisfactory glucose prediction with relatively smaller error than other proposed algorithms and are good candidates to forecast and warn about potential hypoglycemia unless preventive action is taken far in advance. © 2012 Diabetes Technology Society.

Hypoglycemia Early Alarm Systems Based on Recursive Autoregressive Partial Least Squares Models

PubMed Central

Bayrak, Elif Seyma; Turksoy, Kamuran; Cinar, Ali; Quinn, Lauretta; Littlejohn, Elizabeth; Rollins, Derrick

2013-01-01

Background Hypoglycemia caused by intensive insulin therapy is a major challenge for artificial pancreas systems. Early detection and prevention of potential hypoglycemia are essential for the acceptance of fully automated artificial pancreas systems. Many of the proposed alarm systems are based on interpretation of recent values or trends in glucose values. In the present study, subject-specific linear models are introduced to capture glucose variations and predict future blood glucose concentrations. These models can be used in early alarm systems of potential hypoglycemia. Methods A recursive autoregressive partial least squares (RARPLS) algorithm is used to model the continuous glucose monitoring sensor data and predict future glucose concentrations for use in hypoglycemia alarm systems. The partial least squares models constructed are updated recursively at each sampling step with a moving window. An early hypoglycemia alarm algorithm using these models is proposed and evaluated. Results Glucose prediction models based on real-time filtered data has a root mean squared error of 7.79 and a sum of squares of glucose prediction error of 7.35% for six-step-ahead (30 min) glucose predictions. The early alarm systems based on RARPLS shows good performance. A sensitivity of 86% and a false alarm rate of 0.42 false positive/day are obtained for the early alarm system based on six-step-ahead predicted glucose values with an average early detection time of 25.25 min. Conclusions The RARPLS models developed provide satisfactory glucose prediction with relatively smaller error than other proposed algorithms and are good candidates to forecast and warn about potential hypoglycemia unless preventive action is taken far in advance. PMID:23439179

A Generalized Least Squares Regression Approach for Computing Effect Sizes in Single-Case Research: Application Examples

ERIC Educational Resources Information Center

Maggin, Daniel M.; Swaminathan, Hariharan; Rogers, Helen J.; O'Keeffe, Breda V.; Sugai, George; Horner, Robert H.

2011-01-01

A new method for deriving effect sizes from single-case designs is proposed. The strategy is applicable to small-sample time-series data with autoregressive errors. The method uses Generalized Least Squares (GLS) to model the autocorrelation of the data and estimate regression parameters to produce an effect size that represents the magnitude of…

Multifractal detrended cross-correlations between crude oil market and Chinese ten sector stock markets

NASA Astrophysics Data System (ADS)

Yang, Liansheng; Zhu, Yingming; Wang, Yudong; Wang, Yiqi

2016-11-01

Based on the daily price data of spot prices of West Texas Intermediate (WTI) crude oil and ten CSI300 sector indices in China, we apply multifractal detrended cross-correlation analysis (MF-DCCA) method to investigate the cross-correlations between crude oil and Chinese sector stock markets. We find that the strength of multifractality between WTI crude oil and energy sector stock market is the highest, followed by the strength of multifractality between WTI crude oil and financial sector market, which reflects a close connection between energy and financial market. Then we do vector autoregression (VAR) analysis to capture the interdependencies among the multiple time series. By comparing the strength of multifractality for original data and residual errors of VAR model, we get a conclusion that vector auto-regression (VAR) model could not be used to describe the dynamics of the cross-correlations between WTI crude oil and the ten sector stock markets.

[Establishing and applying of autoregressive integrated moving average model to predict the incidence rate of dysentery in Shanghai].

PubMed

Li, Jian; Wu, Huan-Yu; Li, Yan-Ting; Jin, Hui-Ming; Gu, Bao-Ke; Yuan, Zheng-An

2010-01-01

To explore the feasibility of establishing and applying of autoregressive integrated moving average (ARIMA) model to predict the incidence rate of dysentery in Shanghai, so as to provide the theoretical basis for prevention and control of dysentery. ARIMA model was established based on the monthly incidence rate of dysentery of Shanghai from 1990 to 2007. The parameters of model were estimated through unconditional least squares method, the structure was determined according to criteria of residual un-correlation and conclusion, and the model goodness-of-fit was determined through Akaike information criterion (AIC) and Schwarz Bayesian criterion (SBC). The constructed optimal model was applied to predict the incidence rate of dysentery of Shanghai in 2008 and evaluate the validity of model through comparing the difference of predicted incidence rate and actual one. The incidence rate of dysentery in 2010 was predicted by ARIMA model based on the incidence rate from January 1990 to June 2009. The model ARIMA (1, 1, 1) (0, 1, 2)(12) had a good fitness to the incidence rate with both autoregressive coefficient (AR1 = 0.443) during the past time series, moving average coefficient (MA1 = 0.806) and seasonal moving average coefficient (SMA1 = 0.543, SMA2 = 0.321) being statistically significant (P < 0.01). AIC and SBC were 2.878 and 16.131 respectively and predicting error was white noise. The mathematic function was (1-0.443B) (1-B) (1-B(12))Z(t) = (1-0.806B) (1-0.543B(12)) (1-0.321B(2) x 12) micro(t). The predicted incidence rate in 2008 was consistent with the actual one, with the relative error of 6.78%. The predicted incidence rate of dysentery in 2010 based on the incidence rate from January 1990 to June 2009 would be 9.390 per 100 thousand. ARIMA model can be used to fit the changes of incidence rate of dysentery and to forecast the future incidence rate in Shanghai. It is a predicted model of high precision for short-time forecast.

Spectral Analysis of Ultrasound Radiofrequency Backscatter for the Detection of Intercostal Blood Vessels.

PubMed

Klingensmith, Jon D; Haggard, Asher; Fedewa, Russell J; Qiang, Beidi; Cummings, Kenneth; DeGrande, Sean; Vince, D Geoffrey; Elsharkawy, Hesham

2018-04-19

Spectral analysis of ultrasound radiofrequency backscatter has the potential to identify intercostal blood vessels during ultrasound-guided placement of paravertebral nerve blocks and intercostal nerve blocks. Autoregressive models were used for spectral estimation, and bandwidth, autoregressive order and region-of-interest size were evaluated. Eight spectral parameters were calculated and used to create random forests. An autoregressive order of 10, bandwidth of 6 dB and region-of-interest size of 1.0 mm resulted in the minimum out-of-bag error. An additional random forest, using these chosen values, was created from 70% of the data and evaluated independently from the remaining 30% of data. The random forest achieved a predictive accuracy of 92% and Youden's index of 0.85. These results suggest that spectral analysis of ultrasound radiofrequency backscatter has the potential to identify intercostal blood vessels. (jokling@siue.edu) © 2018 World Federation for Ultrasound in Medicine and Biology. Copyright © 2018 World Federation for Ultrasound in Medicine and Biology. Published by Elsevier Inc. All rights reserved.

Small Sample Properties of Bayesian Multivariate Autoregressive Time Series Models

ERIC Educational Resources Information Center

Price, Larry R.

2012-01-01

The aim of this study was to compare the small sample (N = 1, 3, 5, 10, 15) performance of a Bayesian multivariate vector autoregressive (BVAR-SEM) time series model relative to frequentist power and parameter estimation bias. A multivariate autoregressive model was developed based on correlated autoregressive time series vectors of varying…

Comparative analysis of neural network and regression based condition monitoring approaches for wind turbine fault detection

NASA Astrophysics Data System (ADS)

Schlechtingen, Meik; Ferreira Santos, Ilmar

2011-07-01

This paper presents the research results of a comparison of three different model based approaches for wind turbine fault detection in online SCADA data, by applying developed models to five real measured faults and anomalies. The regression based model as the simplest approach to build a normal behavior model is compared to two artificial neural network based approaches, which are a full signal reconstruction and an autoregressive normal behavior model. Based on a real time series containing two generator bearing damages the capabilities of identifying the incipient fault prior to the actual failure are investigated. The period after the first bearing damage is used to develop the three normal behavior models. The developed or trained models are used to investigate how the second damage manifests in the prediction error. Furthermore the full signal reconstruction and the autoregressive approach are applied to further real time series containing gearbox bearing damages and stator temperature anomalies. The comparison revealed all three models being capable of detecting incipient faults. However, they differ in the effort required for model development and the remaining operational time after first indication of damage. The general nonlinear neural network approaches outperform the regression model. The remaining seasonality in the regression model prediction error makes it difficult to detect abnormality and leads to increased alarm levels and thus a shorter remaining operational period. For the bearing damages and the stator anomalies under investigation the full signal reconstruction neural network gave the best fault visibility and thus led to the highest confidence level.

Hybrid empirical mode decomposition- ARIMA for forecasting exchange rates

NASA Astrophysics Data System (ADS)

Abadan, Siti Sarah; Shabri, Ani; Ismail, Shuhaida

2015-02-01

This paper studied the forecasting of monthly Malaysian Ringgit (MYR)/ United State Dollar (USD) exchange rates using the hybrid of two methods which are the empirical model decomposition (EMD) and the autoregressive integrated moving average (ARIMA). MYR is pegged to USD during the Asian financial crisis causing the exchange rates are fixed to 3.800 from 2nd of September 1998 until 21st of July 2005. Thus, the chosen data in this paper is the post-July 2005 data, starting from August 2005 to July 2010. The comparative study using root mean square error (RMSE) and mean absolute error (MAE) showed that the EMD-ARIMA outperformed the single-ARIMA and the random walk benchmark model.

Black-box modeling to estimate tissue temperature during radiofrequency catheter cardiac ablation: Feasibility study on an agar phantom model.

PubMed

Blasco-Gimenez, Ramón; Lequerica, Juan L; Herrero, Maria; Hornero, Fernando; Berjano, Enrique J

2010-04-01

The aim of this work was to study linear deterministic models to predict tissue temperature during radiofrequency cardiac ablation (RFCA) by measuring magnitudes such as electrode temperature, power and impedance between active and dispersive electrodes. The concept involves autoregressive models with exogenous input (ARX), which is a particular case of the autoregressive moving average model with exogenous input (ARMAX). The values of the mode parameters were determined from a least-squares fit of experimental data. The data were obtained from radiofrequency ablations conducted on agar models with different contact pressure conditions between electrode and agar (0 and 20 g) and different flow rates around the electrode (1, 1.5 and 2 L min(-1)). Half of all the ablations were chosen randomly to be used for identification (i.e. determination of model parameters) and the other half were used for model validation. The results suggest that (1) a linear model can be developed to predict tissue temperature at a depth of 4.5 mm during RF cardiac ablation by using the variables applied power, impedance and electrode temperature; (2) the best model provides a reasonably accurate estimate of tissue temperature with a 60% probability of achieving average errors better than 5 degrees C; (3) substantial errors (larger than 15 degrees C) were found only in 6.6% of cases and were associated with abnormal experiments (e.g. those involving the displacement of the ablation electrode) and (4) the impact of measuring impedance on the overall estimate is negligible (around 1 degrees C).

Nonlinear time series modeling and forecasting the seismic data of the Hindu Kush region

NASA Astrophysics Data System (ADS)

Khan, Muhammad Yousaf; Mittnik, Stefan

2018-01-01

In this study, we extended the application of linear and nonlinear time models in the field of earthquake seismology and examined the out-of-sample forecast accuracy of linear Autoregressive (AR), Autoregressive Conditional Duration (ACD), Self-Exciting Threshold Autoregressive (SETAR), Threshold Autoregressive (TAR), Logistic Smooth Transition Autoregressive (LSTAR), Additive Autoregressive (AAR), and Artificial Neural Network (ANN) models for seismic data of the Hindu Kush region. We also extended the previous studies by using Vector Autoregressive (VAR) and Threshold Vector Autoregressive (TVAR) models and compared their forecasting accuracy with linear AR model. Unlike previous studies that typically consider the threshold model specifications by using internal threshold variable, we specified these models with external transition variables and compared their out-of-sample forecasting performance with the linear benchmark AR model. The modeling results show that time series models used in the present study are capable of capturing the dynamic structure present in the seismic data. The point forecast results indicate that the AR model generally outperforms the nonlinear models. However, in some cases, threshold models with external threshold variables specification produce more accurate forecasts, indicating that specification of threshold time series models is of crucial importance. For raw seismic data, the ACD model does not show an improved out-of-sample forecasting performance over the linear AR model. The results indicate that the AR model is the best forecasting device to model and forecast the raw seismic data of the Hindu Kush region.

Error reduction and representation in stages (ERRIS) in hydrological modelling for ensemble streamflow forecasting

NASA Astrophysics Data System (ADS)

Li, Ming; Wang, Q. J.; Bennett, James C.; Robertson, David E.

2016-09-01

This study develops a new error modelling method for ensemble short-term and real-time streamflow forecasting, called error reduction and representation in stages (ERRIS). The novelty of ERRIS is that it does not rely on a single complex error model but runs a sequence of simple error models through four stages. At each stage, an error model attempts to incrementally improve over the previous stage. Stage 1 establishes parameters of a hydrological model and parameters of a transformation function for data normalization, Stage 2 applies a bias correction, Stage 3 applies autoregressive (AR) updating, and Stage 4 applies a Gaussian mixture distribution to represent model residuals. In a case study, we apply ERRIS for one-step-ahead forecasting at a range of catchments. The forecasts at the end of Stage 4 are shown to be much more accurate than at Stage 1 and to be highly reliable in representing forecast uncertainty. Specifically, the forecasts become more accurate by applying the AR updating at Stage 3, and more reliable in uncertainty spread by using a mixture of two Gaussian distributions to represent the residuals at Stage 4. ERRIS can be applied to any existing calibrated hydrological models, including those calibrated to deterministic (e.g. least-squares) objectives.

Using a generalized additive model with autoregressive terms to study the effects of daily temperature on mortality.

PubMed

Yang, Lei; Qin, Guoyou; Zhao, Naiqing; Wang, Chunfang; Song, Guixiang

2012-10-30

Generalized Additive Model (GAM) provides a flexible and effective technique for modelling nonlinear time-series in studies of the health effects of environmental factors. However, GAM assumes that errors are mutually independent, while time series can be correlated in adjacent time points. Here, a GAM with Autoregressive terms (GAMAR) is introduced to fill this gap. Parameters in GAMAR are estimated by maximum partial likelihood using modified Newton's method, and the difference between GAM and GAMAR is demonstrated using two simulation studies and a real data example. GAMM is also compared to GAMAR in simulation study 1. In the simulation studies, the bias of the mean estimates from GAM and GAMAR are similar but GAMAR has better coverage and smaller relative error. While the results from GAMM are similar to GAMAR, the estimation procedure of GAMM is much slower than GAMAR. In the case study, the Pearson residuals from the GAM are correlated, while those from GAMAR are quite close to white noise. In addition, the estimates of the temperature effects are different between GAM and GAMAR. GAMAR incorporates both explanatory variables and AR terms so it can quantify the nonlinear impact of environmental factors on health outcome as well as the serial correlation between the observations. It can be a useful tool in environmental epidemiological studies.

Using a generalized additive model with autoregressive terms to study the effects of daily temperature on mortality

PubMed Central

2012-01-01

Background Generalized Additive Model (GAM) provides a flexible and effective technique for modelling nonlinear time-series in studies of the health effects of environmental factors. However, GAM assumes that errors are mutually independent, while time series can be correlated in adjacent time points. Here, a GAM with Autoregressive terms (GAMAR) is introduced to fill this gap. Methods Parameters in GAMAR are estimated by maximum partial likelihood using modified Newton’s method, and the difference between GAM and GAMAR is demonstrated using two simulation studies and a real data example. GAMM is also compared to GAMAR in simulation study 1. Results In the simulation studies, the bias of the mean estimates from GAM and GAMAR are similar but GAMAR has better coverage and smaller relative error. While the results from GAMM are similar to GAMAR, the estimation procedure of GAMM is much slower than GAMAR. In the case study, the Pearson residuals from the GAM are correlated, while those from GAMAR are quite close to white noise. In addition, the estimates of the temperature effects are different between GAM and GAMAR. Conclusions GAMAR incorporates both explanatory variables and AR terms so it can quantify the nonlinear impact of environmental factors on health outcome as well as the serial correlation between the observations. It can be a useful tool in environmental epidemiological studies. PMID:23110601

Local Linear Regression for Data with AR Errors.

PubMed

Li, Runze; Li, Yan

2009-07-01

In many statistical applications, data are collected over time, and they are likely correlated. In this paper, we investigate how to incorporate the correlation information into the local linear regression. Under the assumption that the error process is an auto-regressive process, a new estimation procedure is proposed for the nonparametric regression by using local linear regression method and the profile least squares techniques. We further propose the SCAD penalized profile least squares method to determine the order of auto-regressive process. Extensive Monte Carlo simulation studies are conducted to examine the finite sample performance of the proposed procedure, and to compare the performance of the proposed procedures with the existing one. From our empirical studies, the newly proposed procedures can dramatically improve the accuracy of naive local linear regression with working-independent error structure. We illustrate the proposed methodology by an analysis of real data set.

A univariate model of river water nitrate time series

NASA Astrophysics Data System (ADS)

Worrall, F.; Burt, T. P.

1999-01-01

Four time series were taken from three catchments in the North and South of England. The sites chosen included two in predominantly agricultural catchments, one at the tidal limit and one downstream of a sewage treatment works. A time series model was constructed for each of these series as a means of decomposing the elements controlling river water nitrate concentrations and to assess whether this approach could provide a simple management tool for protecting water abstractions. Autoregressive (AR) modelling of the detrended and deseasoned time series showed a "memory effect". This memory effect expressed itself as an increase in the winter-summer difference in nitrate levels that was dependent upon the nitrate concentration 12 or 6 months previously. Autoregressive moving average (ARMA) modelling showed that one of the series contained seasonal, non-stationary elements that appeared as an increasing trend in the winter-summer difference. The ARMA model was used to predict nitrate levels and predictions were tested against data held back from the model construction process - predictions gave average percentage errors of less than 10%. Empirical modelling can therefore provide a simple, efficient method for constructing management models for downstream water abstraction.

Forecasting Daily Volume and Acuity of Patients in the Emergency Department.

PubMed

Calegari, Rafael; Fogliatto, Flavio S; Lucini, Filipe R; Neyeloff, Jeruza; Kuchenbecker, Ricardo S; Schaan, Beatriz D

2016-01-01

This study aimed at analyzing the performance of four forecasting models in predicting the demand for medical care in terms of daily visits in an emergency department (ED) that handles high complexity cases, testing the influence of climatic and calendrical factors on demand behavior. We tested different mathematical models to forecast ED daily visits at Hospital de Clínicas de Porto Alegre (HCPA), which is a tertiary care teaching hospital located in Southern Brazil. Model accuracy was evaluated using mean absolute percentage error (MAPE), considering forecasting horizons of 1, 7, 14, 21, and 30 days. The demand time series was stratified according to patient classification using the Manchester Triage System's (MTS) criteria. Models tested were the simple seasonal exponential smoothing (SS), seasonal multiplicative Holt-Winters (SMHW), seasonal autoregressive integrated moving average (SARIMA), and multivariate autoregressive integrated moving average (MSARIMA). Performance of models varied according to patient classification, such that SS was the best choice when all types of patients were jointly considered, and SARIMA was the most accurate for modeling demands of very urgent (VU) and urgent (U) patients. The MSARIMA models taking into account climatic factors did not improve the performance of the SARIMA models, independent of patient classification.

Forecasting Daily Volume and Acuity of Patients in the Emergency Department

PubMed Central

Fogliatto, Flavio S.; Neyeloff, Jeruza; Kuchenbecker, Ricardo S.; Schaan, Beatriz D.

2016-01-01

This study aimed at analyzing the performance of four forecasting models in predicting the demand for medical care in terms of daily visits in an emergency department (ED) that handles high complexity cases, testing the influence of climatic and calendrical factors on demand behavior. We tested different mathematical models to forecast ED daily visits at Hospital de Clínicas de Porto Alegre (HCPA), which is a tertiary care teaching hospital located in Southern Brazil. Model accuracy was evaluated using mean absolute percentage error (MAPE), considering forecasting horizons of 1, 7, 14, 21, and 30 days. The demand time series was stratified according to patient classification using the Manchester Triage System's (MTS) criteria. Models tested were the simple seasonal exponential smoothing (SS), seasonal multiplicative Holt-Winters (SMHW), seasonal autoregressive integrated moving average (SARIMA), and multivariate autoregressive integrated moving average (MSARIMA). Performance of models varied according to patient classification, such that SS was the best choice when all types of patients were jointly considered, and SARIMA was the most accurate for modeling demands of very urgent (VU) and urgent (U) patients. The MSARIMA models taking into account climatic factors did not improve the performance of the SARIMA models, independent of patient classification. PMID:27725842

Forecasting mortality of road traffic injuries in China using seasonal autoregressive integrated moving average model.

PubMed

Zhang, Xujun; Pang, Yuanyuan; Cui, Mengjing; Stallones, Lorann; Xiang, Huiyun

2015-02-01

Road traffic injuries have become a major public health problem in China. This study aimed to develop statistical models for predicting road traffic deaths and to analyze seasonality of deaths in China. A seasonal autoregressive integrated moving average (SARIMA) model was used to fit the data from 2000 to 2011. Akaike Information Criterion, Bayesian Information Criterion, and mean absolute percentage error were used to evaluate the constructed models. Autocorrelation function and partial autocorrelation function of residuals and Ljung-Box test were used to compare the goodness-of-fit between the different models. The SARIMA model was used to forecast monthly road traffic deaths in 2012. The seasonal pattern of road traffic mortality data was statistically significant in China. SARIMA (1, 1, 1) (0, 1, 1)12 model was the best fitting model among various candidate models; the Akaike Information Criterion, Bayesian Information Criterion, and mean absolute percentage error were -483.679, -475.053, and 4.937, respectively. Goodness-of-fit testing showed nonautocorrelations in the residuals of the model (Ljung-Box test, Q = 4.86, P = .993). The fitted deaths using the SARIMA (1, 1, 1) (0, 1, 1)12 model for years 2000 to 2011 closely followed the observed number of road traffic deaths for the same years. The predicted and observed deaths were also very close for 2012. This study suggests that accurate forecasting of road traffic death incidence is possible using SARIMA model. The SARIMA model applied to historical road traffic deaths data could provide important evidence of burden of road traffic injuries in China. Copyright © 2015 Elsevier Inc. All rights reserved.

Prediction of municipal solid waste generation using nonlinear autoregressive network.

PubMed

Younes, Mohammad K; Nopiah, Z M; Basri, N E Ahmad; Basri, H; Abushammala, Mohammed F M; Maulud, K N A

2015-12-01

Most of the developing countries have solid waste management problems. Solid waste strategic planning requires accurate prediction of the quality and quantity of the generated waste. In developing countries, such as Malaysia, the solid waste generation rate is increasing rapidly, due to population growth and new consumption trends that characterize society. This paper proposes an artificial neural network (ANN) approach using feedforward nonlinear autoregressive network with exogenous inputs (NARX) to predict annual solid waste generation in relation to demographic and economic variables like population number, gross domestic product, electricity demand per capita and employment and unemployment numbers. In addition, variable selection procedures are also developed to select a significant explanatory variable. The model evaluation was performed using coefficient of determination (R(2)) and mean square error (MSE). The optimum model that produced the lowest testing MSE (2.46) and the highest R(2) (0.97) had three inputs (gross domestic product, population and employment), eight neurons and one lag in the hidden layer, and used Fletcher-Powell's conjugate gradient as the training algorithm.

Neural network versus classical time series forecasting models

NASA Astrophysics Data System (ADS)

Nor, Maria Elena; Safuan, Hamizah Mohd; Shab, Noorzehan Fazahiyah Md; Asrul, Mohd; Abdullah, Affendi; Mohamad, Nurul Asmaa Izzati; Lee, Muhammad Hisyam

2017-05-01

Artificial neural network (ANN) has advantage in time series forecasting as it has potential to solve complex forecasting problems. This is because ANN is data driven approach which able to be trained to map past values of a time series. In this study the forecast performance between neural network and classical time series forecasting method namely seasonal autoregressive integrated moving average models was being compared by utilizing gold price data. Moreover, the effect of different data preprocessing on the forecast performance of neural network being examined. The forecast accuracy was evaluated using mean absolute deviation, root mean square error and mean absolute percentage error. It was found that ANN produced the most accurate forecast when Box-Cox transformation was used as data preprocessing.

NARMAX model identification of a palm oil biodiesel engine using multi-objective optimization differential evolution

NASA Astrophysics Data System (ADS)

Mansor, Zakwan; Zakaria, Mohd Zakimi; Nor, Azuwir Mohd; Saad, Mohd Sazli; Ahmad, Robiah; Jamaluddin, Hishamuddin

2017-09-01

This paper presents the black-box modelling of palm oil biodiesel engine (POB) using multi-objective optimization differential evolution (MOODE) algorithm. Two objective functions are considered in the algorithm for optimization; minimizing the number of term of a model structure and minimizing the mean square error between actual and predicted outputs. The mathematical model used in this study to represent the POB system is nonlinear auto-regressive moving average with exogenous input (NARMAX) model. Finally, model validity tests are applied in order to validate the possible models that was obtained from MOODE algorithm and lead to select an optimal model.

Applications and Comparisons of Four Time Series Models in Epidemiological Surveillance Data

PubMed Central

Young, Alistair A.; Li, Xiaosong

2014-01-01

Public health surveillance systems provide valuable data for reliable predication of future epidemic events. This paper describes a study that used nine types of infectious disease data collected through a national public health surveillance system in mainland China to evaluate and compare the performances of four time series methods, namely, two decomposition methods (regression and exponential smoothing), autoregressive integrated moving average (ARIMA) and support vector machine (SVM). The data obtained from 2005 to 2011 and in 2012 were used as modeling and forecasting samples, respectively. The performances were evaluated based on three metrics: mean absolute error (MAE), mean absolute percentage error (MAPE), and mean square error (MSE). The accuracy of the statistical models in forecasting future epidemic disease proved their effectiveness in epidemiological surveillance. Although the comparisons found that no single method is completely superior to the others, the present study indeed highlighted that the SVMs outperforms the ARIMA model and decomposition methods in most cases. PMID:24505382

Hybrid Forecasting of Daily River Discharges Considering Autoregressive Heteroscedasticity

NASA Astrophysics Data System (ADS)

Szolgayová, Elena Peksová; Danačová, Michaela; Komorniková, Magda; Szolgay, Ján

2017-06-01

It is widely acknowledged that in the hydrological and meteorological communities, there is a continuing need to improve the quality of quantitative rainfall and river flow forecasts. A hybrid (combined deterministic-stochastic) modelling approach is proposed here that combines the advantages offered by modelling the system dynamics with a deterministic model and a deterministic forecasting error series with a data-driven model in parallel. Since the processes to be modelled are generally nonlinear and the model error series may exhibit nonstationarity and heteroscedasticity, GARCH-type nonlinear time series models are considered here. The fitting, forecasting and simulation performance of such models have to be explored on a case-by-case basis. The goal of this paper is to test and develop an appropriate methodology for model fitting and forecasting applicable for daily river discharge forecast error data from the GARCH family of time series models. We concentrated on verifying whether the use of a GARCH-type model is suitable for modelling and forecasting a hydrological model error time series on the Hron and Morava Rivers in Slovakia. For this purpose we verified the presence of heteroscedasticity in the simulation error series of the KLN multilinear flow routing model; then we fitted the GARCH-type models to the data and compared their fit with that of an ARMA - type model. We produced one-stepahead forecasts from the fitted models and again provided comparisons of the model's performance.

Models for short term malaria prediction in Sri Lanka

PubMed Central

Briët, Olivier JT; Vounatsou, Penelope; Gunawardena, Dissanayake M; Galappaththy, Gawrie NL; Amerasinghe, Priyanie H

2008-01-01

Background Malaria in Sri Lanka is unstable and fluctuates in intensity both spatially and temporally. Although the case counts are dwindling at present, given the past history of resurgence of outbreaks despite effective control measures, the control programmes have to stay prepared. The availability of long time series of monitored/diagnosed malaria cases allows for the study of forecasting models, with an aim to developing a forecasting system which could assist in the efficient allocation of resources for malaria control. Methods Exponentially weighted moving average models, autoregressive integrated moving average (ARIMA) models with seasonal components, and seasonal multiplicative autoregressive integrated moving average (SARIMA) models were compared on monthly time series of district malaria cases for their ability to predict the number of malaria cases one to four months ahead. The addition of covariates such as the number of malaria cases in neighbouring districts or rainfall were assessed for their ability to improve prediction of selected (seasonal) ARIMA models. Results The best model for forecasting and the forecasting error varied strongly among the districts. The addition of rainfall as a covariate improved prediction of selected (seasonal) ARIMA models modestly in some districts but worsened prediction in other districts. Improvement by adding rainfall was more frequent at larger forecasting horizons. Conclusion Heterogeneity of patterns of malaria in Sri Lanka requires regionally specific prediction models. Prediction error was large at a minimum of 22% (for one of the districts) for one month ahead predictions. The modest improvement made in short term prediction by adding rainfall as a covariate to these prediction models may not be sufficient to merit investing in a forecasting system for which rainfall data are routinely processed. PMID:18460204

Development of a Robust Identifier for NPPs Transients Combining ARIMA Model and EBP Algorithm

NASA Astrophysics Data System (ADS)

Moshkbar-Bakhshayesh, Khalil; Ghofrani, Mohammad B.

2014-08-01

This study introduces a novel identification method for recognition of nuclear power plants (NPPs) transients by combining the autoregressive integrated moving-average (ARIMA) model and the neural network with error backpropagation (EBP) learning algorithm. The proposed method consists of three steps. First, an EBP based identifier is adopted to distinguish the plant normal states from the faulty ones. In the second step, ARIMA models use integrated (I) process to convert non-stationary data of the selected variables into stationary ones. Subsequently, ARIMA processes, including autoregressive (AR), moving-average (MA), or autoregressive moving-average (ARMA) are used to forecast time series of the selected plant variables. In the third step, for identification the type of transients, the forecasted time series are fed to the modular identifier which has been developed using the latest advances of EBP learning algorithm. Bushehr nuclear power plant (BNPP) transients are probed to analyze the ability of the proposed identifier. Recognition of transient is based on similarity of its statistical properties to the reference one, rather than the values of input patterns. More robustness against noisy data and improvement balance between memorization and generalization are salient advantages of the proposed identifier. Reduction of false identification, sole dependency of identification on the sign of each output signal, selection of the plant variables for transients training independent of each other, and extendibility for identification of more transients without unfavorable effects are other merits of the proposed identifier.

Hydrological modelling of the Chaohe Basin in China: Statistical model formulation and Bayesian inference

NASA Astrophysics Data System (ADS)

Yang, Jing; Reichert, Peter; Abbaspour, Karim C.; Yang, Hong

2007-07-01

SummaryCalibration of hydrologic models is very difficult because of measurement errors in input and response, errors in model structure, and the large number of non-identifiable parameters of distributed models. The difficulties even increase in arid regions with high seasonal variation of precipitation, where the modelled residuals often exhibit high heteroscedasticity and autocorrelation. On the other hand, support of water management by hydrologic models is important in arid regions, particularly if there is increasing water demand due to urbanization. The use and assessment of model results for this purpose require a careful calibration and uncertainty analysis. Extending earlier work in this field, we developed a procedure to overcome (i) the problem of non-identifiability of distributed parameters by introducing aggregate parameters and using Bayesian inference, (ii) the problem of heteroscedasticity of errors by combining a Box-Cox transformation of results and data with seasonally dependent error variances, (iii) the problems of autocorrelated errors, missing data and outlier omission with a continuous-time autoregressive error model, and (iv) the problem of the seasonal variation of error correlations with seasonally dependent characteristic correlation times. The technique was tested with the calibration of the hydrologic sub-model of the Soil and Water Assessment Tool (SWAT) in the Chaohe Basin in North China. The results demonstrated the good performance of this approach to uncertainty analysis, particularly with respect to the fulfilment of statistical assumptions of the error model. A comparison with an independent error model and with error models that only considered a subset of the suggested techniques clearly showed the superiority of the approach based on all the features (i)-(iv) mentioned above.

Vector autoregressive models: A Gini approach

NASA Astrophysics Data System (ADS)

Mussard, Stéphane; Ndiaye, Oumar Hamady

2018-02-01

In this paper, it is proven that the usual VAR models may be performed in the Gini sense, that is, on a ℓ1 metric space. The Gini regression is robust to outliers. As a consequence, when data are contaminated by extreme values, we show that semi-parametric VAR-Gini regressions may be used to obtain robust estimators. The inference about the estimators is made with the ℓ1 norm. Also, impulse response functions and Gini decompositions for prevision errors are introduced. Finally, Granger's causality tests are properly derived based on U-statistics.

Compression of head-related transfer function using autoregressive-moving-average models and Legendre polynomials.

PubMed

Shekarchi, Sayedali; Hallam, John; Christensen-Dalsgaard, Jakob

2013-11-01

Head-related transfer functions (HRTFs) are generally large datasets, which can be an important constraint for embedded real-time applications. A method is proposed here to reduce redundancy and compress the datasets. In this method, HRTFs are first compressed by conversion into autoregressive-moving-average (ARMA) filters whose coefficients are calculated using Prony's method. Such filters are specified by a few coefficients which can generate the full head-related impulse responses (HRIRs). Next, Legendre polynomials (LPs) are used to compress the ARMA filter coefficients. LPs are derived on the sphere and form an orthonormal basis set for spherical functions. Higher-order LPs capture increasingly fine spatial details. The number of LPs needed to represent an HRTF, therefore, is indicative of its spatial complexity. The results indicate that compression ratios can exceed 98% while maintaining a spectral error of less than 4 dB in the recovered HRTFs.

Comparative study of four time series methods in forecasting typhoid fever incidence in China.

PubMed

Zhang, Xingyu; Liu, Yuanyuan; Yang, Min; Zhang, Tao; Young, Alistair A; Li, Xiaosong

2013-01-01

Accurate incidence forecasting of infectious disease is critical for early prevention and for better government strategic planning. In this paper, we present a comprehensive study of different forecasting methods based on the monthly incidence of typhoid fever. The seasonal autoregressive integrated moving average (SARIMA) model and three different models inspired by neural networks, namely, back propagation neural networks (BPNN), radial basis function neural networks (RBFNN), and Elman recurrent neural networks (ERNN) were compared. The differences as well as the advantages and disadvantages, among the SARIMA model and the neural networks were summarized and discussed. The data obtained for 2005 to 2009 and for 2010 from the Chinese Center for Disease Control and Prevention were used as modeling and forecasting samples, respectively. The performances were evaluated based on three metrics: mean absolute error (MAE), mean absolute percentage error (MAPE), and mean square error (MSE). The results showed that RBFNN obtained the smallest MAE, MAPE and MSE in both the modeling and forecasting processes. The performances of the four models ranked in descending order were: RBFNN, ERNN, BPNN and the SARIMA model.

Comparative Study of Four Time Series Methods in Forecasting Typhoid Fever Incidence in China

PubMed Central

Zhang, Xingyu; Liu, Yuanyuan; Yang, Min; Zhang, Tao; Young, Alistair A.; Li, Xiaosong

2013-01-01

Accurate incidence forecasting of infectious disease is critical for early prevention and for better government strategic planning. In this paper, we present a comprehensive study of different forecasting methods based on the monthly incidence of typhoid fever. The seasonal autoregressive integrated moving average (SARIMA) model and three different models inspired by neural networks, namely, back propagation neural networks (BPNN), radial basis function neural networks (RBFNN), and Elman recurrent neural networks (ERNN) were compared. The differences as well as the advantages and disadvantages, among the SARIMA model and the neural networks were summarized and discussed. The data obtained for 2005 to 2009 and for 2010 from the Chinese Center for Disease Control and Prevention were used as modeling and forecasting samples, respectively. The performances were evaluated based on three metrics: mean absolute error (MAE), mean absolute percentage error (MAPE), and mean square error (MSE). The results showed that RBFNN obtained the smallest MAE, MAPE and MSE in both the modeling and forecasting processes. The performances of the four models ranked in descending order were: RBFNN, ERNN, BPNN and the SARIMA model. PMID:23650546

Advances in nowcasting influenza-like illness rates using search query logs

NASA Astrophysics Data System (ADS)

Lampos, Vasileios; Miller, Andrew C.; Crossan, Steve; Stefansen, Christian

2015-08-01

User-generated content can assist epidemiological surveillance in the early detection and prevalence estimation of infectious diseases, such as influenza. Google Flu Trends embodies the first public platform for transforming search queries to indications about the current state of flu in various places all over the world. However, the original model significantly mispredicted influenza-like illness rates in the US during the 2012-13 flu season. In this work, we build on the previous modeling attempt, proposing substantial improvements. Firstly, we investigate the performance of a widely used linear regularized regression solver, known as the Elastic Net. Then, we expand on this model by incorporating the queries selected by the Elastic Net into a nonlinear regression framework, based on a composite Gaussian Process. Finally, we augment the query-only predictions with an autoregressive model, injecting prior knowledge about the disease. We assess predictive performance using five consecutive flu seasons spanning from 2008 to 2013 and qualitatively explain certain shortcomings of the previous approach. Our results indicate that a nonlinear query modeling approach delivers the lowest cumulative nowcasting error, and also suggest that query information significantly improves autoregressive inferences, obtaining state-of-the-art performance.

Advances in nowcasting influenza-like illness rates using search query logs.

PubMed

Lampos, Vasileios; Miller, Andrew C; Crossan, Steve; Stefansen, Christian

2015-08-03

User-generated content can assist epidemiological surveillance in the early detection and prevalence estimation of infectious diseases, such as influenza. Google Flu Trends embodies the first public platform for transforming search queries to indications about the current state of flu in various places all over the world. However, the original model significantly mispredicted influenza-like illness rates in the US during the 2012-13 flu season. In this work, we build on the previous modeling attempt, proposing substantial improvements. Firstly, we investigate the performance of a widely used linear regularized regression solver, known as the Elastic Net. Then, we expand on this model by incorporating the queries selected by the Elastic Net into a nonlinear regression framework, based on a composite Gaussian Process. Finally, we augment the query-only predictions with an autoregressive model, injecting prior knowledge about the disease. We assess predictive performance using five consecutive flu seasons spanning from 2008 to 2013 and qualitatively explain certain shortcomings of the previous approach. Our results indicate that a nonlinear query modeling approach delivers the lowest cumulative nowcasting error, and also suggest that query information significantly improves autoregressive inferences, obtaining state-of-the-art performance.

Detection of shallow buried objects using an autoregressive model on the ground penetrating radar signal

NASA Astrophysics Data System (ADS)

Nabelek, Daniel P.; Ho, K. C.

2013-06-01

The detection of shallow buried low-metal content objects using ground penetrating radar (GPR) is a challenging task. This is because these targets are right underneath the ground and the ground bounce reflection interferes with their detections. They do not create distinctive hyperbolic signatures as required by most existing GPR detection algorithms due to their special geometric shapes and low metal content. This paper proposes the use of the Autoregressive (AR) modeling method for the detection of these targets. We fit an A-scan of the GPR data to an AR model. It is found that the fitting error will be small when such a target is present and large when it is absent. The ratio of the energy in an Ascan before and after AR model fitting is used as the confidence value for detection. We also apply AR model fitting over scans and utilize the fitting residual energies over several scans to form a feature vector for improving the detections. Using the data collected from a government test site, the proposed method can improve the detection of this kind of targets by 30% compared to the pre-screener, at a false alarm rate of 0.002/m2.

Robust Semi-Active Ride Control under Stochastic Excitation

DTIC Science & Technology

2014-01-01

broad classes of time-series models which are of practical importance; the Auto-Regressive (AR) models, the Integrated (I) models, and the Moving...Average (MA) models [12]. Combinations of these models result in autoregressive moving average (ARMA) and autoregressive integrated moving average...Down Up 4) Down Down These four cases can be written in compact form as: (20) Where is the Heaviside

Bridge Structure Deformation Prediction Based on GNSS Data Using Kalman-ARIMA-GARCH Model

PubMed Central

Li, Xiaoqing; Wang, Yu

2018-01-01

Bridges are an essential part of the ground transportation system. Health monitoring is fundamentally important for the safety and service life of bridges. A large amount of structural information is obtained from various sensors using sensing technology, and the data processing has become a challenging issue. To improve the prediction accuracy of bridge structure deformation based on data mining and to accurately evaluate the time-varying characteristics of bridge structure performance evolution, this paper proposes a new method for bridge structure deformation prediction, which integrates the Kalman filter, autoregressive integrated moving average model (ARIMA), and generalized autoregressive conditional heteroskedasticity (GARCH). Firstly, the raw deformation data is directly pre-processed using the Kalman filter to reduce the noise. After that, the linear recursive ARIMA model is established to analyze and predict the structure deformation. Finally, the nonlinear recursive GARCH model is introduced to further improve the accuracy of the prediction. Simulation results based on measured sensor data from the Global Navigation Satellite System (GNSS) deformation monitoring system demonstrated that: (1) the Kalman filter is capable of denoising the bridge deformation monitoring data; (2) the prediction accuracy of the proposed Kalman-ARIMA-GARCH model is satisfactory, where the mean absolute error increases only from 3.402 mm to 5.847 mm with the increment of the prediction step; and (3) in comparision to the Kalman-ARIMA model, the Kalman-ARIMA-GARCH model results in superior prediction accuracy as it includes partial nonlinear characteristics (heteroscedasticity); the mean absolute error of five-step prediction using the proposed model is improved by 10.12%. This paper provides a new way for structural behavior prediction based on data processing, which can lay a foundation for the early warning of bridge health monitoring system based on sensor data using sensing technology. PMID:29351254

Bridge Structure Deformation Prediction Based on GNSS Data Using Kalman-ARIMA-GARCH Model.

PubMed

Xin, Jingzhou; Zhou, Jianting; Yang, Simon X; Li, Xiaoqing; Wang, Yu

2018-01-19

Bridges are an essential part of the ground transportation system. Health monitoring is fundamentally important for the safety and service life of bridges. A large amount of structural information is obtained from various sensors using sensing technology, and the data processing has become a challenging issue. To improve the prediction accuracy of bridge structure deformation based on data mining and to accurately evaluate the time-varying characteristics of bridge structure performance evolution, this paper proposes a new method for bridge structure deformation prediction, which integrates the Kalman filter, autoregressive integrated moving average model (ARIMA), and generalized autoregressive conditional heteroskedasticity (GARCH). Firstly, the raw deformation data is directly pre-processed using the Kalman filter to reduce the noise. After that, the linear recursive ARIMA model is established to analyze and predict the structure deformation. Finally, the nonlinear recursive GARCH model is introduced to further improve the accuracy of the prediction. Simulation results based on measured sensor data from the Global Navigation Satellite System (GNSS) deformation monitoring system demonstrated that: (1) the Kalman filter is capable of denoising the bridge deformation monitoring data; (2) the prediction accuracy of the proposed Kalman-ARIMA-GARCH model is satisfactory, where the mean absolute error increases only from 3.402 mm to 5.847 mm with the increment of the prediction step; and (3) in comparision to the Kalman-ARIMA model, the Kalman-ARIMA-GARCH model results in superior prediction accuracy as it includes partial nonlinear characteristics (heteroscedasticity); the mean absolute error of five-step prediction using the proposed model is improved by 10.12%. This paper provides a new way for structural behavior prediction based on data processing, which can lay a foundation for the early warning of bridge health monitoring system based on sensor data using sensing technology.

Modelling space of spread Dengue Hemorrhagic Fever (DHF) in Central Java use spatial durbin model

NASA Astrophysics Data System (ADS)

Ispriyanti, Dwi; Prahutama, Alan; Taryono, Arkadina PN

2018-05-01

Dengue Hemorrhagic Fever is one of the major public health problems in Indonesia. From year to year, DHF causes Extraordinary Event in most parts of Indonesia, especially Central Java. Central Java consists of 35 districts or cities where each region is close to each other. Spatial regression is an analysis that suspects the influence of independent variables on the dependent variables with the influences of the region inside. In spatial regression modeling, there are spatial autoregressive model (SAR), spatial error model (SEM) and spatial autoregressive moving average (SARMA). Spatial Durbin model is the development of SAR where the dependent and independent variable have spatial influence. In this research dependent variable used is number of DHF sufferers. The independent variables observed are population density, number of hospitals, residents and health centers, and mean years of schooling. From the multiple regression model test, the variables that significantly affect the spread of DHF disease are the population and mean years of schooling. By using queen contiguity and rook contiguity, the best model produced is the SDM model with queen contiguity because it has the smallest AIC value of 494,12. Factors that generally affect the spread of DHF in Central Java Province are the number of population and the average length of school.

An autoregressive integrated moving average model for short-term prediction of hepatitis C virus seropositivity among male volunteer blood donors in Karachi, Pakistan

PubMed Central

Akhtar, Saeed; Rozi, Shafquat

2009-01-01

AIM: To identify the stochastic autoregressive integrated moving average (ARIMA) model for short term forecasting of hepatitis C virus (HCV) seropositivity among volunteer blood donors in Karachi, Pakistan. METHODS: Ninety-six months (1998-2005) data on HCV seropositive cases (1000-1 × month-1) among male volunteer blood donors tested at four major blood banks in Karachi, Pakistan were subjected to ARIMA modeling. Subsequently, a fitted ARIMA model was used to forecast HCV seropositive donors for 91-96 mo to contrast with observed series of the same months. To assess the forecast accuracy, the mean absolute error rate (%) between the observed and predicted HCV seroprevalence was calculated. Finally, a fitted ARIMA model was used for short-term forecasts beyond the observed series. RESULTS: The goodness-of-fit test of the optimum ARIMA (2,1,7) model showed non-significant autocorrelations in the residuals of the model. The forecasts by ARIMA for 91-96 mo closely followed the pattern of observed series for the same months, with mean monthly absolute forecast errors (%) over 6 mo of 6.5%. The short-term forecasts beyond the observed series adequately captured the pattern in the data and showed increasing tendency of HCV seropositivity with a mean ± SD HCV seroprevalence (1000-1 × month-1) of 24.3 ± 1.4 over the forecast interval. CONCLUSION: To curtail HCV spread, public health authorities need to educate communities and health care providers about HCV transmission routes based on known HCV epidemiology in Pakistan and its neighboring countries. Future research may focus on factors associated with hyperendemic levels of HCV infection. PMID:19340903

Crime Modeling using Spatial Regression Approach

NASA Astrophysics Data System (ADS)

Saleh Ahmar, Ansari; Adiatma; Kasim Aidid, M.

2018-01-01

Act of criminality in Indonesia increased both variety and quantity every year. As murder, rape, assault, vandalism, theft, fraud, fencing, and other cases that make people feel unsafe. Risk of society exposed to crime is the number of reported cases in the police institution. The higher of the number of reporter to the police institution then the number of crime in the region is increasing. In this research, modeling criminality in South Sulawesi, Indonesia with the dependent variable used is the society exposed to the risk of crime. Modelling done by area approach is the using Spatial Autoregressive (SAR) and Spatial Error Model (SEM) methods. The independent variable used is the population density, the number of poor population, GDP per capita, unemployment and the human development index (HDI). Based on the analysis using spatial regression can be shown that there are no dependencies spatial both lag or errors in South Sulawesi.

A hybrid ARIMA and neural network model applied to forecast catch volumes of Selar crumenophthalmus

NASA Astrophysics Data System (ADS)

Aquino, Ronald L.; Alcantara, Nialle Loui Mar T.; Addawe, Rizavel C.

2017-11-01

The Selar crumenophthalmus with the English name big-eyed scad fish, locally known as matang-baka, is one of the fishes commonly caught along the waters of La Union, Philippines. The study deals with the forecasting of catch volumes of big-eyed scad fish for commercial consumption. The data used are quarterly caught volumes of big-eyed scad fish from 2002 to first quarter of 2017. This actual data is available from the open stat database published by the Philippine Statistics Authority (PSA)whose task is to collect, compiles, analyzes and publish information concerning different aspects of the Philippine setting. Autoregressive Integrated Moving Average (ARIMA) models, Artificial Neural Network (ANN) model and the Hybrid model consisting of ARIMA and ANN were developed to forecast catch volumes of big-eyed scad fish. Statistical errors such as Mean Absolute Errors (MAE) and Root Mean Square Errors (RMSE) were computed and compared to choose the most suitable model for forecasting the catch volume for the next few quarters. A comparison of the results of each model and corresponding statistical errors reveals that the hybrid model, ARIMA-ANN (2,1,2)(6:3:1), is the most suitable model to forecast the catch volumes of the big-eyed scad fish for the next few quarters.

Effect of heteroscedasticity treatment in residual error models on model calibration and prediction uncertainty estimation

NASA Astrophysics Data System (ADS)

Sun, Ruochen; Yuan, Huiling; Liu, Xiaoli

2017-11-01

The heteroscedasticity treatment in residual error models directly impacts the model calibration and prediction uncertainty estimation. This study compares three methods to deal with the heteroscedasticity, including the explicit linear modeling (LM) method and nonlinear modeling (NL) method using hyperbolic tangent function, as well as the implicit Box-Cox transformation (BC). Then a combined approach (CA) combining the advantages of both LM and BC methods has been proposed. In conjunction with the first order autoregressive model and the skew exponential power (SEP) distribution, four residual error models are generated, namely LM-SEP, NL-SEP, BC-SEP and CA-SEP, and their corresponding likelihood functions are applied to the Variable Infiltration Capacity (VIC) hydrologic model over the Huaihe River basin, China. Results show that the LM-SEP yields the poorest streamflow predictions with the widest uncertainty band and unrealistic negative flows. The NL and BC methods can better deal with the heteroscedasticity and hence their corresponding predictive performances are improved, yet the negative flows cannot be avoided. The CA-SEP produces the most accurate predictions with the highest reliability and effectively avoids the negative flows, because the CA approach is capable of addressing the complicated heteroscedasticity over the study basin.

Fouling resistance prediction using artificial neural network nonlinear auto-regressive with exogenous input model based on operating conditions and fluid properties correlations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Biyanto, Totok R.

Fouling in a heat exchanger in Crude Preheat Train (CPT) refinery is an unsolved problem that reduces the plant efficiency, increases fuel consumption and CO{sub 2} emission. The fouling resistance behavior is very complex. It is difficult to develop a model using first principle equation to predict the fouling resistance due to different operating conditions and different crude blends. In this paper, Artificial Neural Networks (ANN) MultiLayer Perceptron (MLP) with input structure using Nonlinear Auto-Regressive with eXogenous (NARX) is utilized to build the fouling resistance model in shell and tube heat exchanger (STHX). The input data of the model aremore » flow rates and temperatures of the streams of the heat exchanger, physical properties of product and crude blend data. This model serves as a predicting tool to optimize operating conditions and preventive maintenance of STHX. The results show that the model can capture the complexity of fouling characteristics in heat exchanger due to thermodynamic conditions and variations in crude oil properties (blends). It was found that the Root Mean Square Error (RMSE) are suitable to capture the nonlinearity and complexity of the STHX fouling resistance during phases of training and validation.« less

A heteroskedastic error covariance matrix estimator using a first-order conditional autoregressive Markov simulation for deriving asympotical efficient estimates from ecological sampled Anopheles arabiensis aquatic habitat covariates

PubMed Central

Jacob, Benjamin G; Griffith, Daniel A; Muturi, Ephantus J; Caamano, Erick X; Githure, John I; Novak, Robert J

2009-01-01

Background Autoregressive regression coefficients for Anopheles arabiensis aquatic habitat models are usually assessed using global error techniques and are reported as error covariance matrices. A global statistic, however, will summarize error estimates from multiple habitat locations. This makes it difficult to identify where there are clusters of An. arabiensis aquatic habitats of acceptable prediction. It is therefore useful to conduct some form of spatial error analysis to detect clusters of An. arabiensis aquatic habitats based on uncertainty residuals from individual sampled habitats. In this research, a method of error estimation for spatial simulation models was demonstrated using autocorrelation indices and eigenfunction spatial filters to distinguish among the effects of parameter uncertainty on a stochastic simulation of ecological sampled Anopheles aquatic habitat covariates. A test for diagnostic checking error residuals in an An. arabiensis aquatic habitat model may enable intervention efforts targeting productive habitats clusters, based on larval/pupal productivity, by using the asymptotic distribution of parameter estimates from a residual autocovariance matrix. The models considered in this research extends a normal regression analysis previously considered in the literature. Methods Field and remote-sampled data were collected during July 2006 to December 2007 in Karima rice-village complex in Mwea, Kenya. SAS 9.1.4® was used to explore univariate statistics, correlations, distributions, and to generate global autocorrelation statistics from the ecological sampled datasets. A local autocorrelation index was also generated using spatial covariance parameters (i.e., Moran's Indices) in a SAS/GIS® database. The Moran's statistic was decomposed into orthogonal and uncorrelated synthetic map pattern components using a Poisson model with a gamma-distributed mean (i.e. negative binomial regression). The eigenfunction values from the spatial configuration matrices were then used to define expectations for prior distributions using a Markov chain Monte Carlo (MCMC) algorithm. A set of posterior means were defined in WinBUGS 1.4.3®. After the model had converged, samples from the conditional distributions were used to summarize the posterior distribution of the parameters. Thereafter, a spatial residual trend analyses was used to evaluate variance uncertainty propagation in the model using an autocovariance error matrix. Results By specifying coefficient estimates in a Bayesian framework, the covariate number of tillers was found to be a significant predictor, positively associated with An. arabiensis aquatic habitats. The spatial filter models accounted for approximately 19% redundant locational information in the ecological sampled An. arabiensis aquatic habitat data. In the residual error estimation model there was significant positive autocorrelation (i.e., clustering of habitats in geographic space) based on log-transformed larval/pupal data and the sampled covariate depth of habitat. Conclusion An autocorrelation error covariance matrix and a spatial filter analyses can prioritize mosquito control strategies by providing a computationally attractive and feasible description of variance uncertainty estimates for correctly identifying clusters of prolific An. arabiensis aquatic habitats based on larval/pupal productivity. PMID:19772590

An Ensemble System Based on Hybrid EGARCH-ANN with Different Distributional Assumptions to Predict S&P 500 Intraday Volatility

NASA Astrophysics Data System (ADS)

Lahmiri, S.; Boukadoum, M.

2015-10-01

Accurate forecasting of stock market volatility is an important issue in portfolio risk management. In this paper, an ensemble system for stock market volatility is presented. It is composed of three different models that hybridize the exponential generalized autoregressive conditional heteroscedasticity (GARCH) process and the artificial neural network trained with the backpropagation algorithm (BPNN) to forecast stock market volatility under normal, t-Student, and generalized error distribution (GED) assumption separately. The goal is to design an ensemble system where each single hybrid model is capable to capture normality, excess skewness, or excess kurtosis in the data to achieve complementarity. The performance of each EGARCH-BPNN and the ensemble system is evaluated by the closeness of the volatility forecasts to realized volatility. Based on mean absolute error and mean of squared errors, the experimental results show that proposed ensemble model used to capture normality, skewness, and kurtosis in data is more accurate than the individual EGARCH-BPNN models in forecasting the S&P 500 intra-day volatility based on one and five-minute time horizons data.

Using Google Trends and ambient temperature to predict seasonal influenza outbreaks.

PubMed

Zhang, Yuzhou; Bambrick, Hilary; Mengersen, Kerrie; Tong, Shilu; Hu, Wenbiao

2018-05-16

The discovery of the dynamics of seasonal and non-seasonal influenza outbreaks remains a great challenge. Previous internet-based surveillance studies built purely on internet or climate data do have potential error. We collected influenza notifications, temperature and Google Trends (GT) data between January 1st, 2011 and December 31st, 2016. We performed time-series cross correlation analysis and temporal risk analysis to discover the characteristics of influenza epidemics in the period. Then, the seasonal autoregressive integrated moving average (SARIMA) model and regression tree model were developed to track influenza epidemics using GT and climate data. Influenza infection was significantly corrected with GT at lag of 1-7 weeks in Brisbane and Gold Coast, and temperature at lag of 1-10 weeks for the two study settings. SARIMA models with GT and temperature data had better predictive performance. We identified autoregression (AR) for influenza was the most important determinant for influenza occurrence in both Brisbane and Gold Coast. Our results suggested internet search metrics in conjunction with temperature can be used to predict influenza outbreaks, which can be considered as a pre-requisite for constructing early warning systems using search and temperature data. Copyright © 2018 Elsevier Ltd. All rights reserved.

The quadriceps muscle of knee joint modelling Using Hybrid Particle Swarm Optimization-Neural Network (PSO-NN)

NASA Astrophysics Data System (ADS)

Kamaruddin, Saadi Bin Ahmad; Marponga Tolos, Siti; Hee, Pah Chin; Ghani, Nor Azura Md; Ramli, Norazan Mohamed; Nasir, Noorhamizah Binti Mohamed; Ksm Kader, Babul Salam Bin; Saiful Huq, Mohammad

2017-03-01

Neural framework has for quite a while been known for its ability to handle a complex nonlinear system without a logical model and can learn refined nonlinear associations gives. Theoretically, the most surely understood computation to set up the framework is the backpropagation (BP) count which relies on upon the minimization of the mean square error (MSE). However, this algorithm is not totally efficient in the presence of outliers which usually exist in dynamic data. This paper exhibits the modelling of quadriceps muscle model by utilizing counterfeit smart procedures named consolidated backpropagation neural network nonlinear autoregressive (BPNN-NAR) and backpropagation neural network nonlinear autoregressive moving average (BPNN-NARMA) models in view of utilitarian electrical incitement (FES). We adapted particle swarm optimization (PSO) approach to enhance the performance of backpropagation algorithm. In this research, a progression of tests utilizing FES was led. The information that is gotten is utilized to build up the quadriceps muscle model. 934 preparing information, 200 testing and 200 approval information set are utilized as a part of the improvement of muscle model. It was found that both BPNN-NAR and BPNN-NARMA performed well in modelling this type of data. As a conclusion, the neural network time series models performed reasonably efficient for non-linear modelling such as active properties of the quadriceps muscle with one input, namely output namely muscle force.

Two dynamic regimes in the human gut microbiome

PubMed Central

Smillie, Chris S.; Alm, Eric J.

2017-01-01

The gut microbiome is a dynamic system that changes with host development, health, behavior, diet, and microbe-microbe interactions. Prior work on gut microbial time series has largely focused on autoregressive models (e.g. Lotka-Volterra). However, we show that most of the variance in microbial time series is non-autoregressive. In addition, we show how community state-clustering is flawed when it comes to characterizing within-host dynamics and that more continuous methods are required. Most organisms exhibited stable, mean-reverting behavior suggestive of fixed carrying capacities and abundant taxa were largely shared across individuals. This mean-reverting behavior allowed us to apply sparse vector autoregression (sVAR)—a multivariate method developed for econometrics—to model the autoregressive component of gut community dynamics. We find a strong phylogenetic signal in the non-autoregressive co-variance from our sVAR model residuals, which suggests niche filtering. We show how changes in diet are also non-autoregressive and that Operational Taxonomic Units strongly correlated with dietary variables have much less of an autoregressive component to their variance, which suggests that diet is a major driver of microbial dynamics. Autoregressive variance appears to be driven by multi-day recovery from frequent facultative anaerobe blooms, which may be driven by fluctuations in luminal redox. Overall, we identify two dynamic regimes within the human gut microbiota: one likely driven by external environmental fluctuations, and the other by internal processes. PMID:28222117

Two dynamic regimes in the human gut microbiome.

PubMed

Gibbons, Sean M; Kearney, Sean M; Smillie, Chris S; Alm, Eric J

2017-02-01

The gut microbiome is a dynamic system that changes with host development, health, behavior, diet, and microbe-microbe interactions. Prior work on gut microbial time series has largely focused on autoregressive models (e.g. Lotka-Volterra). However, we show that most of the variance in microbial time series is non-autoregressive. In addition, we show how community state-clustering is flawed when it comes to characterizing within-host dynamics and that more continuous methods are required. Most organisms exhibited stable, mean-reverting behavior suggestive of fixed carrying capacities and abundant taxa were largely shared across individuals. This mean-reverting behavior allowed us to apply sparse vector autoregression (sVAR)-a multivariate method developed for econometrics-to model the autoregressive component of gut community dynamics. We find a strong phylogenetic signal in the non-autoregressive co-variance from our sVAR model residuals, which suggests niche filtering. We show how changes in diet are also non-autoregressive and that Operational Taxonomic Units strongly correlated with dietary variables have much less of an autoregressive component to their variance, which suggests that diet is a major driver of microbial dynamics. Autoregressive variance appears to be driven by multi-day recovery from frequent facultative anaerobe blooms, which may be driven by fluctuations in luminal redox. Overall, we identify two dynamic regimes within the human gut microbiota: one likely driven by external environmental fluctuations, and the other by internal processes.

Quasi-Likelihood Techniques in a Logistic Regression Equation for Identifying Simulium damnosum s.l. Larval Habitats Intra-cluster Covariates in Togo.

PubMed

Jacob, Benjamin G; Novak, Robert J; Toe, Laurent; Sanfo, Moussa S; Afriyie, Abena N; Ibrahim, Mohammed A; Griffith, Daniel A; Unnasch, Thomas R

2012-01-01

The standard methods for regression analyses of clustered riverine larval habitat data of Simulium damnosum s.l. a major black-fly vector of Onchoceriasis, postulate models relating observational ecological-sampled parameter estimators to prolific habitats without accounting for residual intra-cluster error correlation effects. Generally, this correlation comes from two sources: (1) the design of the random effects and their assumed covariance from the multiple levels within the regression model; and, (2) the correlation structure of the residuals. Unfortunately, inconspicuous errors in residual intra-cluster correlation estimates can overstate precision in forecasted S.damnosum s.l. riverine larval habitat explanatory attributes regardless how they are treated (e.g., independent, autoregressive, Toeplitz, etc). In this research, the geographical locations for multiple riverine-based S. damnosum s.l. larval ecosystem habitats sampled from 2 pre-established epidemiological sites in Togo were identified and recorded from July 2009 to June 2010. Initially the data was aggregated into proc genmod. An agglomerative hierarchical residual cluster-based analysis was then performed. The sampled clustered study site data was then analyzed for statistical correlations using Monthly Biting Rates (MBR). Euclidean distance measurements and terrain-related geomorphological statistics were then generated in ArcGIS. A digital overlay was then performed also in ArcGIS using the georeferenced ground coordinates of high and low density clusters stratified by Annual Biting Rates (ABR). This data was overlain onto multitemporal sub-meter pixel resolution satellite data (i.e., QuickBird 0.61m wavbands ). Orthogonal spatial filter eigenvectors were then generated in SAS/GIS. Univariate and non-linear regression-based models (i.e., Logistic, Poisson and Negative Binomial) were also employed to determine probability distributions and to identify statistically significant parameter estimators from the sampled data. Thereafter, Durbin-Watson test statistics were used to test the null hypothesis that the regression residuals were not autocorrelated against the alternative that the residuals followed an autoregressive process in AUTOREG. Bayesian uncertainty matrices were also constructed employing normal priors for each of the sampled estimators in PROC MCMC. The residuals revealed both spatially structured and unstructured error effects in the high and low ABR-stratified clusters. The analyses also revealed that the estimators, levels of turbidity and presence of rocks were statistically significant for the high-ABR-stratified clusters, while the estimators distance between habitats and floating vegetation were important for the low-ABR-stratified cluster. Varying and constant coefficient regression models, ABR- stratified GIS-generated clusters, sub-meter resolution satellite imagery, a robust residual intra-cluster diagnostic test, MBR-based histograms, eigendecomposition spatial filter algorithms and Bayesian matrices can enable accurate autoregressive estimation of latent uncertainity affects and other residual error probabilities (i.e., heteroskedasticity) for testing correlations between georeferenced S. damnosum s.l. riverine larval habitat estimators. The asymptotic distribution of the resulting residual adjusted intra-cluster predictor error autocovariate coefficients can thereafter be established while estimates of the asymptotic variance can lead to the construction of approximate confidence intervals for accurately targeting productive S. damnosum s.l habitats based on spatiotemporal field-sampled count data.

Forecasting of Water Consumptions Expenditure Using Holt-Winter’s and ARIMA

NASA Astrophysics Data System (ADS)

Razali, S. N. A. M.; Rusiman, M. S.; Zawawi, N. I.; Arbin, N.

2018-04-01

This study is carried out to forecast water consumption expenditure of Malaysian university specifically at University Tun Hussein Onn Malaysia (UTHM). The proposed Holt-Winter’s and Auto-Regressive Integrated Moving Average (ARIMA) models were applied to forecast the water consumption expenditure in Ringgit Malaysia from year 2006 until year 2014. The two models were compared and performance measurement of the Mean Absolute Percentage Error (MAPE) and Mean Absolute Deviation (MAD) were used. It is found that ARIMA model showed better results regarding the accuracy of forecast with lower values of MAPE and MAD. Analysis showed that ARIMA (2,1,4) model provided a reasonable forecasting tool for university campus water usage.

Very-short-term wind power prediction by a hybrid model with single- and multi-step approaches

NASA Astrophysics Data System (ADS)

Mohammed, E.; Wang, S.; Yu, J.

2017-05-01

Very-short-term wind power prediction (VSTWPP) has played an essential role for the operation of electric power systems. This paper aims at improving and applying a hybrid method of VSTWPP based on historical data. The hybrid method is combined by multiple linear regressions and least square (MLR&LS), which is intended for reducing prediction errors. The predicted values are obtained through two sub-processes:1) transform the time-series data of actual wind power into the power ratio, and then predict the power ratio;2) use the predicted power ratio to predict the wind power. Besides, the proposed method can include two prediction approaches: single-step prediction (SSP) and multi-step prediction (MSP). WPP is tested comparatively by auto-regressive moving average (ARMA) model from the predicted values and errors. The validity of the proposed hybrid method is confirmed in terms of error analysis by using probability density function (PDF), mean absolute percent error (MAPE) and means square error (MSE). Meanwhile, comparison of the correlation coefficients between the actual values and the predicted values for different prediction times and window has confirmed that MSP approach by using the hybrid model is the most accurate while comparing to SSP approach and ARMA. The MLR&LS is accurate and promising for solving problems in WPP.

Simulation-based power calculation for designing interrupted time series analyses of health policy interventions.

PubMed

Zhang, Fang; Wagner, Anita K; Ross-Degnan, Dennis

2011-11-01

Interrupted time series is a strong quasi-experimental research design to evaluate the impacts of health policy interventions. Using simulation methods, we estimated the power requirements for interrupted time series studies under various scenarios. Simulations were conducted to estimate the power of segmented autoregressive (AR) error models when autocorrelation ranged from -0.9 to 0.9 and effect size was 0.5, 1.0, and 2.0, investigating balanced and unbalanced numbers of time periods before and after an intervention. Simple scenarios of autoregressive conditional heteroskedasticity (ARCH) models were also explored. For AR models, power increased when sample size or effect size increased, and tended to decrease when autocorrelation increased. Compared with a balanced number of study periods before and after an intervention, designs with unbalanced numbers of periods had less power, although that was not the case for ARCH models. The power to detect effect size 1.0 appeared to be reasonable for many practical applications with a moderate or large number of time points in the study equally divided around the intervention. Investigators should be cautious when the expected effect size is small or the number of time points is small. We recommend conducting various simulations before investigation. Copyright © 2011 Elsevier Inc. All rights reserved.

A Rigorous Temperature-Dependent Stochastic Modelling and Testing for MEMS-Based Inertial Sensor Errors.

PubMed

El-Diasty, Mohammed; Pagiatakis, Spiros

2009-01-01

In this paper, we examine the effect of changing the temperature points on MEMS-based inertial sensor random error. We collect static data under different temperature points using a MEMS-based inertial sensor mounted inside a thermal chamber. Rigorous stochastic models, namely Autoregressive-based Gauss-Markov (AR-based GM) models are developed to describe the random error behaviour. The proposed AR-based GM model is initially applied to short stationary inertial data to develop the stochastic model parameters (correlation times). It is shown that the stochastic model parameters of a MEMS-based inertial unit, namely the ADIS16364, are temperature dependent. In addition, field kinematic test data collected at about 17 °C are used to test the performance of the stochastic models at different temperature points in the filtering stage using Unscented Kalman Filter (UKF). It is shown that the stochastic model developed at 20 °C provides a more accurate inertial navigation solution than the ones obtained from the stochastic models developed at -40 °C, -20 °C, 0 °C, +40 °C, and +60 °C. The temperature dependence of the stochastic model is significant and should be considered at all times to obtain optimal navigation solution for MEMS-based INS/GPS integration.

Linear mixed-effects models to describe individual tree crown width for China-fir in Fujian Province, southeast China.

PubMed

Hao, Xu; Yujun, Sun; Xinjie, Wang; Jin, Wang; Yao, Fu

2015-01-01

A multiple linear model was developed for individual tree crown width of Cunninghamia lanceolata (Lamb.) Hook in Fujian province, southeast China. Data were obtained from 55 sample plots of pure China-fir plantation stands. An Ordinary Linear Least Squares (OLS) regression was used to establish the crown width model. To adjust for correlations between observations from the same sample plots, we developed one level linear mixed-effects (LME) models based on the multiple linear model, which take into account the random effects of plots. The best random effects combinations for the LME models were determined by the Akaike's information criterion, the Bayesian information criterion and the -2logarithm likelihood. Heteroscedasticity was reduced by three residual variance functions: the power function, the exponential function and the constant plus power function. The spatial correlation was modeled by three correlation structures: the first-order autoregressive structure [AR(1)], a combination of first-order autoregressive and moving average structures [ARMA(1,1)], and the compound symmetry structure (CS). Then, the LME model was compared to the multiple linear model using the absolute mean residual (AMR), the root mean square error (RMSE), and the adjusted coefficient of determination (adj-R2). For individual tree crown width models, the one level LME model showed the best performance. An independent dataset was used to test the performance of the models and to demonstrate the advantage of calibrating LME models.

Work-related accidents among the Iranian population: a time series analysis, 2000–2011

PubMed Central

Karimlou, Masoud; Imani, Mehdi; Hosseini, Agha-Fatemeh; Dehnad, Afsaneh; Vahabi, Nasim; Bakhtiyari, Mahmood

2015-01-01

Background Work-related accidents result in human suffering and economic losses and are considered as a major health problem worldwide, especially in the economically developing world. Objectives To introduce seasonal autoregressive moving average (ARIMA) models for time series analysis of work-related accident data for workers insured by the Iranian Social Security Organization (ISSO) between 2000 and 2011. Methods In this retrospective study, all insured people experiencing at least one work-related accident during a 10-year period were included in the analyses. We used Box–Jenkins modeling to develop a time series model of the total number of accidents. Results There was an average of 1476 accidents per month (1476·05±458·77, mean±SD). The final ARIMA (p,d,q) (P,D,Q)s model for fitting to data was: ARIMA(1,1,1)×(0,1,1)12 consisting of the first ordering of the autoregressive, moving average and seasonal moving average parameters with 20·942 mean absolute percentage error (MAPE). Conclusions The final model showed that time series analysis of ARIMA models was useful for forecasting the number of work-related accidents in Iran. In addition, the forecasted number of work-related accidents for 2011 explained the stability of occurrence of these accidents in recent years, indicating a need for preventive occupational health and safety policies such as safety inspection. PMID:26119774

Work-related accidents among the Iranian population: a time series analysis, 2000-2011.

PubMed

Karimlou, Masoud; Salehi, Masoud; Imani, Mehdi; Hosseini, Agha-Fatemeh; Dehnad, Afsaneh; Vahabi, Nasim; Bakhtiyari, Mahmood

2015-01-01

Work-related accidents result in human suffering and economic losses and are considered as a major health problem worldwide, especially in the economically developing world. To introduce seasonal autoregressive moving average (ARIMA) models for time series analysis of work-related accident data for workers insured by the Iranian Social Security Organization (ISSO) between 2000 and 2011. In this retrospective study, all insured people experiencing at least one work-related accident during a 10-year period were included in the analyses. We used Box-Jenkins modeling to develop a time series model of the total number of accidents. There was an average of 1476 accidents per month (1476·05±458·77, mean±SD). The final ARIMA (p,d,q) (P,D,Q)s model for fitting to data was: ARIMA(1,1,1)×(0,1,1)12 consisting of the first ordering of the autoregressive, moving average and seasonal moving average parameters with 20·942 mean absolute percentage error (MAPE). The final model showed that time series analysis of ARIMA models was useful for forecasting the number of work-related accidents in Iran. In addition, the forecasted number of work-related accidents for 2011 explained the stability of occurrence of these accidents in recent years, indicating a need for preventive occupational health and safety policies such as safety inspection.

A Comparison of Forecast Error Generators for Modeling Wind and Load Uncertainty

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lu, Ning; Diao, Ruisheng; Hafen, Ryan P.

2013-07-25

This paper presents four algorithms to generate random forecast error time series. The performance of four algorithms is compared. The error time series are used to create real-time (RT), hour-ahead (HA), and day-ahead (DA) wind and load forecast time series that statistically match historically observed forecasting data sets used in power grid operation to study the net load balancing need in variable generation integration studies. The four algorithms are truncated-normal distribution models, state-space based Markov models, seasonal autoregressive moving average (ARMA) models, and a stochastic-optimization based approach. The comparison is made using historical DA load forecast and actual load valuesmore » to generate new sets of DA forecasts with similar stoical forecast error characteristics (i.e., mean, standard deviation, autocorrelation, and cross-correlation). The results show that all methods generate satisfactory results. One method may preserve one or two required statistical characteristics better the other methods, but may not preserve other statistical characteristics as well compared with the other methods. Because the wind and load forecast error generators are used in wind integration studies to produce wind and load forecasts time series for stochastic planning processes, it is sometimes critical to use multiple methods to generate the error time series to obtain a statistically robust result. Therefore, this paper discusses and compares the capabilities of each algorithm to preserve the characteristics of the historical forecast data sets.« less

A comparative study of shallow groundwater level simulation with three time series models in a coastal aquifer of South China

NASA Astrophysics Data System (ADS)

Yang, Q.; Wang, Y.; Zhang, J.; Delgado, J.

2017-05-01

Accurate and reliable groundwater level forecasting models can help ensure the sustainable use of a watershed's aquifers for urban and rural water supply. In this paper, three time series analysis methods, Holt-Winters (HW), integrated time series (ITS), and seasonal autoregressive integrated moving average (SARIMA), are explored to simulate the groundwater level in a coastal aquifer, China. The monthly groundwater table depth data collected in a long time series from 2000 to 2011 are simulated and compared with those three time series models. The error criteria are estimated using coefficient of determination ( R 2), Nash-Sutcliffe model efficiency coefficient ( E), and root-mean-squared error. The results indicate that three models are all accurate in reproducing the historical time series of groundwater levels. The comparisons of three models show that HW model is more accurate in predicting the groundwater levels than SARIMA and ITS models. It is recommended that additional studies explore this proposed method, which can be used in turn to facilitate the development and implementation of more effective and sustainable groundwater management strategies.

Random Process Simulation for stochastic fatigue analysis. Ph.D. Thesis - Rice Univ., Houston, Tex.

NASA Technical Reports Server (NTRS)

Larsen, Curtis E.

1988-01-01

A simulation technique is described which directly synthesizes the extrema of a random process and is more efficient than the Gaussian simulation method. Such a technique is particularly useful in stochastic fatigue analysis because the required stress range moment E(R sup m), is a function only of the extrema of the random stress process. The family of autoregressive moving average (ARMA) models is reviewed and an autoregressive model is presented for modeling the extrema of any random process which has a unimodal power spectral density (psd). The proposed autoregressive technique is found to produce rainflow stress range moments which compare favorably with those computed by the Gaussian technique and to average 11.7 times faster than the Gaussian technique. The autoregressive technique is also adapted for processes having bimodal psd's. The adaptation involves using two autoregressive processes to simulate the extrema due to each mode and the superposition of these two extrema sequences. The proposed autoregressive superposition technique is 9 to 13 times faster than the Gaussian technique and produces comparable values for E(R sup m) for bimodal psd's having the frequency of one mode at least 2.5 times that of the other mode.

Autocorrelated residuals in inverse modelling of soil hydrological processes: a reason for concern or something that can safely be ignored?

NASA Astrophysics Data System (ADS)

Scharnagl, Benedikt; Durner, Wolfgang

2013-04-01

Models are inherently imperfect because they simplify processes that are themselves imperfectly known and understood. Moreover, the input variables and parameters needed to run a model are typically subject to various sources of error. As a consequence of these imperfections, model predictions will always deviate from corresponding observations. In most applications in soil hydrology, these deviations are clearly not random but rather show a systematic structure. From a statistical point of view, this systematic mismatch may be a reason for concern because it violates one of the basic assumptions made in inverse parameter estimation: the assumption of independence of the residuals. But what are the consequences of simply ignoring the autocorrelation in the residuals, as it is current practice in soil hydrology? Are the parameter estimates still valid even though the statistical foundation they are based on is partially collapsed? Theory and practical experience from other fields of science have shown that violation of the independence assumption will result in overconfident uncertainty bounds and that in some cases it may lead to significantly different optimal parameter values. In our contribution, we present three soil hydrological case studies, in which the effect of autocorrelated residuals on the estimated parameters was investigated in detail. We explicitly accounted for autocorrelated residuals using a formal likelihood function that incorporates an autoregressive model. The inverse problem was posed in a Bayesian framework, and the posterior probability density function of the parameters was estimated using Markov chain Monte Carlo simulation. In contrast to many other studies in related fields of science, and quite surprisingly, we found that the first-order autoregressive model, often abbreviated as AR(1), did not work well in the soil hydrological setting. We showed that a second-order autoregressive, or AR(2), model performs much better in these applications, leading to parameter and uncertainty estimates that satisfy all the underlying statistical assumptions. For theoretical reasons, these estimates are deemed more reliable than those estimates based on the neglect of autocorrelation in the residuals. In compliance with theory and results reported in the literature, our results showed that parameter uncertainty bounds were substantially wider if autocorrelation in the residuals was explicitly accounted for, and also the optimal parameter vales were slightly different in this case. We argue that the autoregressive model presented here should be used as a matter of routine in inverse modeling of soil hydrological processes.

A vector auto-regressive model for onshore and offshore wind synthesis incorporating meteorological model information

NASA Astrophysics Data System (ADS)

Hill, D.; Bell, K. R. W.; McMillan, D.; Infield, D.

2014-05-01

The growth of wind power production in the electricity portfolio is striving to meet ambitious targets set, for example by the EU, to reduce greenhouse gas emissions by 20% by 2020. Huge investments are now being made in new offshore wind farms around UK coastal waters that will have a major impact on the GB electrical supply. Representations of the UK wind field in syntheses which capture the inherent structure and correlations between different locations including offshore sites are required. Here, Vector Auto-Regressive (VAR) models are presented and extended in a novel way to incorporate offshore time series from a pan-European meteorological model called COSMO, with onshore wind speeds from the MIDAS dataset provided by the British Atmospheric Data Centre. Forecasting ability onshore is shown to be improved with the inclusion of the offshore sites with improvements of up to 25% in RMS error at 6 h ahead. In addition, the VAR model is used to synthesise time series of wind at each offshore site, which are then used to estimate wind farm capacity factors at the sites in question. These are then compared with estimates of capacity factors derived from the work of Hawkins et al. (2011). A good degree of agreement is established indicating that this synthesis tool should be useful in power system impact studies.

An accurate nonlinear stochastic model for MEMS-based inertial sensor error with wavelet networks

NASA Astrophysics Data System (ADS)

El-Diasty, Mohammed; El-Rabbany, Ahmed; Pagiatakis, Spiros

2007-12-01

The integration of Global Positioning System (GPS) with Inertial Navigation System (INS) has been widely used in many applications for positioning and orientation purposes. Traditionally, random walk (RW), Gauss-Markov (GM), and autoregressive (AR) processes have been used to develop the stochastic model in classical Kalman filters. The main disadvantage of classical Kalman filter is the potentially unstable linearization of the nonlinear dynamic system. Consequently, a nonlinear stochastic model is not optimal in derivative-based filters due to the expected linearization error. With a derivativeless-based filter such as the unscented Kalman filter or the divided difference filter, the filtering process of a complicated highly nonlinear dynamic system is possible without linearization error. This paper develops a novel nonlinear stochastic model for inertial sensor error using a wavelet network (WN). A wavelet network is a highly nonlinear model, which has recently been introduced as a powerful tool for modelling and prediction. Static and kinematic data sets are collected using a MEMS-based IMU (DQI-100) to develop the stochastic model in the static mode and then implement it in the kinematic mode. The derivativeless-based filtering method using GM, AR, and the proposed WN-based processes are used to validate the new model. It is shown that the first-order WN-based nonlinear stochastic model gives superior positioning results to the first-order GM and AR models with an overall improvement of 30% when 30 and 60 seconds GPS outages are introduced.

Forecasting coconut production in the Philippines with ARIMA model

NASA Astrophysics Data System (ADS)

Lim, Cristina Teresa

2015-02-01

The study aimed to depict the situation of the coconut industry in the Philippines for the future years applying Autoregressive Integrated Moving Average (ARIMA) method. Data on coconut production, one of the major industrial crops of the country, for the period of 1990 to 2012 were analyzed using time-series methods. Autocorrelation (ACF) and partial autocorrelation functions (PACF) were calculated for the data. Appropriate Box-Jenkins autoregressive moving average model was fitted. Validity of the model was tested using standard statistical techniques. The forecasting power of autoregressive moving average (ARMA) model was used to forecast coconut production for the eight leading years.

Subregional Nowcasts of Seasonal Influenza Using Search Trends.

PubMed

Kandula, Sasikiran; Hsu, Daniel; Shaman, Jeffrey

2017-11-06

Limiting the adverse effects of seasonal influenza outbreaks at state or city level requires close monitoring of localized outbreaks and reliable forecasts of their progression. Whereas forecasting models for influenza or influenza-like illness (ILI) are becoming increasingly available, their applicability to localized outbreaks is limited by the nonavailability of real-time observations of the current outbreak state at local scales. Surveillance data collected by various health departments are widely accepted as the reference standard for estimating the state of outbreaks, and in the absence of surveillance data, nowcast proxies built using Web-based activities such as search engine queries, tweets, and access of health-related webpages can be useful. Nowcast estimates of state and municipal ILI were previously published by Google Flu Trends (GFT); however, validations of these estimates were seldom reported. The aim of this study was to develop and validate models to nowcast ILI at subregional geographic scales. We built nowcast models based on autoregressive (autoregressive integrated moving average; ARIMA) and supervised regression methods (Random forests) at the US state level using regional weighted ILI and Web-based search activity derived from Google's Extended Trends application programming interface. We validated the performance of these methods using actual surveillance data for the 50 states across six seasons. We also built state-level nowcast models using state-level estimates of ILI and compared the accuracy of these estimates with the estimates of the regional models extrapolated to the state level and with the nowcast estimates published by GFT. Models built using regional ILI extrapolated to state level had a median correlation of 0.84 (interquartile range: 0.74-0.91) and a median root mean square error (RMSE) of 1.01 (IQR: 0.74-1.50), with noticeable variability across seasons and by state population size. Model forms that hypothesize the availability of timely state-level surveillance data show significantly lower errors of 0.83 (0.55-0.23). Compared with GFT, the latter model forms have lower errors but also lower correlation. These results suggest that the proposed methods may be an alternative to the discontinued GFT and that further improvements in the quality of subregional nowcasts may require increased access to more finely resolved surveillance data. ©Sasikiran Kandula, Daniel Hsu, Jeffrey Shaman. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 06.11.2017.

Modeling Seasonality in Carbon Dioxide Emissions From Fossil Fuel Consumption

NASA Astrophysics Data System (ADS)

Gregg, J. S.; Andres, R. J.

2004-05-01

Using United States data, a method is developed to estimate the monthly consumption of solid, liquid and gaseous fossil fuels using monthly sales data to estimate the relative monthly proportions of the total annual national fossil fuel use. These proportions are then used to estimate the total monthly carbon dioxide emissions for each state. From these data, the goal is to develop mathematical models that describe the seasonal flux in consumption for each type of fuel, as well as the total emissions for the nation. The time series models have two components. First, the general long-term yearly trend is determined with regression models for the annual totals. After removing the general trend, two alternatives are considered for modeling the seasonality. The first alternative uses the mean of the monthly proportions to predict the seasonal distribution. Because the seasonal patterns are fairly consistent in the United States, this is an effective modeling technique. Such regularity, however, may not be present with data from other nations. Therefore, as a second alternative, an ordinary least squares autoregressive model is used. This model is chosen for its ability to accurately describe dependent data and for its predictive capacity. It also has a meaningful interpretation, as each coefficient in the model quantifies the dependency for each corresponding time lag. Most importantly, it is dynamic, and able to adapt to anomalies and changing patterns. The order of the autoregressive model is chosen by the Akaike Information Criterion (AIC), which minimizes the predicted variance for all models of increasing complexity. To model the monthly fuel consumption, the annual trend is combined with the seasonal model. The models for each fuel type are then summed together to predict the total carbon dioxide emissions. The prediction error is estimated with the root mean square error (RMSE) from the actual estimated emission values. Overall, the models perform very well, with relative RMSE less than 10% for all fuel types, and under 5% for the national total emissions. Development of successful models is important to better understand and predict global environmental impacts from fossil fuel consumption.

Equivalent Dynamic Models.

PubMed

Molenaar, Peter C M

2017-01-01

Equivalences of two classes of dynamic models for weakly stationary multivariate time series are discussed: dynamic factor models and autoregressive models. It is shown that exploratory dynamic factor models can be rotated, yielding an infinite set of equivalent solutions for any observed series. It also is shown that dynamic factor models with lagged factor loadings are not equivalent to the currently popular state-space models, and that restriction of attention to the latter type of models may yield invalid results. The known equivalent vector autoregressive model types, standard and structural, are given a new interpretation in which they are conceived of as the extremes of an innovating type of hybrid vector autoregressive models. It is shown that consideration of hybrid models solves many problems, in particular with Granger causality testing.

Vector Autoregression, Structural Equation Modeling, and Their Synthesis in Neuroimaging Data Analysis

PubMed Central

Chen, Gang; Glen, Daniel R.; Saad, Ziad S.; Hamilton, J. Paul; Thomason, Moriah E.; Gotlib, Ian H.; Cox, Robert W.

2011-01-01

Vector autoregression (VAR) and structural equation modeling (SEM) are two popular brain-network modeling tools. VAR, which is a data-driven approach, assumes that connected regions exert time-lagged influences on one another. In contrast, the hypothesis-driven SEM is used to validate an existing connectivity model where connected regions have contemporaneous interactions among them. We present the two models in detail and discuss their applicability to FMRI data, and interpretational limits. We also propose a unified approach that models both lagged and contemporaneous effects. The unifying model, structural vector autoregression (SVAR), may improve statistical and explanatory power, and avoids some prevalent pitfalls that can occur when VAR and SEM are utilized separately. PMID:21975109

Efficient hierarchical trans-dimensional Bayesian inversion of magnetotelluric data

NASA Astrophysics Data System (ADS)

Xiang, Enming; Guo, Rongwen; Dosso, Stan E.; Liu, Jianxin; Dong, Hao; Ren, Zhengyong

2018-06-01

This paper develops an efficient hierarchical trans-dimensional (trans-D) Bayesian algorithm to invert magnetotelluric (MT) data for subsurface geoelectrical structure, with unknown geophysical model parameterization (the number of conductivity-layer interfaces) and data-error models parameterized by an auto-regressive (AR) process to account for potential error correlations. The reversible-jump Markov-chain Monte Carlo algorithm, which adds/removes interfaces and AR parameters in birth/death steps, is applied to sample the trans-D posterior probability density for model parameterization, model parameters, error variance and AR parameters, accounting for the uncertainties of model dimension and data-error statistics in the uncertainty estimates of the conductivity profile. To provide efficient sampling over the multiple subspaces of different dimensions, advanced proposal schemes are applied. Parameter perturbations are carried out in principal-component space, defined by eigen-decomposition of the unit-lag model covariance matrix, to minimize the effect of inter-parameter correlations and provide effective perturbation directions and length scales. Parameters of new layers in birth steps are proposed from the prior, instead of focused distributions centred at existing values, to improve birth acceptance rates. Parallel tempering, based on a series of parallel interacting Markov chains with successively relaxed likelihoods, is applied to improve chain mixing over model dimensions. The trans-D inversion is applied in a simulation study to examine the resolution of model structure according to the data information content. The inversion is also applied to a measured MT data set from south-central Australia.

How to compare cross-lagged associations in a multilevel autoregressive model.

PubMed

Schuurman, Noémi K; Ferrer, Emilio; de Boer-Sonnenschein, Mieke; Hamaker, Ellen L

2016-06-01

By modeling variables over time it is possible to investigate the Granger-causal cross-lagged associations between variables. By comparing the standardized cross-lagged coefficients, the relative strength of these associations can be evaluated in order to determine important driving forces in the dynamic system. The aim of this study was twofold: first, to illustrate the added value of a multilevel multivariate autoregressive modeling approach for investigating these associations over more traditional techniques; and second, to discuss how the coefficients of the multilevel autoregressive model should be standardized for comparing the strength of the cross-lagged associations. The hierarchical structure of multilevel multivariate autoregressive models complicates standardization, because subject-based statistics or group-based statistics can be used to standardize the coefficients, and each method may result in different conclusions. We argue that in order to make a meaningful comparison of the strength of the cross-lagged associations, the coefficients should be standardized within persons. We further illustrate the bivariate multilevel autoregressive model and the standardization of the coefficients, and we show that disregarding individual differences in dynamics can prove misleading, by means of an empirical example on experienced competence and exhaustion in persons diagnosed with burnout. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

To center or not to center? Investigating inertia with a multilevel autoregressive model.

PubMed

Hamaker, Ellen L; Grasman, Raoul P P P

2014-01-01

Whether level 1 predictors should be centered per cluster has received considerable attention in the multilevel literature. While most agree that there is no one preferred approach, it has also been argued that cluster mean centering is desirable when the within-cluster slope and the between-cluster slope are expected to deviate, and the main interest is in the within-cluster slope. However, we show in a series of simulations that if one has a multilevel autoregressive model in which the level 1 predictor is the lagged outcome variable (i.e., the outcome variable at the previous occasion), cluster mean centering will in general lead to a downward bias in the parameter estimate of the within-cluster slope (i.e., the autoregressive relationship). This is particularly relevant if the main question is whether there is on average an autoregressive effect. Nonetheless, we show that if the main interest is in estimating the effect of a level 2 predictor on the autoregressive parameter (i.e., a cross-level interaction), cluster mean centering should be preferred over other forms of centering. Hence, researchers should be clear on what is considered the main goal of their study, and base their choice of centering method on this when using a multilevel autoregressive model.

To center or not to center? Investigating inertia with a multilevel autoregressive model

PubMed Central

Hamaker, Ellen L.; Grasman, Raoul P. P. P.

2015-01-01

Whether level 1 predictors should be centered per cluster has received considerable attention in the multilevel literature. While most agree that there is no one preferred approach, it has also been argued that cluster mean centering is desirable when the within-cluster slope and the between-cluster slope are expected to deviate, and the main interest is in the within-cluster slope. However, we show in a series of simulations that if one has a multilevel autoregressive model in which the level 1 predictor is the lagged outcome variable (i.e., the outcome variable at the previous occasion), cluster mean centering will in general lead to a downward bias in the parameter estimate of the within-cluster slope (i.e., the autoregressive relationship). This is particularly relevant if the main question is whether there is on average an autoregressive effect. Nonetheless, we show that if the main interest is in estimating the effect of a level 2 predictor on the autoregressive parameter (i.e., a cross-level interaction), cluster mean centering should be preferred over other forms of centering. Hence, researchers should be clear on what is considered the main goal of their study, and base their choice of centering method on this when using a multilevel autoregressive model. PMID:25688215

A comparison between different error modeling of MEMS applied to GPS/INS integrated systems.

PubMed

Quinchia, Alex G; Falco, Gianluca; Falletti, Emanuela; Dovis, Fabio; Ferrer, Carles

2013-07-24

Advances in the development of micro-electromechanical systems (MEMS) have made possible the fabrication of cheap and small dimension accelerometers and gyroscopes, which are being used in many applications where the global positioning system (GPS) and the inertial navigation system (INS) integration is carried out, i.e., identifying track defects, terrestrial and pedestrian navigation, unmanned aerial vehicles (UAVs), stabilization of many platforms, etc. Although these MEMS sensors are low-cost, they present different errors, which degrade the accuracy of the navigation systems in a short period of time. Therefore, a suitable modeling of these errors is necessary in order to minimize them and, consequently, improve the system performance. In this work, the most used techniques currently to analyze the stochastic errors that affect these sensors are shown and compared: we examine in detail the autocorrelation, the Allan variance (AV) and the power spectral density (PSD) techniques. Subsequently, an analysis and modeling of the inertial sensors, which combines autoregressive (AR) filters and wavelet de-noising, is also achieved. Since a low-cost INS (MEMS grade) presents error sources with short-term (high-frequency) and long-term (low-frequency) components, we introduce a method that compensates for these error terms by doing a complete analysis of Allan variance, wavelet de-nosing and the selection of the level of decomposition for a suitable combination between these techniques. Eventually, in order to assess the stochastic models obtained with these techniques, the Extended Kalman Filter (EKF) of a loosely-coupled GPS/INS integration strategy is augmented with different states. Results show a comparison between the proposed method and the traditional sensor error models under GPS signal blockages using real data collected in urban roadways.

A Comparison between Different Error Modeling of MEMS Applied to GPS/INS Integrated Systems

PubMed Central

Quinchia, Alex G.; Falco, Gianluca; Falletti, Emanuela; Dovis, Fabio; Ferrer, Carles

2013-01-01

Advances in the development of micro-electromechanical systems (MEMS) have made possible the fabrication of cheap and small dimension accelerometers and gyroscopes, which are being used in many applications where the global positioning system (GPS) and the inertial navigation system (INS) integration is carried out, i.e., identifying track defects, terrestrial and pedestrian navigation, unmanned aerial vehicles (UAVs), stabilization of many platforms, etc. Although these MEMS sensors are low-cost, they present different errors, which degrade the accuracy of the navigation systems in a short period of time. Therefore, a suitable modeling of these errors is necessary in order to minimize them and, consequently, improve the system performance. In this work, the most used techniques currently to analyze the stochastic errors that affect these sensors are shown and compared: we examine in detail the autocorrelation, the Allan variance (AV) and the power spectral density (PSD) techniques. Subsequently, an analysis and modeling of the inertial sensors, which combines autoregressive (AR) filters and wavelet de-noising, is also achieved. Since a low-cost INS (MEMS grade) presents error sources with short-term (high-frequency) and long-term (low-frequency) components, we introduce a method that compensates for these error terms by doing a complete analysis of Allan variance, wavelet de-nosing and the selection of the level of decomposition for a suitable combination between these techniques. Eventually, in order to assess the stochastic models obtained with these techniques, the Extended Kalman Filter (EKF) of a loosely-coupled GPS/INS integration strategy is augmented with different states. Results show a comparison between the proposed method and the traditional sensor error models under GPS signal blockages using real data collected in urban roadways. PMID:23887084

Mixture of autoregressive modeling orders and its implication on single trial EEG classification

PubMed Central

Atyabi, Adham; Shic, Frederick; Naples, Adam

2016-01-01

Autoregressive (AR) models are of commonly utilized feature types in Electroencephalogram (EEG) studies due to offering better resolution, smoother spectra and being applicable to short segments of data. Identifying correct AR’s modeling order is an open challenge. Lower model orders poorly represent the signal while higher orders increase noise. Conventional methods for estimating modeling order includes Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and Final Prediction Error (FPE). This article assesses the hypothesis that appropriate mixture of multiple AR orders is likely to better represent the true signal compared to any single order. Better spectral representation of underlying EEG patterns can increase utility of AR features in Brain Computer Interface (BCI) systems by increasing timely & correctly responsiveness of such systems to operator’s thoughts. Two mechanisms of Evolutionary-based fusion and Ensemble-based mixture are utilized for identifying such appropriate mixture of modeling orders. The classification performance of the resultant AR-mixtures are assessed against several conventional methods utilized by the community including 1) A well-known set of commonly used orders suggested by the literature, 2) conventional order estimation approaches (e.g., AIC, BIC and FPE), 3) blind mixture of AR features originated from a range of well-known orders. Five datasets from BCI competition III that contain 2, 3 and 4 motor imagery tasks are considered for the assessment. The results indicate superiority of Ensemble-based modeling order mixture and evolutionary-based order fusion methods within all datasets. PMID:28740331

Characteristics of the transmission of autoregressive sub-patterns in financial time series

NASA Astrophysics Data System (ADS)

Gao, Xiangyun; An, Haizhong; Fang, Wei; Huang, Xuan; Li, Huajiao; Zhong, Weiqiong

2014-09-01

There are many types of autoregressive patterns in financial time series, and they form a transmission process. Here, we define autoregressive patterns quantitatively through an econometrical regression model. We present a computational algorithm that sets the autoregressive patterns as nodes and transmissions between patterns as edges, and then converts the transmission process of autoregressive patterns in a time series into a network. We utilised daily Shanghai (securities) composite index time series to study the transmission characteristics of autoregressive patterns. We found statistically significant evidence that the financial market is not random and that there are similar characteristics between parts and whole time series. A few types of autoregressive sub-patterns and transmission patterns drive the oscillations of the financial market. A clustering effect on fluctuations appears in the transmission process, and certain non-major autoregressive sub-patterns have high media capabilities in the financial time series. Different stock indexes exhibit similar characteristics in the transmission of fluctuation information. This work not only proposes a distinctive perspective for analysing financial time series but also provides important information for investors.

Characteristics of the transmission of autoregressive sub-patterns in financial time series

PubMed Central

Gao, Xiangyun; An, Haizhong; Fang, Wei; Huang, Xuan; Li, Huajiao; Zhong, Weiqiong

2014-01-01

There are many types of autoregressive patterns in financial time series, and they form a transmission process. Here, we define autoregressive patterns quantitatively through an econometrical regression model. We present a computational algorithm that sets the autoregressive patterns as nodes and transmissions between patterns as edges, and then converts the transmission process of autoregressive patterns in a time series into a network. We utilised daily Shanghai (securities) composite index time series to study the transmission characteristics of autoregressive patterns. We found statistically significant evidence that the financial market is not random and that there are similar characteristics between parts and whole time series. A few types of autoregressive sub-patterns and transmission patterns drive the oscillations of the financial market. A clustering effect on fluctuations appears in the transmission process, and certain non-major autoregressive sub-patterns have high media capabilities in the financial time series. Different stock indexes exhibit similar characteristics in the transmission of fluctuation information. This work not only proposes a distinctive perspective for analysing financial time series but also provides important information for investors. PMID:25189200

Highly Efficient Compression Algorithms for Multichannel EEG.

PubMed

Shaw, Laxmi; Rahman, Daleef; Routray, Aurobinda

2018-05-01

The difficulty associated with processing and understanding the high dimensionality of electroencephalogram (EEG) data requires developing efficient and robust compression algorithms. In this paper, different lossless compression techniques of single and multichannel EEG data, including Huffman coding, arithmetic coding, Markov predictor, linear predictor, context-based error modeling, multivariate autoregression (MVAR), and a low complexity bivariate model have been examined and their performances have been compared. Furthermore, a high compression algorithm named general MVAR and a modified context-based error modeling for multichannel EEG have been proposed. The resulting compression algorithm produces a higher relative compression ratio of 70.64% on average compared with the existing methods, and in some cases, it goes up to 83.06%. The proposed methods are designed to compress a large amount of multichannel EEG data efficiently so that the data storage and transmission bandwidth can be effectively used. These methods have been validated using several experimental multichannel EEG recordings of different subjects and publicly available standard databases. The satisfactory parametric measures of these methods, namely percent-root-mean square distortion, peak signal-to-noise ratio, root-mean-square error, and cross correlation, show their superiority over the state-of-the-art compression methods.

Evaluating the performance of infectious disease forecasts: A comparison of climate-driven and seasonal dengue forecasts for Mexico.

PubMed

Johansson, Michael A; Reich, Nicholas G; Hota, Aditi; Brownstein, John S; Santillana, Mauricio

2016-09-26

Dengue viruses, which infect millions of people per year worldwide, cause large epidemics that strain healthcare systems. Despite diverse efforts to develop forecasting tools including autoregressive time series, climate-driven statistical, and mechanistic biological models, little work has been done to understand the contribution of different components to improved prediction. We developed a framework to assess and compare dengue forecasts produced from different types of models and evaluated the performance of seasonal autoregressive models with and without climate variables for forecasting dengue incidence in Mexico. Climate data did not significantly improve the predictive power of seasonal autoregressive models. Short-term and seasonal autocorrelation were key to improving short-term and long-term forecasts, respectively. Seasonal autoregressive models captured a substantial amount of dengue variability, but better models are needed to improve dengue forecasting. This framework contributes to the sparse literature of infectious disease prediction model evaluation, using state-of-the-art validation techniques such as out-of-sample testing and comparison to an appropriate reference model.

Evaluating the performance of infectious disease forecasts: A comparison of climate-driven and seasonal dengue forecasts for Mexico

PubMed Central

Johansson, Michael A.; Reich, Nicholas G.; Hota, Aditi; Brownstein, John S.; Santillana, Mauricio

2016-01-01

Dengue viruses, which infect millions of people per year worldwide, cause large epidemics that strain healthcare systems. Despite diverse efforts to develop forecasting tools including autoregressive time series, climate-driven statistical, and mechanistic biological models, little work has been done to understand the contribution of different components to improved prediction. We developed a framework to assess and compare dengue forecasts produced from different types of models and evaluated the performance of seasonal autoregressive models with and without climate variables for forecasting dengue incidence in Mexico. Climate data did not significantly improve the predictive power of seasonal autoregressive models. Short-term and seasonal autocorrelation were key to improving short-term and long-term forecasts, respectively. Seasonal autoregressive models captured a substantial amount of dengue variability, but better models are needed to improve dengue forecasting. This framework contributes to the sparse literature of infectious disease prediction model evaluation, using state-of-the-art validation techniques such as out-of-sample testing and comparison to an appropriate reference model. PMID:27665707

Nonlinear ARMA models for the D(st) index and their physical interpretation

NASA Technical Reports Server (NTRS)

Vassiliadis, D.; Klimas, A. J.; Baker, D. N.

1996-01-01

Time series models successfully reproduce or predict geomagnetic activity indices from solar wind parameters. A method is presented that converts a type of nonlinear filter, the nonlinear Autoregressive Moving Average (ARMA) model to the nonlinear damped oscillator physical model. The oscillator parameters, the growth and decay, the oscillation frequencies and the coupling strength to the input are derived from the filter coefficients. Mathematical methods are derived to obtain unique and consistent filter coefficients while keeping the prediction error low. These methods are applied to an oscillator model for the Dst geomagnetic index driven by the solar wind input. A data set is examined in two ways: the model parameters are calculated as averages over short time intervals, and a nonlinear ARMA model is calculated and the model parameters are derived as a function of the phase space.

One day prediction of nighttime VLF amplitudes using nonlinear autoregression and neural network modeling

NASA Astrophysics Data System (ADS)

Santosa, H.; Hobara, Y.

2017-01-01

The electric field amplitude of very low frequency (VLF) transmitter from Hawaii (NPM) has been continuously recorded at Chofu (CHF), Tokyo, Japan. The VLF amplitude variability indicates lower ionospheric perturbation in the D region (60-90 km altitude range) around the NPM-CHF propagation path. We carried out the prediction of daily nighttime mean VLF amplitude by using Nonlinear Autoregressive with Exogenous Input Neural Network (NARX NN). The NARX NN model, which was built based on the daily input variables of various physical parameters such as stratospheric temperature, total column ozone, cosmic rays, Dst, and Kp indices possess good accuracy during the model building. The fitted model was constructed within the training period from 1 January 2011 to 4 February 2013 by using three algorithms, namely, Bayesian Neural Network (BRANN), Levenberg Marquardt Neural Network (LMANN), and Scaled Conjugate Gradient (SCG). The LMANN has the largest Pearson correlation coefficient (r) of 0.94 and smallest root-mean-square error (RMSE) of 1.19 dB. The constructed models by using LMANN were applied to predict the VLF amplitude from 5 February 2013 to 31 December 2013. As a result the one step (1 day) ahead predicted nighttime VLF amplitude has the r of 0.93 and RMSE of 2.25 dB. We conclude that the model built according to the proposed methodology provides good predictions of the electric field amplitude of VLF waves for NPM-CHF (midlatitude) propagation path.

[Application of multiple seasonal autoregressive integrated moving average model in predicting the mumps incidence].

PubMed

Hui, Shisheng; Chen, Lizhang; Liu, Fuqiang; Ouyang, Yanhao

2015-12-01

To establish multiple seasonal autoregressive integrated moving average model(ARIMA) according to mumps disease incidence in Hunan province, and to predict the mumps incidence from May 2015 to April 2016 in Hunan province by the model. The data were downloaded from "Disease Surveillance Information Reporting Management System" in China Information System for Disease Control and Prevention. The monthly incidence of mumps in Hunan province was collected from January 2004 to April 2015 according to the onset date, including clinical diagnosis and laboratory confirmed cases. The predictive analysis method was the ARIMA model in SPSS 18.0 software, the ARIMA model was established on the monthly incidence of mumps from January 2004 to April 2014, and the date from May 2014 to April 2015 was used as the testing sample, Box-Ljung Q test was used to test the residual of the selected model. Finally, the monthly incidence of mumps from May 2015 to April 2016 was predicted by the model. The peak months of the mumps incidence were May to July every year, and the secondary peak months were November to January of the following year, during January 2004 to April 2014 in Hunan province. After the data sequence was handled by smooth sequence, model identification, establishment and diagnosis, the ARIMA(2,1,1) × (0,1,1)(12) was established, Box-Ljung Q test found, Q=8.40, P=0.868, the residual sequence was white noise, the established model to the data information extraction was complete, the model was reasonable. The R(2) value of the model fitting degree was 0.871, and the value of BIC was -1.646, while the average absolute error of the predicted value and the actual value was 0.025/100 000, the average relative error was 13.004%. The relative error of the model for the prediction of the mumps incidence in Hunan province was small, and the predicting results were reliable. Using the ARIMA(2,1,1) ×(0,1,1)(12) model to predict the mumps incidence from April 2016 to May 2015 in Hunan province, the peak months of the mumps incidence were May to July, and the secondary peak months were November to January of the following year, the incidence of the peak month was close to the same period. The ARIMA(2,1,1)×(0,1,1)(12) model is well fitted the trend of the mumps disease incidence in Hunan province, it has some practical value for the prevention and control of the disease.

Balancing the stochastic description of uncertainties as a function of hydrologic model complexity

NASA Astrophysics Data System (ADS)

Del Giudice, D.; Reichert, P.; Albert, C.; Kalcic, M.; Logsdon Muenich, R.; Scavia, D.; Bosch, N. S.; Michalak, A. M.

2016-12-01

Uncertainty analysis is becoming an important component of forecasting water and pollutant fluxes in urban and rural environments. Properly accounting for errors in the modeling process can help to robustly assess the uncertainties associated with the inputs (e.g. precipitation) and outputs (e.g. runoff) of hydrological models. In recent years we have investigated several Bayesian methods to infer the parameters of a mechanistic hydrological model along with those of the stochastic error component. The latter describes the uncertainties of model outputs and possibly inputs. We have adapted our framework to a variety of applications, ranging from predicting floods in small stormwater systems to nutrient loads in large agricultural watersheds. Given practical constraints, we discuss how in general the number of quantities to infer probabilistically varies inversely with the complexity of the mechanistic model. Most often, when evaluating a hydrological model of intermediate complexity, we can infer the parameters of the model as well as of the output error model. Describing the output errors as a first order autoregressive process can realistically capture the "downstream" effect of inaccurate inputs and structure. With simpler runoff models we can additionally quantify input uncertainty by using a stochastic rainfall process. For complex hydrologic transport models, instead, we show that keeping model parameters fixed and just estimating time-dependent output uncertainties could be a viable option. The common goal across all these applications is to create time-dependent prediction intervals which are both reliable (cover the nominal amount of validation data) and precise (are as narrow as possible). In conclusion, we recommend focusing both on the choice of the hydrological model and of the probabilistic error description. The latter can include output uncertainty only, if the model is computationally-expensive, or, with simpler models, it can separately account for different sources of errors like in the inputs and the structure of the model.

Kepler AutoRegressive Planet Search: Motivation & Methodology

NASA Astrophysics Data System (ADS)

Caceres, Gabriel; Feigelson, Eric; Jogesh Babu, G.; Bahamonde, Natalia; Bertin, Karine; Christen, Alejandra; Curé, Michel; Meza, Cristian

2015-08-01

The Kepler AutoRegressive Planet Search (KARPS) project uses statistical methodology associated with autoregressive (AR) processes to model Kepler lightcurves in order to improve exoplanet transit detection in systems with high stellar variability. We also introduce a planet-search algorithm to detect transits in time-series residuals after application of the AR models. One of the main obstacles in detecting faint planetary transits is the intrinsic stellar variability of the host star. The variability displayed by many stars may have autoregressive properties, wherein later flux values are correlated with previous ones in some manner. Auto-Regressive Moving-Average (ARMA) models, Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH), and related models are flexible, phenomenological methods used with great success to model stochastic temporal behaviors in many fields of study, particularly econometrics. Powerful statistical methods are implemented in the public statistical software environment R and its many packages. Modeling involves maximum likelihood fitting, model selection, and residual analysis. These techniques provide a useful framework to model stellar variability and are used in KARPS with the objective of reducing stellar noise to enhance opportunities to find as-yet-undiscovered planets. Our analysis procedure consisting of three steps: pre-processing of the data to remove discontinuities, gaps and outliers; ARMA-type model selection and fitting; and transit signal search of the residuals using a new Transit Comb Filter (TCF) that replaces traditional box-finding algorithms. We apply the procedures to simulated Kepler-like time series with known stellar and planetary signals to evaluate the effectiveness of the KARPS procedures. The ARMA-type modeling is effective at reducing stellar noise, but also reduces and transforms the transit signal into ingress/egress spikes. A periodogram based on the TCF is constructed to concentrate the signal of these periodic spikes. When a periodic transit is found, the model is displayed on a standard period-folded averaged light curve. We also illustrate the efficient coding in R.

Study on homogenization of synthetic GNSS-retrieved IWV time series and its impact on trend estimates with autoregressive noise

NASA Astrophysics Data System (ADS)

Klos, Anna; Pottiaux, Eric; Van Malderen, Roeland; Bock, Olivier; Bogusz, Janusz

2017-04-01

A synthetic benchmark dataset of Integrated Water Vapour (IWV) was created within the activity of "Data homogenisation" of sub-working group WG3 of COST ES1206 Action. The benchmark dataset was created basing on the analysis of IWV differences retrieved by Global Positioning System (GPS) International GNSS Service (IGS) stations using European Centre for Medium-Range Weather Forecats (ECMWF) reanalysis data (ERA-Interim). Having analysed a set of 120 series of IWV differences (ERAI-GPS) derived for IGS stations, we delivered parameters of a number of gaps and breaks for every certain station. Moreover, we estimated values of trends, significant seasonalities and character of residuals when deterministic model was removed. We tested five different noise models and found that a combination of white and autoregressive processes of first order describes the stochastic part with a good accuracy. Basing on this analysis, we performed Monte Carlo simulations of 25 years long data with two different types of noise: white as well as combination of white and autoregressive processes. We also added few strictly defined offsets, creating three variants of synthetic dataset: easy, less-complicated and fully-complicated. The 'Easy' dataset included seasonal signals (annual, semi-annual, 3 and 4 months if present for a particular station), offsets and white noise. The 'Less-complicated' dataset included above-mentioned, as well as the combination of white and first order autoregressive processes (AR(1)+WH). The 'Fully-complicated' dataset included, beyond above, a trend and gaps. In this research, we show the impact of manual homogenisation on the estimates of trend and its error. We also cross-compare the results for three above-mentioned datasets, as the synthetized noise type might have a significant influence on manual homogenisation. Therefore, it might mostly affect the values of trend and their uncertainties when inappropriately handled. In a future, the synthetic dataset we present is going to be used as a benchmark to test various statistical tools in terms of homogenisation task.

Volatility in GARCH Models of Business Tendency Index

NASA Astrophysics Data System (ADS)

Wahyuni, Dwi A. S.; Wage, Sutarman; Hartono, Ateng

2018-01-01

This paper aims to obtain a model of business tendency index by considering volatility factor. Volatility factor detected by ARCH (Autoregressive Conditional Heteroscedasticity). The ARCH checking was performed using the Lagrange multiplier test. The modeling is Generalized Autoregressive Conditional Heteroscedasticity (GARCH) are able to overcome volatility problems by incorporating past residual elements and residual variants.

Arima model and exponential smoothing method: A comparison

NASA Astrophysics Data System (ADS)

Wan Ahmad, Wan Kamarul Ariffin; Ahmad, Sabri

2013-04-01

This study shows the comparison between Autoregressive Moving Average (ARIMA) model and Exponential Smoothing Method in making a prediction. The comparison is focused on the ability of both methods in making the forecasts with the different number of data sources and the different length of forecasting period. For this purpose, the data from The Price of Crude Palm Oil (RM/tonne), Exchange Rates of Ringgit Malaysia (RM) in comparison to Great Britain Pound (GBP) and also The Price of SMR 20 Rubber Type (cents/kg) with three different time series are used in the comparison process. Then, forecasting accuracy of each model is measured by examinethe prediction error that producedby using Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute deviation (MAD). The study shows that the ARIMA model can produce a better prediction for the long-term forecasting with limited data sources, butcannot produce a better prediction for time series with a narrow range of one point to another as in the time series for Exchange Rates. On the contrary, Exponential Smoothing Method can produce a better forecasting for Exchange Rates that has a narrow range of one point to another for its time series, while itcannot produce a better prediction for a longer forecasting period.

Measurement error, time lag, unmeasured confounding: Considerations for longitudinal estimation of the effect of a mediator in randomised clinical trials.

PubMed

Goldsmith, K A; Chalder, T; White, P D; Sharpe, M; Pickles, A

2018-06-01

Clinical trials are expensive and time-consuming and so should also be used to study how treatments work, allowing for the evaluation of theoretical treatment models and refinement and improvement of treatments. These treatment processes can be studied using mediation analysis. Randomised treatment makes some of the assumptions of mediation models plausible, but the mediator-outcome relationship could remain subject to bias. In addition, mediation is assumed to be a temporally ordered longitudinal process, but estimation in most mediation studies to date has been cross-sectional and unable to explore this assumption. This study used longitudinal structural equation modelling of mediator and outcome measurements from the PACE trial of rehabilitative treatments for chronic fatigue syndrome (ISRCTN 54285094) to address these issues. In particular, autoregressive and simplex models were used to study measurement error in the mediator, different time lags in the mediator-outcome relationship, unmeasured confounding of the mediator and outcome, and the assumption of a constant mediator-outcome relationship over time. Results showed that allowing for measurement error and unmeasured confounding were important. Contemporaneous rather than lagged mediator-outcome effects were more consistent with the data, possibly due to the wide spacing of measurements. Assuming a constant mediator-outcome relationship over time increased precision.

Measurement error, time lag, unmeasured confounding: Considerations for longitudinal estimation of the effect of a mediator in randomised clinical trials

PubMed Central

Goldsmith, KA; Chalder, T; White, PD; Sharpe, M; Pickles, A

2016-01-01

Clinical trials are expensive and time-consuming and so should also be used to study how treatments work, allowing for the evaluation of theoretical treatment models and refinement and improvement of treatments. These treatment processes can be studied using mediation analysis. Randomised treatment makes some of the assumptions of mediation models plausible, but the mediator–outcome relationship could remain subject to bias. In addition, mediation is assumed to be a temporally ordered longitudinal process, but estimation in most mediation studies to date has been cross-sectional and unable to explore this assumption. This study used longitudinal structural equation modelling of mediator and outcome measurements from the PACE trial of rehabilitative treatments for chronic fatigue syndrome (ISRCTN 54285094) to address these issues. In particular, autoregressive and simplex models were used to study measurement error in the mediator, different time lags in the mediator–outcome relationship, unmeasured confounding of the mediator and outcome, and the assumption of a constant mediator–outcome relationship over time. Results showed that allowing for measurement error and unmeasured confounding were important. Contemporaneous rather than lagged mediator–outcome effects were more consistent with the data, possibly due to the wide spacing of measurements. Assuming a constant mediator–outcome relationship over time increased precision. PMID:27647810

Univariate Time Series Prediction of Solar Power Using a Hybrid Wavelet-ARMA-NARX Prediction Method

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nazaripouya, Hamidreza; Wang, Yubo; Chu, Chi-Cheng

This paper proposes a new hybrid method for super short-term solar power prediction. Solar output power usually has a complex, nonstationary, and nonlinear characteristic due to intermittent and time varying behavior of solar radiance. In addition, solar power dynamics is fast and is inertia less. An accurate super short-time prediction is required to compensate for the fluctuations and reduce the impact of solar power penetration on the power system. The objective is to predict one step-ahead solar power generation based only on historical solar power time series data. The proposed method incorporates discrete wavelet transform (DWT), Auto-Regressive Moving Average (ARMA)more » models, and Recurrent Neural Networks (RNN), while the RNN architecture is based on Nonlinear Auto-Regressive models with eXogenous inputs (NARX). The wavelet transform is utilized to decompose the solar power time series into a set of richer-behaved forming series for prediction. ARMA model is employed as a linear predictor while NARX is used as a nonlinear pattern recognition tool to estimate and compensate the error of wavelet-ARMA prediction. The proposed method is applied to the data captured from UCLA solar PV panels and the results are compared with some of the common and most recent solar power prediction methods. The results validate the effectiveness of the proposed approach and show a considerable improvement in the prediction precision.« less

A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series

NASA Astrophysics Data System (ADS)

Wang, Wen-Chuan; Chau, Kwok-Wing; Cheng, Chun-Tian; Qiu, Lin

2009-08-01

SummaryDeveloping a hydrological forecasting model based on past records is crucial to effective hydropower reservoir management and scheduling. Traditionally, time series analysis and modeling is used for building mathematical models to generate hydrologic records in hydrology and water resources. Artificial intelligence (AI), as a branch of computer science, is capable of analyzing long-series and large-scale hydrological data. In recent years, it is one of front issues to apply AI technology to the hydrological forecasting modeling. In this paper, autoregressive moving-average (ARMA) models, artificial neural networks (ANNs) approaches, adaptive neural-based fuzzy inference system (ANFIS) techniques, genetic programming (GP) models and support vector machine (SVM) method are examined using the long-term observations of monthly river flow discharges. The four quantitative standard statistical performance evaluation measures, the coefficient of correlation ( R), Nash-Sutcliffe efficiency coefficient ( E), root mean squared error (RMSE), mean absolute percentage error (MAPE), are employed to evaluate the performances of various models developed. Two case study river sites are also provided to illustrate their respective performances. The results indicate that the best performance can be obtained by ANFIS, GP and SVM, in terms of different evaluation criteria during the training and validation phases.

Time to burn: Modeling wildland arson as an autoregressive crime function

Treesearch

Jeffrey P. Prestemon; David T. Butry

2005-01-01

Six Poisson autoregressive models of order p [PAR(p)] of daily wildland arson ignition counts are estimated for five locations in Florida (1994-2001). In addition, a fixed effects time-series Poisson model of annual arson counts is estimated for all Florida counties (1995-2001). PAR(p) model estimates reveal highly significant arson ignition autocorrelation, lasting up...

Comparison of vector autoregressive (VAR) and vector error correction models (VECM) for index of ASEAN stock price

NASA Astrophysics Data System (ADS)

Suharsono, Agus; Aziza, Auliya; Pramesti, Wara

2017-12-01

Capital markets can be an indicator of the development of a country's economy. The presence of capital markets also encourages investors to trade; therefore investors need information and knowledge of which shares are better. One way of making decisions for short-term investments is the need for modeling to forecast stock prices in the period to come. Issue of stock market-stock integration ASEAN is very important. The problem is that ASEAN does not have much time to implement one market in the economy, so it would be very interesting if there is evidence whether the capital market in the ASEAN region, especially the countries of Indonesia, Malaysia, Philippines, Singapore and Thailand deserve to be integrated or still segmented. Furthermore, it should also be known and proven What kind of integration is happening: what A capital market affects only the market Other capital, or a capital market only Influenced by other capital markets, or a Capital market as well as affecting as well Influenced by other capital markets in one ASEAN region. In this study, it will compare forecasting of Indonesian share price (IHSG) with neighboring countries (ASEAN) including developed and developing countries such as Malaysia (KLSE), Singapore (SGE), Thailand (SETI), Philippines (PSE) to find out which stock country the most superior and influential. These countries are the founders of ASEAN and share price index owners who have close relations with Indonesia in terms of trade, especially exports and imports. Stock price modeling in this research is using multivariate time series analysis that is VAR (Vector Autoregressive) and VECM (Vector Error Correction Modeling). VAR and VECM models not only predict more than one variable but also can see the interrelations between variables with each other. If the assumption of white noise is not met in the VAR modeling, then the cause can be assumed that there is an outlier. With this modeling will be able to know the pattern of relationship or linkage of share prices of each country in ASEAN. The best modeling comparison result of the ASEAN stock price index is VAR.

Spatial Autocorrelation And Autoregressive Models In Ecology

Treesearch

Jeremy W. Lichstein; Theodore R. Simons; Susan A. Shriner; Kathleen E. Franzreb

2003-01-01

Abstract. Recognition and analysis of spatial autocorrelation has defined a new paradigm in ecology. Attention to spatial pattern can lead to insights that would have been otherwise overlooked, while ignoring space may lead to false conclusions about ecological relationships. We used Gaussian spatial autoregressive models, fit with widely available...

A new approach to modeling temperature-related mortality: Non-linear autoregressive models with exogenous input.

PubMed

Lee, Cameron C; Sheridan, Scott C

2018-07-01

Temperature-mortality relationships are nonlinear, time-lagged, and can vary depending on the time of year and geographic location, all of which limits the applicability of simple regression models in describing these associations. This research demonstrates the utility of an alternative method for modeling such complex relationships that has gained recent traction in other environmental fields: nonlinear autoregressive models with exogenous input (NARX models). All-cause mortality data and multiple temperature-based data sets were gathered from 41 different US cities, for the period 1975-2010, and subjected to ensemble NARX modeling. Models generally performed better in larger cities and during the winter season. Across the US, median absolute percentage errors were 10% (ranging from 4% to 15% in various cities), the average improvement in the r-squared over that of a simple persistence model was 17% (6-24%), and the hit rate for modeling spike days in mortality (>80th percentile) was 54% (34-71%). Mortality responded acutely to hot summer days, peaking at 0-2 days of lag before dropping precipitously, and there was an extended mortality response to cold winter days, peaking at 2-4 days of lag and dropping slowly and continuing for multiple weeks. Spring and autumn showed both of the aforementioned temperature-mortality relationships, but generally to a lesser magnitude than what was seen in summer or winter. When compared to distributed lag nonlinear models, NARX model output was nearly identical. These results highlight the applicability of NARX models for use in modeling complex and time-dependent relationships for various applications in epidemiology and environmental sciences. Copyright © 2018 Elsevier Inc. All rights reserved.

Interest rate next-day variation prediction based on hybrid feedforward neural network, particle swarm optimization, and multiresolution techniques

NASA Astrophysics Data System (ADS)

Lahmiri, Salim

2016-02-01

Multiresolution analysis techniques including continuous wavelet transform, empirical mode decomposition, and variational mode decomposition are tested in the context of interest rate next-day variation prediction. In particular, multiresolution analysis techniques are used to decompose interest rate actual variation and feedforward neural network for training and prediction. Particle swarm optimization technique is adopted to optimize its initial weights. For comparison purpose, autoregressive moving average model, random walk process and the naive model are used as main reference models. In order to show the feasibility of the presented hybrid models that combine multiresolution analysis techniques and feedforward neural network optimized by particle swarm optimization, we used a set of six illustrative interest rates; including Moody's seasoned Aaa corporate bond yield, Moody's seasoned Baa corporate bond yield, 3-Month, 6-Month and 1-Year treasury bills, and effective federal fund rate. The forecasting results show that all multiresolution-based prediction systems outperform the conventional reference models on the criteria of mean absolute error, mean absolute deviation, and root mean-squared error. Therefore, it is advantageous to adopt hybrid multiresolution techniques and soft computing models to forecast interest rate daily variations as they provide good forecasting performance.

Parametric system identification of catamaran for improving controller design

NASA Astrophysics Data System (ADS)

Timpitak, Surasak; Prempraneerach, Pradya; Pengwang, Eakkachai

2018-01-01

This paper presents an estimation of simplified dynamic model for only surge- and yaw- motions of catamaran by using system identification (SI) techniques to determine associated unknown parameters. These methods will enhance the performance of designing processes for the motion control system of Unmanned Surface Vehicle (USV). The simulation results demonstrate an effective way to solve for damping forces and to determine added masses by applying least-square and AutoRegressive Exogenous (ARX) methods. Both methods are then evaluated according to estimated parametric errors from the vehicle’s dynamic model. The ARX method, which yields better estimated accuracy, can then be applied to identify unknown parameters as well as to help improving a controller design of a real unmanned catamaran.

Seabed roughness parameters from joint backscatter and reflection inversion at the Malta Plateau.

PubMed

Steininger, Gavin; Holland, Charles W; Dosso, Stan E; Dettmer, Jan

2013-09-01

This paper presents estimates of seabed roughness and geoacoustic parameters and uncertainties on the Malta Plateau, Mediterranean Sea, by joint Bayesian inversion of mono-static backscatter and spherical wave reflection-coefficient data. The data are modeled using homogeneous fluid sediment layers overlying an elastic basement. The scattering model assumes a randomly rough water-sediment interface with a von Karman roughness power spectrum. Scattering and reflection data are inverted simultaneously using a population of interacting Markov chains to sample roughness and geoacoustic parameters as well as residual error parameters. Trans-dimensional sampling is applied to treat the number of sediment layers and the order (zeroth or first) of an autoregressive error model (to represent potential residual correlation) as unknowns. Results are considered in terms of marginal posterior probability profiles and distributions, which quantify the effective data information content to resolve scattering/geoacoustic structure. Results indicate well-defined scattering (roughness) parameters in good agreement with existing measurements, and a multi-layer sediment profile over a high-speed (elastic) basement, consistent with independent knowledge of sand layers over limestone.

Medium- and Long-term Prediction of LOD Change with the Leap-step Autoregressive Model

NASA Astrophysics Data System (ADS)

Liu, Q. B.; Wang, Q. J.; Lei, M. F.

2015-09-01

It is known that the accuracies of medium- and long-term prediction of changes of length of day (LOD) based on the combined least-square and autoregressive (LS+AR) decrease gradually. The leap-step autoregressive (LSAR) model is more accurate and stable in medium- and long-term prediction, therefore it is used to forecast the LOD changes in this work. Then the LOD series from EOP 08 C04 provided by IERS (International Earth Rotation and Reference Systems Service) is used to compare the effectiveness of the LSAR and traditional AR methods. The predicted series resulted from the two models show that the prediction accuracy with the LSAR model is better than that from AR model in medium- and long-term prediction.

Mathematical model with autoregressive process for electrocardiogram signals

NASA Astrophysics Data System (ADS)

Evaristo, Ronaldo M.; Batista, Antonio M.; Viana, Ricardo L.; Iarosz, Kelly C.; Szezech, José D., Jr.; Godoy, Moacir F. de

2018-04-01

The cardiovascular system is composed of the heart, blood and blood vessels. Regarding the heart, cardiac conditions are determined by the electrocardiogram, that is a noninvasive medical procedure. In this work, we propose autoregressive process in a mathematical model based on coupled differential equations in order to obtain the tachograms and the electrocardiogram signals of young adults with normal heartbeats. Our results are compared with experimental tachogram by means of Poincaré plot and dentrended fluctuation analysis. We verify that the results from the model with autoregressive process show good agreement with experimental measures from tachogram generated by electrical activity of the heartbeat. With the tachogram we build the electrocardiogram by means of coupled differential equations.

Passenger Flow Forecasting Research for Airport Terminal Based on SARIMA Time Series Model

NASA Astrophysics Data System (ADS)

Li, Ziyu; Bi, Jun; Li, Zhiyin

2017-12-01

Based on the data of practical operating of Kunming Changshui International Airport during2016, this paper proposes Seasonal Autoregressive Integrated Moving Average (SARIMA) model to predict the passenger flow. This article not only considers the non-stationary and autocorrelation of the sequence, but also considers the daily periodicity of the sequence. The prediction results can accurately describe the change trend of airport passenger flow and provide scientific decision support for the optimal allocation of airport resources and optimization of departure process. The result shows that this model is applicable to the short-term prediction of airport terminal departure passenger traffic and the average error ranges from 1% to 3%. The difference between the predicted and the true values of passenger traffic flow is quite small, which indicates that the model has fairly good passenger traffic flow prediction ability.

Functional MRI and Multivariate Autoregressive Models

PubMed Central

Rogers, Baxter P.; Katwal, Santosh B.; Morgan, Victoria L.; Asplund, Christopher L.; Gore, John C.

2010-01-01

Connectivity refers to the relationships that exist between different regions of the brain. In the context of functional magnetic resonance imaging (fMRI), it implies a quantifiable relationship between hemodynamic signals from different regions. One aspect of this relationship is the existence of small timing differences in the signals in different regions. Delays of 100 ms or less may be measured with fMRI, and these may reflect important aspects of the manner in which brain circuits respond as well as the overall functional organization of the brain. The multivariate autoregressive time series model has features to recommend it for measuring these delays, and is straightforward to apply to hemodynamic data. In this review, we describe the current usage of the multivariate autoregressive model for fMRI, discuss the issues that arise when it is applied to hemodynamic time series, and consider several extensions. Connectivity measures like Granger causality that are based on the autoregressive model do not always reflect true neuronal connectivity; however, we conclude that careful experimental design could make this methodology quite useful in extending the information obtainable using fMRI. PMID:20444566

Kumaraswamy autoregressive moving average models for double bounded environmental data

NASA Astrophysics Data System (ADS)

Bayer, Fábio Mariano; Bayer, Débora Missio; Pumi, Guilherme

2017-12-01

In this paper we introduce the Kumaraswamy autoregressive moving average models (KARMA), which is a dynamic class of models for time series taking values in the double bounded interval (a,b) following the Kumaraswamy distribution. The Kumaraswamy family of distribution is widely applied in many areas, especially hydrology and related fields. Classical examples are time series representing rates and proportions observed over time. In the proposed KARMA model, the median is modeled by a dynamic structure containing autoregressive and moving average terms, time-varying regressors, unknown parameters and a link function. We introduce the new class of models and discuss conditional maximum likelihood estimation, hypothesis testing inference, diagnostic analysis and forecasting. In particular, we provide closed-form expressions for the conditional score vector and conditional Fisher information matrix. An application to environmental real data is presented and discussed.

Forecasting typhoid fever incidence in the Cordillera administrative region in the Philippines using seasonal ARIMA models

NASA Astrophysics Data System (ADS)

Cawiding, Olive R.; Natividad, Gina May R.; Bato, Crisostomo V.; Addawe, Rizavel C.

2017-11-01

The prevalence of typhoid fever in developing countries such as the Philippines calls for a need for accurate forecasting of the disease. This will be of great assistance in strategic disease prevention. This paper presents a development of useful models that predict the behavior of typhoid fever incidence based on the monthly incidence in the provinces of the Cordillera Administrative Region from 2010 to 2015 using univariate time series analysis. The data used was obtained from the Cordillera Office of the Department of Health (DOH-CAR). Seasonal autoregressive moving average (SARIMA) models were used to incorporate the seasonality of the data. A comparison of the results of the obtained models revealed that the SARIMA (1,1,7)(0,0,1)12 with a fixed coefficient at the seventh lag produces the smallest root mean square error (RMSE), mean absolute error (MAE), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). The model suggested that for the year 2016, the number of cases would increase from the months of July to September and have a drop in December. This was then validated using the data collected from January 2016 to December 2016.

Estimation des paramètres d'un modèle hydrologique mixte appliqué à la région du haut plateau Bolivien

NASA Astrophysics Data System (ADS)

Gárfias, Jaime; Verrette, Jean-Louis; Antigüedad, Iñaki; André, Cécile

1996-03-01

This paper discusses the development and application of a technique which permits the analysis and improvement of hydrological models for the management of water resources of complex systems. Considering that such models are intended for practical application, the model was applied to the conditions of the Bolivian highlands. The model consisted of a deterministic part (HEC-1 model) linked to a stochastic component. The experience acquired indicated the possibility of adapting a more general procedure to compensate for the lack of rigour in the homoscedastic and independence hypothesis of the residuals. Use of this concept improved the estimation accuracy of the parameters and provided independent residuals with constant variance. A Box-Cox transformation was used to stabilize error variance and an autoregressive model was used to remove autocorrelation in the residuals.

The log-periodic-AR(1)-GARCH(1,1) model for financial crashes

NASA Astrophysics Data System (ADS)

Gazola, L.; Fernandes, C.; Pizzinga, A.; Riera, R.

2008-02-01

This paper intends to meet recent claims for the attainment of more rigorous statistical methodology within the econophysics literature. To this end, we consider an econometric approach to investigate the outcomes of the log-periodic model of price movements, which has been largely used to forecast financial crashes. In order to accomplish reliable statistical inference for unknown parameters, we incorporate an autoregressive dynamic and a conditional heteroskedasticity structure in the error term of the original model, yielding the log-periodic-AR(1)-GARCH(1,1) model. Both the original and the extended models are fitted to financial indices of U. S. market, namely S&P500 and NASDAQ. Our analysis reveal two main points: (i) the log-periodic-AR(1)-GARCH(1,1) model has residuals with better statistical properties and (ii) the estimation of the parameter concerning the time of the financial crash has been improved.

Linear models of coregionalization for multivariate lattice data: Order-dependent and order-free cMCARs.

PubMed

MacNab, Ying C

2016-08-01

This paper concerns with multivariate conditional autoregressive models defined by linear combination of independent or correlated underlying spatial processes. Known as linear models of coregionalization, the method offers a systematic and unified approach for formulating multivariate extensions to a broad range of univariate conditional autoregressive models. The resulting multivariate spatial models represent classes of coregionalized multivariate conditional autoregressive models that enable flexible modelling of multivariate spatial interactions, yielding coregionalization models with symmetric or asymmetric cross-covariances of different spatial variation and smoothness. In the context of multivariate disease mapping, for example, they facilitate borrowing strength both over space and cross variables, allowing for more flexible multivariate spatial smoothing. Specifically, we present a broadened coregionalization framework to include order-dependent, order-free, and order-robust multivariate models; a new class of order-free coregionalized multivariate conditional autoregressives is introduced. We tackle computational challenges and present solutions that are integral for Bayesian analysis of these models. We also discuss two ways of computing deviance information criterion for comparison among competing hierarchical models with or without unidentifiable prior parameters. The models and related methodology are developed in the broad context of modelling multivariate data on spatial lattice and illustrated in the context of multivariate disease mapping. The coregionalization framework and related methods also present a general approach for building spatially structured cross-covariance functions for multivariate geostatistics. © The Author(s) 2016.

A back-fitting algorithm to improve real-time flood forecasting

NASA Astrophysics Data System (ADS)

Zhang, Xiaojing; Liu, Pan; Cheng, Lei; Liu, Zhangjun; Zhao, Yan

2018-07-01

Real-time flood forecasting is important for decision-making with regards to flood control and disaster reduction. The conventional approach involves a postprocessor calibration strategy that first calibrates the hydrological model and then estimates errors. This procedure can simulate streamflow consistent with observations, but obtained parameters are not optimal. Joint calibration strategies address this issue by refining hydrological model parameters jointly with the autoregressive (AR) model. In this study, five alternative schemes are used to forecast floods. Scheme I uses only the hydrological model, while scheme II includes an AR model for error correction. In scheme III, differencing is used to remove non-stationarity in the error series. A joint inference strategy employed in scheme IV calibrates the hydrological and AR models simultaneously. The back-fitting algorithm, a basic approach for training an additive model, is adopted in scheme V to alternately recalibrate hydrological and AR model parameters. The performance of the five schemes is compared with a case study of 15 recorded flood events from China's Baiyunshan reservoir basin. Our results show that (1) schemes IV and V outperform scheme III during the calibration and validation periods and (2) scheme V is inferior to scheme IV in the calibration period, but provides better results in the validation period. Joint calibration strategies can therefore improve the accuracy of flood forecasting. Additionally, the back-fitting recalibration strategy produces weaker overcorrection and a more robust performance compared with the joint inference strategy.

Forecasting Instability Indicators in the Horn of Africa

DTIC Science & Technology

2008-03-01

further than 2 (Makridakis, et al, 1983, 359). 2-32 Autoregressive Integrated Moving Average ( ARIMA ) Model . Similar to the ARMA model except for...stationary process. ARIMA models are described as ARIMA (p,d,q), where p is the order of the autoregressive process, d is the degree of the...differential process, and q is the order of the moving average process. The ARMA (1,1) model shown above is equivalent to an ARIMA (1,0,1) model . An ARIMA

Multispectral code excited linear prediction coding and its application in magnetic resonance images.

PubMed

Hu, J H; Wang, Y; Cahill, P T

1997-01-01

This paper reports a multispectral code excited linear prediction (MCELP) method for the compression of multispectral images. Different linear prediction models and adaptation schemes have been compared. The method that uses a forward adaptive autoregressive (AR) model has been proven to achieve a good compromise between performance, complexity, and robustness. This approach is referred to as the MFCELP method. Given a set of multispectral images, the linear predictive coefficients are updated over nonoverlapping three-dimensional (3-D) macroblocks. Each macroblock is further divided into several 3-D micro-blocks, and the best excitation signal for each microblock is determined through an analysis-by-synthesis procedure. The MFCELP method has been applied to multispectral magnetic resonance (MR) images. To satisfy the high quality requirement for medical images, the error between the original image set and the synthesized one is further specified using a vector quantizer. This method has been applied to images from 26 clinical MR neuro studies (20 slices/study, three spectral bands/slice, 256x256 pixels/band, 12 b/pixel). The MFCELP method provides a significant visual improvement over the discrete cosine transform (DCT) based Joint Photographers Expert Group (JPEG) method, the wavelet transform based embedded zero-tree wavelet (EZW) coding method, and the vector tree (VT) coding method, as well as the multispectral segmented autoregressive moving average (MSARMA) method we developed previously.

Applying the LANL Statistical Pattern Recognition Paradigm for Structural Health Monitoring to Data from a Surface-Effect Fast Patrol Boat

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hoon Sohn; Charles Farrar; Norman Hunter

2001-01-01

This report summarizes the analysis of fiber-optic strain gauge data obtained from a surface-effect fast patrol boat being studied by the staff at the Norwegian Defense Research Establishment (NDRE) in Norway and the Naval Research Laboratory (NRL) in Washington D.C. Data from two different structural conditions were provided to the staff at Los Alamos National Laboratory. The problem was then approached from a statistical pattern recognition paradigm. This paradigm can be described as a four-part process: (1) operational evaluation, (2) data acquisition & cleansing, (3) feature extraction and data reduction, and (4) statistical model development for feature discrimination. Given thatmore » the first two portions of this paradigm were mostly completed by the NDRE and NRL staff, this study focused on data normalization, feature extraction, and statistical modeling for feature discrimination. The feature extraction process began by looking at relatively simple statistics of the signals and progressed to using the residual errors from auto-regressive (AR) models fit to the measured data as the damage-sensitive features. Data normalization proved to be the most challenging portion of this investigation. A novel approach to data normalization, where the residual errors in the AR model are considered to be an unmeasured input and an auto-regressive model with exogenous inputs (ARX) is then fit to portions of the data exhibiting similar waveforms, was successfully applied to this problem. With this normalization procedure, a clear distinction between the two different structural conditions was obtained. A false-positive study was also run, and the procedure developed herein did not yield any false-positive indications of damage. Finally, the results must be qualified by the fact that this procedure has only been applied to very limited data samples. A more complete analysis of additional data taken under various operational and environmental conditions as well as other structural conditions is necessary before one can definitively state that the procedure is robust enough to be used in practice.« less

Sea-Level Trend Uncertainty With Pacific Climatic Variability and Temporally-Correlated Noise

NASA Astrophysics Data System (ADS)

Royston, Sam; Watson, Christopher S.; Legrésy, Benoît; King, Matt A.; Church, John A.; Bos, Machiel S.

2018-03-01

Recent studies have identified climatic drivers of the east-west see-saw of Pacific Ocean satellite altimetry era sea level trends and a number of sea-level trend and acceleration assessments attempt to account for this. We investigate the effect of Pacific climate variability, together with temporally-correlated noise, on linear trend error estimates and determine new time-of-emergence (ToE) estimates across the Indian and Pacific Oceans. Sea-level trend studies often advocate the use of auto-regressive (AR) noise models to adequately assess formal uncertainties, yet sea level often exhibits colored but non-AR(1) noise. Standard error estimates are over- or under-estimated by an AR(1) model for much of the Indo-Pacific sea level. Allowing for PDO and ENSO variability in the trend estimate only reduces standard errors across the tropics and we find noise characteristics are largely unaffected. Of importance for trend and acceleration detection studies, formal error estimates remain on average up to 1.6 times those from an AR(1) model for long-duration tide gauge data. There is an even chance that the observed trend from the satellite altimetry era exceeds the noise in patches of the tropical Pacific and Indian Oceans and the south-west and north-east Pacific gyres. By including climate indices in the trend analysis, the time it takes for the observed linear sea-level trend to emerge from the noise reduces by up to 2 decades.

An application of seasonal ARIMA models on group commodities to forecast Philippine merchandise exports performance

NASA Astrophysics Data System (ADS)

Natividad, Gina May R.; Cawiding, Olive R.; Addawe, Rizavel C.

2017-11-01

The increase in the merchandise exports of the country offers information about the Philippines' trading role within the global economy. Merchandise exports statistics are used to monitor the country's overall production that is consumed overseas. This paper investigates the comparison between two models obtained by a) clustering the commodity groups into two based on its proportional contribution to the total exports, and b) treating only the total exports. Different seasonal autoregressive integrated moving average (SARIMA) models were then developed for the clustered commodities and for the total exports based on the monthly merchandise exports of the Philippines from 2011 to 2016. The data set used in this study was retrieved from the Philippine Statistics Authority (PSA) which is the central statistical authority in the country responsible for primary data collection. A test for significance of the difference between means at 0.05 level of significance was then performed on the forecasts produced. The result indicates that there is a significant difference between the mean of the forecasts of the two models. Moreover, upon a comparison of the root mean square error (RMSE) and mean absolute error (MAE) of the models, it was found that the models used for the clustered groups outperform the model for the total exports.

The Performance of Multilevel Growth Curve Models under an Autoregressive Moving Average Process

ERIC Educational Resources Information Center

Murphy, Daniel L.; Pituch, Keenan A.

2009-01-01

The authors examined the robustness of multilevel linear growth curve modeling to misspecification of an autoregressive moving average process. As previous research has shown (J. Ferron, R. Dailey, & Q. Yi, 2002; O. Kwok, S. G. West, & S. B. Green, 2007; S. Sivo, X. Fan, & L. Witta, 2005), estimates of the fixed effects were unbiased, and Type I…

Testing the Causal Links between School Climate, School Violence, and School Academic Performance: A Cross-Lagged Panel Autoregressive Model

ERIC Educational Resources Information Center

Benbenishty, Rami; Astor, Ron Avi; Roziner, Ilan; Wrabel, Stephani L.

2016-01-01

The present study explores the causal link between school climate, school violence, and a school's general academic performance over time using a school-level, cross-lagged panel autoregressive modeling design. We hypothesized that reductions in school violence and climate improvement would lead to schools' overall improved academic performance.…

Processing on weak electric signals by the autoregressive model

NASA Astrophysics Data System (ADS)

Ding, Jinli; Zhao, Jiayin; Wang, Lanzhou; Li, Qiao

2008-10-01

A model of the autoregressive model of weak electric signals in two plants was set up for the first time. The result of the AR model to forecast 10 values of the weak electric signals is well. It will construct a standard set of the AR model coefficient of the plant electric signal and the environmental factor, and can be used as the preferences for the intelligent autocontrol system based on the adaptive characteristic of plants to achieve the energy saving on agricultural productions.

Signal Analysis Algorithms for Optimized Fitting of Nonresonant Laser Induced Thermal Acoustics Damped Sinusoids

NASA Technical Reports Server (NTRS)

Balla, R. Jeffrey; Miller, Corey A.

2008-01-01

This study seeks a numerical algorithm which optimizes frequency precision for the damped sinusoids generated by the nonresonant LITA technique. It compares computed frequencies, frequency errors, and fit errors obtained using five primary signal analysis methods. Using variations on different algorithms within each primary method, results from 73 fits are presented. Best results are obtained using an AutoRegressive method. Compared to previous results using Prony s method, single shot waveform frequencies are reduced approx.0.4% and frequency errors are reduced by a factor of approx.20 at 303K to approx. 0.1%. We explore the advantages of high waveform sample rates and potential for measurements in low density gases.

A hybrid least squares support vector machines and GMDH approach for river flow forecasting

NASA Astrophysics Data System (ADS)

Samsudin, R.; Saad, P.; Shabri, A.

2010-06-01

This paper proposes a novel hybrid forecasting model, which combines the group method of data handling (GMDH) and the least squares support vector machine (LSSVM), known as GLSSVM. The GMDH is used to determine the useful input variables for LSSVM model and the LSSVM model which works as time series forecasting. In this study the application of GLSSVM for monthly river flow forecasting of Selangor and Bernam River are investigated. The results of the proposed GLSSVM approach are compared with the conventional artificial neural network (ANN) models, Autoregressive Integrated Moving Average (ARIMA) model, GMDH and LSSVM models using the long term observations of monthly river flow discharge. The standard statistical, the root mean square error (RMSE) and coefficient of correlation (R) are employed to evaluate the performance of various models developed. Experiment result indicates that the hybrid model was powerful tools to model discharge time series and can be applied successfully in complex hydrological modeling.

Time series models on analysing mortality rates and acute childhood lymphoid leukaemia.

PubMed

Kis, Maria

2005-01-01

In this paper we demonstrate applying time series models on medical research. The Hungarian mortality rates were analysed by autoregressive integrated moving average models and seasonal time series models examined the data of acute childhood lymphoid leukaemia.The mortality data may be analysed by time series methods such as autoregressive integrated moving average (ARIMA) modelling. This method is demonstrated by two examples: analysis of the mortality rates of ischemic heart diseases and analysis of the mortality rates of cancer of digestive system. Mathematical expressions are given for the results of analysis. The relationships between time series of mortality rates were studied with ARIMA models. Calculations of confidence intervals for autoregressive parameters by tree methods: standard normal distribution as estimation and estimation of the White's theory and the continuous time case estimation. Analysing the confidence intervals of the first order autoregressive parameters we may conclude that the confidence intervals were much smaller than other estimations by applying the continuous time estimation model.We present a new approach to analysing the occurrence of acute childhood lymphoid leukaemia. We decompose time series into components. The periodicity of acute childhood lymphoid leukaemia in Hungary was examined using seasonal decomposition time series method. The cyclic trend of the dates of diagnosis revealed that a higher percent of the peaks fell within the winter months than in the other seasons. This proves the seasonal occurrence of the childhood leukaemia in Hungary.

Medium- and Long-term Prediction of LOD Change by the Leap-step Autoregressive Model

NASA Astrophysics Data System (ADS)

Wang, Qijie

2015-08-01

The accuracy of medium- and long-term prediction of length of day (LOD) change base on combined least-square and autoregressive (LS+AR) deteriorates gradually. Leap-step autoregressive (LSAR) model can significantly reduce the edge effect of the observation sequence. Especially, LSAR model greatly improves the resolution of signals’ low-frequency components. Therefore, it can improve the efficiency of prediction. In this work, LSAR is used to forecast the LOD change. The LOD series from EOP 08 C04 provided by IERS is modeled by both the LSAR and AR models. The results of the two models are analyzed and compared. When the prediction length is between 10-30 days, the accuracy improvement is less than 10%. When the prediction length amounts to above 30 day, the accuracy improved obviously, with the maximum being around 19%. The results show that the LSAR model has higher prediction accuracy and stability in medium- and long-term prediction.

Economic growth and CO2 emissions: an investigation with smooth transition autoregressive distributed lag models for the 1800-2014 period in the USA.

PubMed

Bildirici, Melike; Ersin, Özgür Ömer

2018-01-01

The study aims to combine the autoregressive distributed lag (ARDL) cointegration framework with smooth transition autoregressive (STAR)-type nonlinear econometric models for causal inference. Further, the proposed STAR distributed lag (STARDL) models offer new insights in terms of modeling nonlinearity in the long- and short-run relations between analyzed variables. The STARDL method allows modeling and testing nonlinearity in the short-run and long-run parameters or both in the short- and long-run relations. To this aim, the relation between CO 2 emissions and economic growth rates in the USA is investigated for the 1800-2014 period, which is one of the largest data sets available. The proposed hybrid models are the logistic, exponential, and second-order logistic smooth transition autoregressive distributed lag (LSTARDL, ESTARDL, and LSTAR2DL) models combine the STAR framework with nonlinear ARDL-type cointegration to augment the linear ARDL approach with smooth transitional nonlinearity. The proposed models provide a new approach to the relevant econometrics and environmental economics literature. Our results indicated the presence of asymmetric long-run and short-run relations between the analyzed variables that are from the GDP towards CO 2 emissions. By the use of newly proposed STARDL models, the results are in favor of important differences in terms of the response of CO 2 emissions in regimes 1 and 2 for the estimated LSTAR2DL and LSTARDL models.

Selecting a Separable Parametric Spatiotemporal Covariance Structure for Longitudinal Imaging Data

PubMed Central

George, Brandon; Aban, Inmaculada

2014-01-01

Longitudinal imaging studies allow great insight into how the structure and function of a subject’s internal anatomy changes over time. Unfortunately, the analysis of longitudinal imaging data is complicated by inherent spatial and temporal correlation: the temporal from the repeated measures, and the spatial from the outcomes of interest being observed at multiple points in a patients body. We propose the use of a linear model with a separable parametric spatiotemporal error structure for the analysis of repeated imaging data. The model makes use of spatial (exponential, spherical, and Matérn) and temporal (compound symmetric, autoregressive-1, Toeplitz, and unstructured) parametric correlation functions. A simulation study, inspired by a longitudinal cardiac imaging study on mitral regurgitation patients, compared different information criteria for selecting a particular separable parametric spatiotemporal correlation structure as well as the effects on Type I and II error rates for inference on fixed effects when the specified model is incorrect. Information criteria were found to be highly accurate at choosing between separable parametric spatiotemporal correlation structures. Misspecification of the covariance structure was found to have the ability to inflate the Type I error or have an overly conservative test size, which corresponded to decreased power. An example with clinical data is given illustrating how the covariance structure procedure can be done in practice, as well as how covariance structure choice can change inferences about fixed effects. PMID:25293361

Comparative Time Series Analysis of Aerosol Optical Depth over Sites in United States and China Using ARIMA Modeling

NASA Astrophysics Data System (ADS)

Li, X.; Zhang, C.; Li, W.

2017-12-01

Long-term spatiotemporal analysis and modeling of aerosol optical depth (AOD) distribution is of paramount importance to study radiative forcing, climate change, and human health. This study is focused on the trends and variations of AOD over six stations located in United States and China during 2003 to 2015, using satellite-retrieved Moderate Resolution Imaging Spectrometer (MODIS) Collection 6 retrievals and ground measurements derived from Aerosol Robotic NETwork (AERONET). An autoregressive integrated moving average (ARIMA) model is applied to simulate and predict AOD values. The R2, adjusted R2, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Bayesian Information Criterion (BIC) are used as indices to select the best fitted model. Results show that there is a persistent decreasing trend in AOD for both MODIS data and AERONET data over three stations. Monthly and seasonal AOD variations reveal consistent aerosol patterns over stations along mid-latitudes. Regional differences impacted by climatology and land cover types are observed for the selected stations. Statistical validation of time series models indicates that the non-seasonal ARIMA model performs better for AERONET AOD data than for MODIS AOD data over most stations, suggesting the method works better for data with higher quality. By contrast, the seasonal ARIMA model reproduces the seasonal variations of MODIS AOD data much more precisely. Overall, the reasonably predicted results indicate the applicability and feasibility of the stochastic ARIMA modeling technique to forecast future and missing AOD values.

State space model approach for forecasting the use of electrical energy (a case study on: PT. PLN (Persero) district of Kroya)

NASA Astrophysics Data System (ADS)

Kurniati, Devi; Hoyyi, Abdul; Widiharih, Tatik

2018-05-01

Time series data is a series of data taken or measured based on observations at the same time interval. Time series data analysis is used to perform data analysis considering the effect of time. The purpose of time series analysis is to know the characteristics and patterns of a data and predict a data value in some future period based on data in the past. One of the forecasting methods used for time series data is the state space model. This study discusses the modeling and forecasting of electric energy consumption using the state space model for univariate data. The modeling stage is began with optimal Autoregressive (AR) order selection, determination of state vector through canonical correlation analysis, estimation of parameter, and forecasting. The result of this research shows that modeling of electric energy consumption using state space model of order 4 with Mean Absolute Percentage Error (MAPE) value 3.655%, so the model is very good forecasting category.

Questioning the sustainable palm oil demand: case study from French-Indonesia supply chain

NASA Astrophysics Data System (ADS)

Chalil, D.; Barus, R.

2018-02-01

Sustainable palm oil has been widely debated. Consuming countries insist certified sustainable produces palm oil, but in fact the absorption of the certified palm oil is still less than 60%. This raise questions about the sustainable palm oil demand. In this study, such a condition will be analysed in French-Indonesia supply chain case. Using monthly and quarterly data from 2010 to 2016 with Autoregressive Distributed Lag (ARDL) approach and Error Correction Model, demand influencing factors and price integration in each market of the supply chain is estimated. Two scenarios namely re-export and direct export models are considered in the Error Correction Model. The results show that France Gross Domestic Product, prices of France palm oil import from Indonesia, Malaysia, and Germany, and price of France groundnut import significantly influence the France palm oil import volume from Indonesia. Prices in each market along palm oil re-export France-Indonesia supply chain are co-integrated and converge towards long-run equilibrium, but not in the direct export supply chain. This leads to a conclusion that France market preferences in specific and EU market preferences in general need to be considered by Indonesian palm oil decision makers.

Equilibrium Policy Proposals with Abstentions.

DTIC Science & Technology

1981-05-01

David M. Kreps. 262. ’Autoregressive Modelling and Money Income (ajusality Detection." by (heng lisiao. 263. "Measurement IError in a Dynamiic...34Autoregressive Modeling of"Canadian Money and Income Data," by Cheng Ilsjao. 277. "We Can’t Disagree IForever," by John 1). Geanakoplos and Heraklis...34*Optimal & Voluntary Income Distribution," by K. J. Arrow. 289. "’Asymptotic Values mif Mixed Gaime,.," by Abraham Neymnan. 290. "Tinie Series Modelling

Time-varying coefficient vector autoregressions model based on dynamic correlation with an application to crude oil and stock markets

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lu, Fengbin, E-mail: fblu@amss.ac.cn

This paper proposes a new time-varying coefficient vector autoregressions (VAR) model, in which the coefficient is a linear function of dynamic lagged correlation. The proposed model allows for flexibility in choices of dynamic correlation models (e.g. dynamic conditional correlation generalized autoregressive conditional heteroskedasticity (GARCH) models, Markov-switching GARCH models and multivariate stochastic volatility models), which indicates that it can describe many types of time-varying causal effects. Time-varying causal relations between West Texas Intermediate (WTI) crude oil and the US Standard and Poor’s 500 (S&P 500) stock markets are examined by the proposed model. The empirical results show that their causal relationsmore » evolve with time and display complex characters. Both positive and negative causal effects of the WTI on the S&P 500 in the subperiods have been found and confirmed by the traditional VAR models. Similar results have been obtained in the causal effects of S&P 500 on WTI. In addition, the proposed model outperforms the traditional VAR model.« less

Time-varying coefficient vector autoregressions model based on dynamic correlation with an application to crude oil and stock markets.

PubMed

Lu, Fengbin; Qiao, Han; Wang, Shouyang; Lai, Kin Keung; Li, Yuze

2017-01-01

This paper proposes a new time-varying coefficient vector autoregressions (VAR) model, in which the coefficient is a linear function of dynamic lagged correlation. The proposed model allows for flexibility in choices of dynamic correlation models (e.g. dynamic conditional correlation generalized autoregressive conditional heteroskedasticity (GARCH) models, Markov-switching GARCH models and multivariate stochastic volatility models), which indicates that it can describe many types of time-varying causal effects. Time-varying causal relations between West Texas Intermediate (WTI) crude oil and the US Standard and Poor's 500 (S&P 500) stock markets are examined by the proposed model. The empirical results show that their causal relations evolve with time and display complex characters. Both positive and negative causal effects of the WTI on the S&P 500 in the subperiods have been found and confirmed by the traditional VAR models. Similar results have been obtained in the causal effects of S&P 500 on WTI. In addition, the proposed model outperforms the traditional VAR model. Copyright © 2016 Elsevier Ltd. All rights reserved.

The Disparate Labor Market Impacts of Monetary Policy

ERIC Educational Resources Information Center

Carpenter, Seth B.; Rodgers, William M., III

2004-01-01

Employing two widely used approaches to identify the effects of monetary policy, this paper explores the differential impact of policy on the labor market outcomes of teenagers, minorities, out-of-school youth, and less-skilled individuals. Evidence from recursive vector autoregressions and autoregressive distributed lag models that use…

A time domain frequency-selective multivariate Granger causality approach.

PubMed

Leistritz, Lutz; Witte, Herbert

2016-08-01

The investigation of effective connectivity is one of the major topics in computational neuroscience to understand the interaction between spatially distributed neuronal units of the brain. Thus, a wide variety of methods has been developed during the last decades to investigate functional and effective connectivity in multivariate systems. Their spectrum ranges from model-based to model-free approaches with a clear separation into time and frequency range methods. We present in this simulation study a novel time domain approach based on Granger's principle of predictability, which allows frequency-selective considerations of directed interactions. It is based on a comparison of prediction errors of multivariate autoregressive models fitted to systematically modified time series. These modifications are based on signal decompositions, which enable a targeted cancellation of specific signal components with specific spectral properties. Depending on the embedded signal decomposition method, a frequency-selective or data-driven signal-adaptive Granger Causality Index may be derived.

(Re)evaluating the Implications of the Autoregressive Latent Trajectory Model Through Likelihood Ratio Tests of Its Initial Conditions.

PubMed

Ou, Lu; Chow, Sy-Miin; Ji, Linying; Molenaar, Peter C M

2017-01-01

The autoregressive latent trajectory (ALT) model synthesizes the autoregressive model and the latent growth curve model. The ALT model is flexible enough to produce a variety of discrepant model-implied change trajectories. While some researchers consider this a virtue, others have cautioned that this may confound interpretations of the model's parameters. In this article, we show that some-but not all-of these interpretational difficulties may be clarified mathematically and tested explicitly via likelihood ratio tests (LRTs) imposed on the initial conditions of the model. We show analytically the nested relations among three variants of the ALT model and the constraints needed to establish equivalences. A Monte Carlo simulation study indicated that LRTs, particularly when used in combination with information criterion measures, can allow researchers to test targeted hypotheses about the functional forms of the change process under study. We further demonstrate when and how such tests may justifiably be used to facilitate our understanding of the underlying process of change using a subsample (N = 3,995) of longitudinal family income data from the National Longitudinal Survey of Youth.

Statistical process control of mortality series in the Australian and New Zealand Intensive Care Society (ANZICS) adult patient database: implications of the data generating process.

PubMed

Moran, John L; Solomon, Patricia J

2013-05-24

Statistical process control (SPC), an industrial sphere initiative, has recently been applied in health care and public health surveillance. SPC methods assume independent observations and process autocorrelation has been associated with increase in false alarm frequency. Monthly mean raw mortality (at hospital discharge) time series, 1995-2009, at the individual Intensive Care unit (ICU) level, were generated from the Australia and New Zealand Intensive Care Society adult patient database. Evidence for series (i) autocorrelation and seasonality was demonstrated using (partial)-autocorrelation ((P)ACF) function displays and classical series decomposition and (ii) "in-control" status was sought using risk-adjusted (RA) exponentially weighted moving average (EWMA) control limits (3 sigma). Risk adjustment was achieved using a random coefficient (intercept as ICU site and slope as APACHE III score) logistic regression model, generating an expected mortality series. Application of time-series to an exemplar complete ICU series (1995-(end)2009) was via Box-Jenkins methodology: autoregressive moving average (ARMA) and (G)ARCH ((Generalised) Autoregressive Conditional Heteroscedasticity) models, the latter addressing volatility of the series variance. The overall data set, 1995-2009, consisted of 491324 records from 137 ICU sites; average raw mortality was 14.07%; average(SD) raw and expected mortalities ranged from 0.012(0.113) and 0.013(0.045) to 0.296(0.457) and 0.278(0.247) respectively. For the raw mortality series: 71 sites had continuous data for assessment up to or beyond lag40 and 35% had autocorrelation through to lag40; and of 36 sites with continuous data for ≥ 72 months, all demonstrated marked seasonality. Similar numbers and percentages were seen with the expected series. Out-of-control signalling was evident for the raw mortality series with respect to RA-EWMA control limits; a seasonal ARMA model, with GARCH effects, displayed white-noise residuals which were in-control with respect to EWMA control limits and one-step prediction error limits (3SE). The expected series was modelled with a multiplicative seasonal autoregressive model. The data generating process of monthly raw mortality series at the ICU level displayed autocorrelation, seasonality and volatility. False-positive signalling of the raw mortality series was evident with respect to RA-EWMA control limits. A time series approach using residual control charts resolved these issues.

Glucose Prediction Algorithms from Continuous Monitoring Data: Assessment of Accuracy via Continuous Glucose Error-Grid Analysis.

PubMed

Zanderigo, Francesca; Sparacino, Giovanni; Kovatchev, Boris; Cobelli, Claudio

2007-09-01

The aim of this article was to use continuous glucose error-grid analysis (CG-EGA) to assess the accuracy of two time-series modeling methodologies recently developed to predict glucose levels ahead of time using continuous glucose monitoring (CGM) data. We considered subcutaneous time series of glucose concentration monitored every 3 minutes for 48 hours by the minimally invasive CGM sensor Glucoday® (Menarini Diagnostics, Florence, Italy) in 28 type 1 diabetic volunteers. Two prediction algorithms, based on first-order polynomial and autoregressive (AR) models, respectively, were considered with prediction horizons of 30 and 45 minutes and forgetting factors (ff) of 0.2, 0.5, and 0.8. CG-EGA was used on the predicted profiles to assess their point and dynamic accuracies using original CGM profiles as reference. Continuous glucose error-grid analysis showed that the accuracy of both prediction algorithms is overall very good and that their performance is similar from a clinical point of view. However, the AR model seems preferable for hypoglycemia prevention. CG-EGA also suggests that, irrespective of the time-series model, the use of ff = 0.8 yields the highest accurate readings in all glucose ranges. For the first time, CG-EGA is proposed as a tool to assess clinically relevant performance of a prediction method separately at hypoglycemia, euglycemia, and hyperglycemia. In particular, we have shown that CG-EGA can be helpful in comparing different prediction algorithms, as well as in optimizing their parameters.

Short-term forecasting of emergency inpatient flow.

PubMed

Abraham, Gad; Byrnes, Graham B; Bain, Christopher A

2009-05-01

Hospital managers have to manage resources effectively, while maintaining a high quality of care. For hospitals where admissions from the emergency department to the wards represent a large proportion of admissions, the ability to forecast these admissions and the resultant ward occupancy is especially useful for resource planning purposes. Since emergency admissions often compete with planned elective admissions, modeling emergency demand may result in improved elective planning as well. We compare several models for forecasting daily emergency inpatient admissions and occupancy. The models are applied to three years of daily data. By measuring their mean square error in a cross-validation framework, we find that emergency admissions are largely random, and hence, unpredictable, whereas emergency occupancy can be forecasted using a model combining regression and autoregressive integrated moving average (ARIMA) model, or a seasonal ARIMA model, for up to one week ahead. Faced with variable admissions and occupancy, hospitals must prepare a reserve capacity of beds and staff. Our approach allows estimation of the required reserve capacity.

Toward a Model-Based Predictive Controller Design in Brain–Computer Interfaces

PubMed Central

Kamrunnahar, M.; Dias, N. S.; Schiff, S. J.

2013-01-01

A first step in designing a robust and optimal model-based predictive controller (MPC) for brain–computer interface (BCI) applications is presented in this article. An MPC has the potential to achieve improved BCI performance compared to the performance achieved by current ad hoc, nonmodel-based filter applications. The parameters in designing the controller were extracted as model-based features from motor imagery task-related human scalp electroencephalography. Although the parameters can be generated from any model-linear or non-linear, we here adopted a simple autoregressive model that has well-established applications in BCI task discriminations. It was shown that the parameters generated for the controller design can as well be used for motor imagery task discriminations with performance (with 8–23% task discrimination errors) comparable to the discrimination performance of the commonly used features such as frequency specific band powers and the AR model parameters directly used. An optimal MPC has significant implications for high performance BCI applications. PMID:21267657

Toward a model-based predictive controller design in brain-computer interfaces.

PubMed

Kamrunnahar, M; Dias, N S; Schiff, S J

2011-05-01

A first step in designing a robust and optimal model-based predictive controller (MPC) for brain-computer interface (BCI) applications is presented in this article. An MPC has the potential to achieve improved BCI performance compared to the performance achieved by current ad hoc, nonmodel-based filter applications. The parameters in designing the controller were extracted as model-based features from motor imagery task-related human scalp electroencephalography. Although the parameters can be generated from any model-linear or non-linear, we here adopted a simple autoregressive model that has well-established applications in BCI task discriminations. It was shown that the parameters generated for the controller design can as well be used for motor imagery task discriminations with performance (with 8-23% task discrimination errors) comparable to the discrimination performance of the commonly used features such as frequency specific band powers and the AR model parameters directly used. An optimal MPC has significant implications for high performance BCI applications.

Prediction of global ionospheric VTEC maps using an adaptive autoregressive model

NASA Astrophysics Data System (ADS)

Wang, Cheng; Xin, Shaoming; Liu, Xiaolu; Shi, Chuang; Fan, Lei

2018-02-01

In this contribution, an adaptive autoregressive model is proposed and developed to predict global ionospheric vertical total electron content maps (VTEC). Specifically, the spherical harmonic (SH) coefficients are predicted based on the autoregressive model, and the order of the autoregressive model is determined adaptively using the F-test method. To test our method, final CODE and IGS global ionospheric map (GIM) products, as well as altimeter TEC data during low and mid-to-high solar activity period collected by JASON, are used to evaluate the precision of our forecasting products. Results indicate that the predicted products derived from the model proposed in this paper have good consistency with the final GIMs in low solar activity, where the annual mean of the root-mean-square value is approximately 1.5 TECU. However, the performance of predicted vertical TEC in periods of mid-to-high solar activity has less accuracy than that during low solar activity periods, especially in the equatorial ionization anomaly region and the Southern Hemisphere. Additionally, in comparison with forecasting products, the final IGS GIMs have the best consistency with altimeter TEC data. Future work is needed to investigate the performance of forecasting products using the proposed method in an operational environment, rather than using the SH coefficients from the final CODE products, to understand the real-time applicability of the method.

A comparison of monthly precipitation point estimates at 6 locations in Iran using integration of soft computing methods and GARCH time series model

NASA Astrophysics Data System (ADS)

Mehdizadeh, Saeid; Behmanesh, Javad; Khalili, Keivan

2017-11-01

Precipitation plays an important role in determining the climate of a region. Precise estimation of precipitation is required to manage and plan water resources, as well as other related applications such as hydrology, climatology, meteorology and agriculture. Time series of hydrologic variables such as precipitation are composed of deterministic and stochastic parts. Despite this fact, the stochastic part of the precipitation data is not usually considered in modeling of precipitation process. As an innovation, the present study introduces three new hybrid models by integrating soft computing methods including multivariate adaptive regression splines (MARS), Bayesian networks (BN) and gene expression programming (GEP) with a time series model, namely generalized autoregressive conditional heteroscedasticity (GARCH) for modeling of the monthly precipitation. For this purpose, the deterministic (obtained by soft computing methods) and stochastic (obtained by GARCH time series model) parts are combined with each other. To carry out this research, monthly precipitation data of Babolsar, Bandar Anzali, Gorgan, Ramsar, Tehran and Urmia stations with different climates in Iran were used during the period of 1965-2014. Root mean square error (RMSE), relative root mean square error (RRMSE), mean absolute error (MAE) and determination coefficient (R2) were employed to evaluate the performance of conventional/single MARS, BN and GEP, as well as the proposed MARS-GARCH, BN-GARCH and GEP-GARCH hybrid models. It was found that the proposed novel models are more precise than single MARS, BN and GEP models. Overall, MARS-GARCH and BN-GARCH models yielded better accuracy than GEP-GARCH. The results of the present study confirmed the suitability of proposed methodology for precise modeling of precipitation.

High-Order Model and Dynamic Filtering for Frame Rate Up-Conversion.

PubMed

Bao, Wenbo; Zhang, Xiaoyun; Chen, Li; Ding, Lianghui; Gao, Zhiyong

2018-08-01

This paper proposes a novel frame rate up-conversion method through high-order model and dynamic filtering (HOMDF) for video pixels. Unlike the constant brightness and linear motion assumptions in traditional methods, the intensity and position of the video pixels are both modeled with high-order polynomials in terms of time. Then, the key problem of our method is to estimate the polynomial coefficients that represent the pixel's intensity variation, velocity, and acceleration. We propose to solve it with two energy objectives: one minimizes the auto-regressive prediction error of intensity variation by its past samples, and the other minimizes video frame's reconstruction error along the motion trajectory. To efficiently address the optimization problem for these coefficients, we propose the dynamic filtering solution inspired by video's temporal coherence. The optimal estimation of these coefficients is reformulated into a dynamic fusion of the prior estimate from pixel's temporal predecessor and the maximum likelihood estimate from current new observation. Finally, frame rate up-conversion is implemented using motion-compensated interpolation by pixel-wise intensity variation and motion trajectory. Benefited from the advanced model and dynamic filtering, the interpolated frame has much better visual quality. Extensive experiments on the natural and synthesized videos demonstrate the superiority of HOMDF over the state-of-the-art methods in both subjective and objective comparisons.

Detecting P and S-wave of Mt. Rinjani seismic based on a locally stationary autoregressive (LSAR) model

NASA Astrophysics Data System (ADS)

Nurhaida, Subanar, Abdurakhman, Abadi, Agus Maman

2017-08-01

Seismic data is usually modelled using autoregressive processes. The aim of this paper is to find the arrival times of the seismic waves of Mt. Rinjani in Indonesia. Kitagawa algorithm's is used to detect the seismic P and S-wave. Householder transformation used in the algorithm made it effectively finding the number of change points and parameters of the autoregressive models. The results show that the use of Box-Cox transformation on the variable selection level makes the algorithm works well in detecting the change points. Furthermore, when the basic span of the subinterval is set 200 seconds and the maximum AR order is 20, there are 8 change points which occur at 1601, 2001, 7401, 7601,7801, 8001, 8201 and 9601. Finally, The P and S-wave arrival times are detected at time 1671 and 2045 respectively using a precise detection algorithm.

Medium term municipal solid waste generation prediction by autoregressive integrated moving average

DOE Office of Scientific and Technical Information (OSTI.GOV)

Younes, Mohammad K.; Nopiah, Z. M.; Basri, Noor Ezlin A.

2014-09-12

Generally, solid waste handling and management are performed by municipality or local authority. In most of developing countries, local authorities suffer from serious solid waste management (SWM) problems and insufficient data and strategic planning. Thus it is important to develop robust solid waste generation forecasting model. It helps to proper manage the generated solid waste and to develop future plan based on relatively accurate figures. In Malaysia, solid waste generation rate increases rapidly due to the population growth and new consumption trends that characterize the modern life style. This paper aims to develop monthly solid waste forecasting model using Autoregressivemore » Integrated Moving Average (ARIMA), such model is applicable even though there is lack of data and will help the municipality properly establish the annual service plan. The results show that ARIMA (6,1,0) model predicts monthly municipal solid waste generation with root mean square error equals to 0.0952 and the model forecast residuals are within accepted 95% confident interval.« less

Software sensors for biomass concentration in a SSC process using artificial neural networks and support vector machine.

PubMed

Acuña, Gonzalo; Ramirez, Cristian; Curilem, Millaray

2014-01-01

The lack of sensors for some relevant state variables in fermentation processes can be coped by developing appropriate software sensors. In this work, NARX-ANN, NARMAX-ANN, NARX-SVM and NARMAX-SVM models are compared when acting as software sensors of biomass concentration for a solid substrate cultivation (SSC) process. Results show that NARMAX-SVM outperforms the other models with an SMAPE index under 9 for a 20 % amplitude noise. In addition, NARMAX models perform better than NARX models under the same noise conditions because of their better predictive capabilities as they include prediction errors as inputs. In the case of perturbation of initial conditions of the autoregressive variable, NARX models exhibited better convergence capabilities. This work also confirms that a difficult to measure variable, like biomass concentration, can be estimated on-line from easy to measure variables like CO₂ and O₂ using an adequate software sensor based on computational intelligence techniques.

Predation and fragmentation portrayed in the statistical structure of prey time series

PubMed Central

Hendrichsen, Ditte K; Topping, Chris J; Forchhammer, Mads C

2009-01-01

Background Statistical autoregressive analyses of direct and delayed density dependence are widespread in ecological research. The models suggest that changes in ecological factors affecting density dependence, like predation and landscape heterogeneity are directly portrayed in the first and second order autoregressive parameters, and the models are therefore used to decipher complex biological patterns. However, independent tests of model predictions are complicated by the inherent variability of natural populations, where differences in landscape structure, climate or species composition prevent controlled repeated analyses. To circumvent this problem, we applied second-order autoregressive time series analyses to data generated by a realistic agent-based computer model. The model simulated life history decisions of individual field voles under controlled variations in predator pressure and landscape fragmentation. Analyses were made on three levels: comparisons between predated and non-predated populations, between populations exposed to different types of predators and between populations experiencing different degrees of habitat fragmentation. Results The results are unambiguous: Changes in landscape fragmentation and the numerical response of predators are clearly portrayed in the statistical time series structure as predicted by the autoregressive model. Populations without predators displayed significantly stronger negative direct density dependence than did those exposed to predators, where direct density dependence was only moderately negative. The effects of predation versus no predation had an even stronger effect on the delayed density dependence of the simulated prey populations. In non-predated prey populations, the coefficients of delayed density dependence were distinctly positive, whereas they were negative in predated populations. Similarly, increasing the degree of fragmentation of optimal habitat available to the prey was accompanied with a shift in the delayed density dependence, from strongly negative to gradually becoming less negative. Conclusion We conclude that statistical second-order autoregressive time series analyses are capable of deciphering interactions within and across trophic levels and their effect on direct and delayed density dependence. PMID:19419539

Computational problems in autoregressive moving average (ARMA) models

NASA Technical Reports Server (NTRS)

Agarwal, G. C.; Goodarzi, S. M.; Oneill, W. D.; Gottlieb, G. L.

1981-01-01

The choice of the sampling interval and the selection of the order of the model in time series analysis are considered. Band limited (up to 15 Hz) random torque perturbations are applied to the human ankle joint. The applied torque input, the angular rotation output, and the electromyographic activity using surface electrodes from the extensor and flexor muscles of the ankle joint are recorded. Autoregressive moving average models are developed. A parameter constraining technique is applied to develop more reliable models. The asymptotic behavior of the system must be taken into account during parameter optimization to develop predictive models.

Modeling Polio Data Using the First Order Non-Negative Integer-Valued Autoregressive, INAR(1), Model

NASA Astrophysics Data System (ADS)

Vazifedan, Turaj; Shitan, Mahendran

Time series data may consists of counts, such as the number of road accidents, the number of patients in a certain hospital, the number of customers waiting for service at a certain time and etc. When the value of the observations are large it is usual to use Gaussian Autoregressive Moving Average (ARMA) process to model the time series. However if the observed counts are small, it is not appropriate to use ARMA process to model the observed phenomenon. In such cases we need to model the time series data by using Non-Negative Integer valued Autoregressive (INAR) process. The modeling of counts data is based on the binomial thinning operator. In this paper we illustrate the modeling of counts data using the monthly number of Poliomyelitis data in United States between January 1970 until December 1983. We applied the AR(1), Poisson regression model and INAR(1) model and the suitability of these models were assessed by using the Index of Agreement(I.A.). We found that INAR(1) model is more appropriate in the sense it had a better I.A. and it is natural since the data are counts.

Acceleration and Velocity Sensing from Measured Strain

NASA Technical Reports Server (NTRS)

Pak, Chan-Gi; Truax, Roger

2015-01-01

A simple approach for computing acceleration and velocity of a structure from the strain is proposed in this study. First, deflection and slope of the structure are computed from the strain using a two-step theory. Frequencies of the structure are computed from the time histories of strain using a parameter estimation technique together with an autoregressive moving average model. From deflection, slope, and frequencies of the structure, acceleration and velocity of the structure can be obtained using the proposed approach. Simple harmonic motion is assumed for the acceleration computations, and the central difference equation with a linear autoregressive model is used for the computations of velocity. A cantilevered rectangular wing model is used to validate the simple approach. Quality of the computed deflection, acceleration, and velocity values are independent of the number of fibers. The central difference equation with a linear autoregressive model proposed in this study follows the target response with reasonable accuracy. Therefore, the handicap of the backward difference equation, phase shift, is successfully overcome.

Anomalous Fluctuations in Autoregressive Models with Long-Term Memory

NASA Astrophysics Data System (ADS)

Sakaguchi, Hidetsugu; Honjo, Haruo

2015-10-01

An autoregressive model with a power-law type memory kernel is studied as a stochastic process that exhibits a self-affine-fractal-like behavior for a small time scale. We find numerically that the root-mean-square displacement Δ(m) for the time interval m increases with a power law as mα with α < 1/2 for small m but saturates at sufficiently large m. The exponent α changes with the power exponent of the memory kernel.

EEG data reduction by means of autoregressive representation and discriminant analysis procedures.

PubMed

Blinowska, K J; Czerwosz, L T; Drabik, W; Franaszczuk, P J; Ekiert, H

1981-06-01

A program for automatic evaluation of EEG spectra, providing considerable reduction of data, was devised. Artefacts were eliminated in two steps: first, the longer duration eye movement artefacts were removed by a fast and simple 'moving integral' methods, then occasional spikes were identified by means of a detection function defined in the formalism of the autoregressive (AR) model. The evaluation of power spectra was performed by means of an FFT and autoregressive representation, which made possible the comparison of both methods. The spectra obtained by means of the AR model had much smaller statistical fluctuations and better resolution, enabling us to follow the time changes of the EEG pattern. Another advantage of the autoregressive approach was the parametric description of the signal. This last property appeared to be essential in distinguishing the changes in the EEG pattern. In a drug study the application of the coefficients of the AR model as input parameters in the discriminant analysis, instead of arbitrary chosen frequency bands, brought a significant improvement in distinguishing the effects of the medication. The favourable properties of the AR model are connected with the fact that the above approach fulfils the maximum entropy principle. This means that the method describes in a maximally consistent way the available information and is free from additional assumptions, which is not the case for the FFT estimate.

Accounting for spatial effects in land use regression for urban air pollution modeling.

PubMed

Bertazzon, Stefania; Johnson, Markey; Eccles, Kristin; Kaplan, Gilaad G

2015-01-01

In order to accurately assess air pollution risks, health studies require spatially resolved pollution concentrations. Land-use regression (LUR) models estimate ambient concentrations at a fine spatial scale. However, spatial effects such as spatial non-stationarity and spatial autocorrelation can reduce the accuracy of LUR estimates by increasing regression errors and uncertainty; and statistical methods for resolving these effects--e.g., spatially autoregressive (SAR) and geographically weighted regression (GWR) models--may be difficult to apply simultaneously. We used an alternate approach to address spatial non-stationarity and spatial autocorrelation in LUR models for nitrogen dioxide. Traditional models were re-specified to include a variable capturing wind speed and direction, and re-fit as GWR models. Mean R(2) values for the resulting GWR-wind models (summer: 0.86, winter: 0.73) showed a 10-20% improvement over traditional LUR models. GWR-wind models effectively addressed both spatial effects and produced meaningful predictive models. These results suggest a useful method for improving spatially explicit models. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

AR(p) -based detrended fluctuation analysis

NASA Astrophysics Data System (ADS)

Alvarez-Ramirez, J.; Rodriguez, E.

2018-07-01

Autoregressive models are commonly used for modeling time-series from nature, economics and finance. This work explored simple autoregressive AR(p) models to remove long-term trends in detrended fluctuation analysis (DFA). Crude oil prices and bitcoin exchange rate were considered, with the former corresponding to a mature market and the latter to an emergent market. Results showed that AR(p) -based DFA performs similar to traditional DFA. However, the former DFA provides information on stability of long-term trends, which is valuable for understanding and quantifying the dynamics of complex time series from financial systems.

Numerical limitations in application of vector autoregressive modeling and Granger causality to analysis of EEG time series

NASA Astrophysics Data System (ADS)

Kammerdiner, Alla; Xanthopoulos, Petros; Pardalos, Panos M.

2007-11-01

In this chapter a potential problem with application of the Granger-causality based on the simple vector autoregressive (VAR) modeling to EEG data is investigated. Although some initial studies tested whether the data support the stationarity assumption of VAR, the stability of the estimated model is rarely (if ever) been verified. In fact, in cases when the stability condition is violated the process may exhibit a random walk like behavior or even be explosive. The problem is illustrated by an example.

Statistical models for estimating daily streamflow in Michigan

USGS Publications Warehouse

Holtschlag, D.J.; Salehi, Habib

1992-01-01

Statistical models for estimating daily streamflow were analyzed for 25 pairs of streamflow-gaging stations in Michigan. Stations were paired by randomly choosing a station operated in 1989 at which 10 or more years of continuous flow data had been collected and at which flow is virtually unregulated; a nearby station was chosen where flow characteristics are similar. Streamflow data from the 25 randomly selected stations were used as the response variables; streamflow data at the nearby stations were used to generate a set of explanatory variables. Ordinary-least squares regression (OLSR) equations, autoregressive integrated moving-average (ARIMA) equations, and transfer function-noise (TFN) equations were developed to estimate the log transform of flow for the 25 randomly selected stations. The precision of each type of equation was evaluated on the basis of the standard deviation of the estimation errors. OLSR equations produce one set of estimation errors; ARIMA and TFN models each produce l sets of estimation errors corresponding to the forecast lead. The lead-l forecast is the estimate of flow l days ahead of the most recent streamflow used as a response variable in the estimation. In this analysis, the standard deviation of lead l ARIMA and TFN forecast errors were generally lower than the standard deviation of OLSR errors for l < 2 days and l < 9 days, respectively. Composite estimates were computed as a weighted average of forecasts based on TFN equations and backcasts (forecasts of the reverse-ordered series) based on ARIMA equations. The standard deviation of composite errors varied throughout the length of the estimation interval and generally was at maximum near the center of the interval. For comparison with OLSR errors, the mean standard deviation of composite errors were computed for intervals of length 1 to 40 days. The mean standard deviation of length-l composite errors were generally less than the standard deviation of the OLSR errors for l < 32 days. In addition, the composite estimates ensure a gradual transition between periods of estimated and measured flows. Model performance among stations of differing model error magnitudes were compared by computing ratios of the mean standard deviation of the length l composite errors to the standard deviation of OLSR errors. The mean error ratio for the set of 25 selected stations was less than 1 for intervals l < 32 days. Considering the frequency characteristics of the length of intervals of estimated record in Michigan, the effective mean error ratio for intervals < 30 days was 0.52. Thus, for intervals of estimation of 1 month or less, the error of the composite estimate is substantially lower than error of the OLSR estimate.

Selecting a separable parametric spatiotemporal covariance structure for longitudinal imaging data.

PubMed

George, Brandon; Aban, Inmaculada

2015-01-15

Longitudinal imaging studies allow great insight into how the structure and function of a subject's internal anatomy changes over time. Unfortunately, the analysis of longitudinal imaging data is complicated by inherent spatial and temporal correlation: the temporal from the repeated measures and the spatial from the outcomes of interest being observed at multiple points in a patient's body. We propose the use of a linear model with a separable parametric spatiotemporal error structure for the analysis of repeated imaging data. The model makes use of spatial (exponential, spherical, and Matérn) and temporal (compound symmetric, autoregressive-1, Toeplitz, and unstructured) parametric correlation functions. A simulation study, inspired by a longitudinal cardiac imaging study on mitral regurgitation patients, compared different information criteria for selecting a particular separable parametric spatiotemporal correlation structure as well as the effects on types I and II error rates for inference on fixed effects when the specified model is incorrect. Information criteria were found to be highly accurate at choosing between separable parametric spatiotemporal correlation structures. Misspecification of the covariance structure was found to have the ability to inflate the type I error or have an overly conservative test size, which corresponded to decreased power. An example with clinical data is given illustrating how the covariance structure procedure can be performed in practice, as well as how covariance structure choice can change inferences about fixed effects. Copyright © 2014 John Wiley & Sons, Ltd.

The color of sea level: Importance of spatial variations in spectral shape for assessing the significance of trends

NASA Astrophysics Data System (ADS)

Hughes, Chris W.; Williams, Simon D. P.

2010-10-01

We investigate spatial variations in the shape of the spectrum of sea level variability based on a homogeneously sampled 12 year gridded altimeter data set. We present a method of plotting spectral information as color, focusing on periods between 2 and 24 weeks, which shows that significant spatial variations in the spectral shape exist and contain useful dynamical information. Using the Bayesian Information Criterion, we determine that, typically, a fifth-order autoregressive model is needed to capture the structure in the spectrum. Using this model, we show that statistical errors in fitted local trends range between less than 1 and more than 5 times of what would be calculated assuming "white" noise and that the time needed to detect a 1 mm/yr trend ranges between about 5 years and many decades. For global mean sea level, the statistical error reduces to 0.1 mm/yr over 12 years, with only 2 years needed to detect a 1 mm/yr trend. We find significant regional differences in trend from the global mean. The patterns of these regional differences are indicative of a sea level trend dominated by dynamical ocean processes over this period.

Forecasting hotspots in East Kutai, Kutai Kartanegara, and West Kutai as early warning information

NASA Astrophysics Data System (ADS)

Wahyuningsih, S.; Goejantoro, R.; Rizki, N. A.

2018-04-01

The aims of this research are to model hotspots and forecast hotspot 2017 in East Kutai, Kutai Kartanegara and West Kutai. The methods which used in this research were Holt exponential smoothing, Holt’s additive dump trend method, Holt-Winters’ additive method, additive decomposition method, multiplicative decomposition method, Loess decomposition method and Box-Jenkins method. For smoothing techniques, additive decomposition is better than Holt’s exponential smoothing. The hotspots model using Box-Jenkins method were Autoregressive Moving Average ARIMA(1,1,0), ARIMA(0,2,1), and ARIMA(0,1,0). Comparing the results from all methods which were used in this research, and based on Root of Mean Squared Error (RMSE), show that Loess decomposition method is the best times series model, because it has the least RMSE. Thus the Loess decomposition model used to forecast the number of hotspot. The forecasting result indicatethat hotspots pattern tend to increase at the end of 2017 in Kutai Kartanegara and West Kutai, but stationary in East Kutai.

Time series modelling of increased soil temperature anomalies during long period

NASA Astrophysics Data System (ADS)

Shirvani, Amin; Moradi, Farzad; Moosavi, Ali Akbar

2015-10-01

Soil temperature just beneath the soil surface is highly dynamic and has a direct impact on plant seed germination and is probably the most distinct and recognisable factor governing emergence. Autoregressive integrated moving average as a stochastic model was developed to predict the weekly soil temperature anomalies at 10 cm depth, one of the most important soil parameters. The weekly soil temperature anomalies for the periods of January1986-December 2011 and January 2012-December 2013 were taken into consideration to construct and test autoregressive integrated moving average models. The proposed model autoregressive integrated moving average (2,1,1) had a minimum value of Akaike information criterion and its estimated coefficients were different from zero at 5% significance level. The prediction of the weekly soil temperature anomalies during the test period using this proposed model indicated a high correlation coefficient between the observed and predicted data - that was 0.99 for lead time 1 week. Linear trend analysis indicated that the soil temperature anomalies warmed up significantly by 1.8°C during the period of 1986-2011.

Forecasting malaria in a highly endemic country using environmental and clinical predictors.

PubMed

Zinszer, Kate; Kigozi, Ruth; Charland, Katia; Dorsey, Grant; Brewer, Timothy F; Brownstein, John S; Kamya, Moses R; Buckeridge, David L

2015-06-18

Malaria thrives in poor tropical and subtropical countries where local resources are limited. Accurate disease forecasts can provide public and clinical health services with the information needed to implement targeted approaches for malaria control that make effective use of limited resources. The objective of this study was to determine the relevance of environmental and clinical predictors of malaria across different settings in Uganda. Forecasting models were based on health facility data collected by the Uganda Malaria Surveillance Project and satellite-derived rainfall, temperature, and vegetation estimates from 2006 to 2013. Facility-specific forecasting models of confirmed malaria were developed using multivariate autoregressive integrated moving average models and produced weekly forecast horizons over a 52-week forecasting period. The model with the most accurate forecasts varied by site and by forecast horizon. Clinical predictors were retained in the models with the highest predictive power for all facility sites. The average error over the 52 forecasting horizons ranged from 26 to 128% whereas the cumulative burden forecast error ranged from 2 to 22%. Clinical data, such as drug treatment, could be used to improve the accuracy of malaria predictions in endemic settings when coupled with environmental predictors. Further exploration of malaria forecasting is necessary to improve its accuracy and value in practice, including examining other environmental and intervention predictors, including insecticide-treated nets.

[Application of R-based multiple seasonal ARIMA model, in predicting the incidence of hand, foot and mouth disease in Shaanxi province].

PubMed

Liu, F; Zhu, N; Qiu, L; Wang, J J; Wang, W H

2016-08-10

To apply the ' auto-regressive integrated moving average product seasonal model' in predicting the number of hand, foot and mouth disease in Shaanxi province. In Shaanxi province, the trend of hand, foot and mouth disease was analyzed and tested, under the use of R software, between January 2009 and June 2015. Multiple seasonal ARIMA model was then fitted under time series to predict the number of hand, foot and mouth disease in 2016 and 2017. Seasonal effect was seen in hand, foot and mouth disease in Shaanxi province. A multiple seasonal ARIMA (2,1,0)×(1,1,0)12 was established, with the equation as (1 -B)(1 -B12)Ln (Xt) =((1-1.000B)/(1-0.532B-0.363B(2))*(1-0.644B12-0.454B12(2)))*Epsilont. The mean of absolute error and the relative error were 531.535 and 0.114, respectively when compared to the simulated number of patients from Jun to Dec in 2015. RESULTS under the prediction of multiple seasonal ARIMA model showed that the numbers of patients in both 2016 and 2017 were similar to that of 2015 in Shaanxi province. Multiple seasonal ARIMA (2,1,0)×(1,1,0)12 model could be used to successfully predict the incidence of hand, foot and mouth disease in Shaanxi province.

Maximum likelihood estimation for periodic autoregressive moving average models

USGS Publications Warehouse

Vecchia, A.V.

1985-01-01

A useful class of models for seasonal time series that cannot be filtered or standardized to achieve second-order stationarity is that of periodic autoregressive moving average (PARMA) models, which are extensions of ARMA models that allow periodic (seasonal) parameters. An approximation to the exact likelihood for Gaussian PARMA processes is developed, and a straightforward algorithm for its maximization is presented. The algorithm is tested on several periodic ARMA(1, 1) models through simulation studies and is compared to moment estimation via the seasonal Yule-Walker equations. Applicability of the technique is demonstrated through an analysis of a seasonal stream-flow series from the Rio Caroni River in Venezuela.

Application of a new hybrid model with seasonal auto-regressive integrated moving average (ARIMA) and nonlinear auto-regressive neural network (NARNN) in forecasting incidence cases of HFMD in Shenzhen, China.

PubMed

Yu, Lijing; Zhou, Lingling; Tan, Li; Jiang, Hongbo; Wang, Ying; Wei, Sheng; Nie, Shaofa

2014-01-01

Outbreaks of hand-foot-mouth disease (HFMD) have been reported for many times in Asia during the last decades. This emerging disease has drawn worldwide attention and vigilance. Nowadays, the prevention and control of HFMD has become an imperative issue in China. Early detection and response will be helpful before it happening, using modern information technology during the epidemic. In this paper, a hybrid model combining seasonal auto-regressive integrated moving average (ARIMA) model and nonlinear auto-regressive neural network (NARNN) is proposed to predict the expected incidence cases from December 2012 to May 2013, using the retrospective observations obtained from China Information System for Disease Control and Prevention from January 2008 to November 2012. The best-fitted hybrid model was combined with seasonal ARIMA [Formula: see text] and NARNN with 15 hidden units and 5 delays. The hybrid model makes the good forecasting performance and estimates the expected incidence cases from December 2012 to May 2013, which are respectively -965.03, -1879.58, 4138.26, 1858.17, 4061.86 and 6163.16 with an obviously increasing trend. The model proposed in this paper can predict the incidence trend of HFMD effectively, which could be helpful to policy makers. The usefulness of expected cases of HFMD perform not only in detecting outbreaks or providing probability statements, but also in providing decision makers with a probable trend of the variability of future observations that contains both historical and recent information.

Forecasting currency circulation data of Bank Indonesia by using hybrid ARIMAX-ANN model

NASA Astrophysics Data System (ADS)

Prayoga, I. Gede Surya Adi; Suhartono, Rahayu, Santi Puteri

2017-05-01

The purpose of this study is to forecast currency inflow and outflow data of Bank Indonesia. Currency circulation in Indonesia is highly influenced by the presence of Eid al-Fitr. One way to forecast the data with Eid al-Fitr effect is using autoregressive integrated moving average with exogenous input (ARIMAX) model. However, ARIMAX is a linear model, which cannot handle nonlinear correlation structures of the data. In the field of forecasting, inaccurate predictions can be considered caused by the existence of nonlinear components that are uncaptured by the model. In this paper, we propose a hybrid model of ARIMAX and artificial neural networks (ANN) that can handle both linear and nonlinear correlation. This method was applied for 46 series of currency inflow and 46 series of currency outflow. The results showed that based on out-of-sample root mean squared error (RMSE), the hybrid models are up to10.26 and 10.65 percent better than ARIMAX for inflow and outflow series, respectively. It means that ANN performs well in modeling nonlinear correlation of the data and can increase the accuracy of linear model.

Forecasting incidence of dengue in Rajasthan, using time series analyses.

PubMed

Bhatnagar, Sunil; Lal, Vivek; Gupta, Shiv D; Gupta, Om P

2012-01-01

To develop a prediction model for dengue fever/dengue haemorrhagic fever (DF/DHF) using time series data over the past decade in Rajasthan and to forecast monthly DF/DHF incidence for 2011. Seasonal autoregressive integrated moving average (SARIMA) model was used for statistical modeling. During January 2001 to December 2010, the reported DF/DHF cases showed a cyclical pattern with seasonal variation. SARIMA (0,0,1) (0,1,1) 12 model had the lowest normalized Bayesian information criteria (BIC) of 9.426 and mean absolute percentage error (MAPE) of 263.361 and appeared to be the best model. The proportion of variance explained by the model was 54.3%. Adequacy of the model was established through Ljung-Box test (Q statistic 4.910 and P-value 0.996), which showed no significant correlation between residuals at different lag times. The forecast for the year 2011 showed a seasonal peak in the month of October with an estimated 546 cases. Application of SARIMA model may be useful for forecast of cases and impending outbreaks of DF/DHF and other infectious diseases, which exhibit seasonal pattern.

Sleep analysis for wearable devices applying autoregressive parametric models.

PubMed

Mendez, M O; Villantieri, O; Bianchi, A; Cerutti, S

2005-01-01

We applied time-variant and time-invariant parametric models in both healthy subjects and patients with sleep disorder recordings in order to assess the skills of those approaches to sleep disorders diagnosis in wearable devices. The recordings present the Obstructive Sleep Apnea (OSA) pathology which is characterized by fluctuations in the heart rate, bradycardia in apneonic phase and tachycardia at the recovery of ventilation. Data come from a web database in www.physionet.org. During OSA the spectral indexes obtained by time-variant lattice filters presented oscillations that correspond to the changes brady-tachycardia of the RR intervals and greater values than healthy ones. Multivariate autoregressive models showed an increment in very low frequency component (PVLF) at each apneic event. Also a rise in high frequency component (PHF) occurred over the breathing restore in the spectrum of both quadratic coherence and cross-spectrum in OSA. These autoregressive parametric approaches could help in the diagnosis of Sleep Disorder inside of the wearable devices.

[A novel method of multi-channel feature extraction combining multivariate autoregression and multiple-linear principal component analysis].

PubMed

Wang, Jinjia; Zhang, Yanna

2015-02-01

Brain-computer interface (BCI) systems identify brain signals through extracting features from them. In view of the limitations of the autoregressive model feature extraction method and the traditional principal component analysis to deal with the multichannel signals, this paper presents a multichannel feature extraction method that multivariate autoregressive (MVAR) model combined with the multiple-linear principal component analysis (MPCA), and used for magnetoencephalography (MEG) signals and electroencephalograph (EEG) signals recognition. Firstly, we calculated the MVAR model coefficient matrix of the MEG/EEG signals using this method, and then reduced the dimensions to a lower one, using MPCA. Finally, we recognized brain signals by Bayes Classifier. The key innovation we introduced in our investigation showed that we extended the traditional single-channel feature extraction method to the case of multi-channel one. We then carried out the experiments using the data groups of IV-III and IV - I. The experimental results proved that the method proposed in this paper was feasible.

Time-Series Forecast Modeling on High-Bandwidth Network Measurements

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yoo, Wucherl; Sim, Alex

With the increasing number of geographically distributed scientific collaborations and the growing sizes of scientific data, it has become challenging for users to achieve the best possible network performance on a shared network. In this paper, we have developed a model to forecast expected bandwidth utilization on high-bandwidth wide area networks. The forecast model can improve the efficiency of the resource utilization and scheduling of data movements on high-bandwidth networks to accommodate ever increasing data volume for large-scale scientific data applications. A univariate time-series forecast model is developed with the Seasonal decomposition of Time series by Loess (STL) and themore » AutoRegressive Integrated Moving Average (ARIMA) on Simple Network Management Protocol (SNMP) path utilization measurement data. Compared with the traditional approach such as Box-Jenkins methodology to train the ARIMA model, our forecast model reduces computation time up to 92.6 %. It also shows resilience against abrupt network usage changes. Finally, our forecast model conducts the large number of multi-step forecast, and the forecast errors are within the mean absolute deviation (MAD) of the monitored measurements.« less

A spatial panel ordered-response model with application to the analysis of urban land-use development intensity patterns

NASA Astrophysics Data System (ADS)

Ferdous, Nazneen; Bhat, Chandra R.

2013-01-01

This paper proposes and estimates a spatial panel ordered-response probit model with temporal autoregressive error terms to analyze changes in urban land development intensity levels over time. Such a model structure maintains a close linkage between the land owner's decision (unobserved to the analyst) and the land development intensity level (observed by the analyst) and accommodates spatial interactions between land owners that lead to spatial spillover effects. In addition, the model structure incorporates spatial heterogeneity as well as spatial heteroscedasticity. The resulting model is estimated using a composite marginal likelihood (CML) approach that does not require any simulation machinery and that can be applied to data sets of any size. A simulation exercise indicates that the CML approach recovers the model parameters very well, even in the presence of high spatial and temporal dependence. In addition, the simulation results demonstrate that ignoring spatial dependency and spatial heterogeneity when both are actually present will lead to bias in parameter estimation. A demonstration exercise applies the proposed model to examine urban land development intensity levels using parcel-level data from Austin, Texas.

Time-Series Forecast Modeling on High-Bandwidth Network Measurements

DOE PAGES

Yoo, Wucherl; Sim, Alex

2016-06-24

With the increasing number of geographically distributed scientific collaborations and the growing sizes of scientific data, it has become challenging for users to achieve the best possible network performance on a shared network. In this paper, we have developed a model to forecast expected bandwidth utilization on high-bandwidth wide area networks. The forecast model can improve the efficiency of the resource utilization and scheduling of data movements on high-bandwidth networks to accommodate ever increasing data volume for large-scale scientific data applications. A univariate time-series forecast model is developed with the Seasonal decomposition of Time series by Loess (STL) and themore » AutoRegressive Integrated Moving Average (ARIMA) on Simple Network Management Protocol (SNMP) path utilization measurement data. Compared with the traditional approach such as Box-Jenkins methodology to train the ARIMA model, our forecast model reduces computation time up to 92.6 %. It also shows resilience against abrupt network usage changes. Finally, our forecast model conducts the large number of multi-step forecast, and the forecast errors are within the mean absolute deviation (MAD) of the monitored measurements.« less

Application of multivariate autoregressive spectrum estimation to ULF waves

NASA Technical Reports Server (NTRS)

Ioannidis, G. A.

1975-01-01

The estimation of the power spectrum of a time series by fitting a finite autoregressive model to the data has recently found widespread application in the physical sciences. The extension of this method to the analysis of vector time series is presented here through its application to ULF waves observed in the magnetosphere by the ATS 6 synchronous satellite. Autoregressive spectral estimates of the power and cross-power spectra of these waves are computed with computer programs developed by the author and are compared with the corresponding Blackman-Tukey spectral estimates. The resulting spectral density matrices are then analyzed to determine the direction of propagation and polarization of the observed waves.

Optimal HRF and smoothing parameters for fMRI time series within an autoregressive modeling framework.

PubMed

Galka, Andreas; Siniatchkin, Michael; Stephani, Ulrich; Groening, Kristina; Wolff, Stephan; Bosch-Bayard, Jorge; Ozaki, Tohru

2010-12-01

The analysis of time series obtained by functional magnetic resonance imaging (fMRI) may be approached by fitting predictive parametric models, such as nearest-neighbor autoregressive models with exogeneous input (NNARX). As a part of the modeling procedure, it is possible to apply instantaneous linear transformations to the data. Spatial smoothing, a common preprocessing step, may be interpreted as such a transformation. The autoregressive parameters may be constrained, such that they provide a response behavior that corresponds to the canonical haemodynamic response function (HRF). We present an algorithm for estimating the parameters of the linear transformations and of the HRF within a rigorous maximum-likelihood framework. Using this approach, an optimal amount of both the spatial smoothing and the HRF can be estimated simultaneously for a given fMRI data set. An example from a motor-task experiment is discussed. It is found that, for this data set, weak, but non-zero, spatial smoothing is optimal. Furthermore, it is demonstrated that activated regions can be estimated within the maximum-likelihood framework.

Principal dynamic mode analysis of neural mass model for the identification of epileptic states

NASA Astrophysics Data System (ADS)

Cao, Yuzhen; Jin, Liu; Su, Fei; Wang, Jiang; Deng, Bin

2016-11-01

The detection of epileptic seizures in Electroencephalography (EEG) signals is significant for the diagnosis and treatment of epilepsy. In this paper, in order to obtain characteristics of various epileptiform EEGs that may differentiate different states of epilepsy, the concept of Principal Dynamic Modes (PDMs) was incorporated to an autoregressive model framework. First, the neural mass model was used to simulate the required intracerebral EEG signals of various epileptiform activities. Then, the PDMs estimated from the nonlinear autoregressive Volterra models, as well as the corresponding Associated Nonlinear Functions (ANFs), were used for the modeling of epileptic EEGs. The efficient PDM modeling approach provided physiological interpretation of the system. Results revealed that the ANFs of the 1st and 2nd PDMs for the auto-regressive input exhibited evident differences among different states of epilepsy, where the ANFs of the sustained spikes' activity encountered at seizure onset or during a seizure were the most differentiable from that of the normal state. Therefore, the ANFs may be characteristics for the classification of normal and seizure states in the clinical detection of seizures and thus provide assistance for the diagnosis of epilepsy.

Texture classification using autoregressive filtering

NASA Technical Reports Server (NTRS)

Lawton, W. M.; Lee, M.

1984-01-01

A general theory of image texture models is proposed and its applicability to the problem of scene segmentation using texture classification is discussed. An algorithm, based on half-plane autoregressive filtering, which optimally utilizes second order statistics to discriminate between texture classes represented by arbitrary wide sense stationary random fields is described. Empirical results of applying this algorithm to natural and sysnthesized scenes are presented and future research is outlined.

Verification of ARMA identification for modelling temporal correlation of GPS observations using the toolbox ARMASA

NASA Astrophysics Data System (ADS)

Luo, Xiaoguang; Mayer, Michael; Heck, Bernhard

2010-05-01

One essential deficiency of the stochastic model used in many GNSS (Global Navigation Satellite Systems) software products consists in neglecting temporal correlation of GNSS observations. Analysing appropriately detrended time series of observation residuals resulting from GPS (Global Positioning System) data processing, the temporal correlation behaviour of GPS observations can be sufficiently described by means of so-called autoregressive moving average (ARMA) processes. Using the toolbox ARMASA which is available free of charge in MATLAB® Central (open exchange platform for the MATLAB® and SIMULINK® user community), a well-fitting time series model can be identified automatically in three steps. Firstly, AR, MA, and ARMA models are computed up to some user-specified maximum order. Subsequently, for each model type, the best-fitting model is selected using the combined (for AR processes) resp. generalised (for MA and ARMA processes) information criterion. The final model identification among the best-fitting AR, MA, and ARMA models is performed based on the minimum prediction error characterising the discrepancies between the given data and the fitted model. The ARMA coefficients are computed using Burg's maximum entropy algorithm (for AR processes), Durbin's first (for MA processes) and second (for ARMA processes) methods, respectively. This paper verifies the performance of the automated ARMA identification using the toolbox ARMASA. For this purpose, a representative data base is generated by means of ARMA simulation with respect to sample size, correlation level, and model complexity. The model error defined as a transform of the prediction error is used as measure for the deviation between the true and the estimated model. The results of the study show that the recognition rates of underlying true processes increase with increasing sample sizes and decrease with rising model complexity. Considering large sample sizes, the true underlying processes can be correctly recognised for nearly 80% of the analysed data sets. Additionally, the model errors of first-order AR resp. MA processes converge clearly more rapidly to the corresponding asymptotical values than those of high-order ARMA processes.

Modelling subject-specific childhood growth using linear mixed-effect models with cubic regression splines.

PubMed

Grajeda, Laura M; Ivanescu, Andrada; Saito, Mayuko; Crainiceanu, Ciprian; Jaganath, Devan; Gilman, Robert H; Crabtree, Jean E; Kelleher, Dermott; Cabrera, Lilia; Cama, Vitaliano; Checkley, William

2016-01-01

Childhood growth is a cornerstone of pediatric research. Statistical models need to consider individual trajectories to adequately describe growth outcomes. Specifically, well-defined longitudinal models are essential to characterize both population and subject-specific growth. Linear mixed-effect models with cubic regression splines can account for the nonlinearity of growth curves and provide reasonable estimators of population and subject-specific growth, velocity and acceleration. We provide a stepwise approach that builds from simple to complex models, and account for the intrinsic complexity of the data. We start with standard cubic splines regression models and build up to a model that includes subject-specific random intercepts and slopes and residual autocorrelation. We then compared cubic regression splines vis-à-vis linear piecewise splines, and with varying number of knots and positions. Statistical code is provided to ensure reproducibility and improve dissemination of methods. Models are applied to longitudinal height measurements in a cohort of 215 Peruvian children followed from birth until their fourth year of life. Unexplained variability, as measured by the variance of the regression model, was reduced from 7.34 when using ordinary least squares to 0.81 (p < 0.001) when using a linear mixed-effect models with random slopes and a first order continuous autoregressive error term. There was substantial heterogeneity in both the intercept (p < 0.001) and slopes (p < 0.001) of the individual growth trajectories. We also identified important serial correlation within the structure of the data (ρ = 0.66; 95 % CI 0.64 to 0.68; p < 0.001), which we modeled with a first order continuous autoregressive error term as evidenced by the variogram of the residuals and by a lack of association among residuals. The final model provides a parametric linear regression equation for both estimation and prediction of population- and individual-level growth in height. We show that cubic regression splines are superior to linear regression splines for the case of a small number of knots in both estimation and prediction with the full linear mixed effect model (AIC 19,352 vs. 19,598, respectively). While the regression parameters are more complex to interpret in the former, we argue that inference for any problem depends more on the estimated curve or differences in curves rather than the coefficients. Moreover, use of cubic regression splines provides biological meaningful growth velocity and acceleration curves despite increased complexity in coefficient interpretation. Through this stepwise approach, we provide a set of tools to model longitudinal childhood data for non-statisticians using linear mixed-effect models.

An efficient approach to ARMA modeling of biological systems with multiple inputs and delays

NASA Technical Reports Server (NTRS)

Perrott, M. H.; Cohen, R. J.

1996-01-01

This paper presents a new approach to AutoRegressive Moving Average (ARMA or ARX) modeling which automatically seeks the best model order to represent investigated linear, time invariant systems using their input/output data. The algorithm seeks the ARMA parameterization which accounts for variability in the output of the system due to input activity and contains the fewest number of parameters required to do so. The unique characteristics of the proposed system identification algorithm are its simplicity and efficiency in handling systems with delays and multiple inputs. We present results of applying the algorithm to simulated data and experimental biological data In addition, a technique for assessing the error associated with the impulse responses calculated from estimated ARMA parameterizations is presented. The mapping from ARMA coefficients to impulse response estimates is nonlinear, which complicates any effort to construct confidence bounds for the obtained impulse responses. Here a method for obtaining a linearization of this mapping is derived, which leads to a simple procedure to approximate the confidence bounds.

Testing the EKC hypothesis by considering trade openness, urbanization, and financial development: the case of Turkey.

PubMed

Ozatac, Nesrin; Gokmenoglu, Korhan K; Taspinar, Nigar

2017-07-01

This study investigates the environmental Kuznets curve (EKC) hypothesis for the case of Turkey from 1960 to 2013 by considering energy consumption, trade, urbanization, and financial development variables. Although previous literature examines various aspects of the EKC hypothesis for the case of Turkey, our model augments the basic model with several covariates to develop a better understanding of the relationship among the variables and to refrain from omitted variable bias. The results of the bounds test and the error correction model under autoregressive distributed lag mechanism suggest long-run relationships among the variables as well as proof of the EKC and the scale effect in Turkey. A conditional Granger causality test reveals that there are causal relationships among the variables. Our findings can have policy implications including the imposition of a "polluter pays" mechanism, such as the implementation of a carbon tax for pollution trading, to raise the urban population's awareness about the importance of adopting renewable energy and to support clean, environmentally friendly technology.

Intelligent Ensemble Forecasting System of Stock Market Fluctuations Based on Symetric and Asymetric Wavelet Functions

NASA Astrophysics Data System (ADS)

Lahmiri, Salim; Boukadoum, Mounir

2015-08-01

We present a new ensemble system for stock market returns prediction where continuous wavelet transform (CWT) is used to analyze return series and backpropagation neural networks (BPNNs) for processing CWT-based coefficients, determining the optimal ensemble weights, and providing final forecasts. Particle swarm optimization (PSO) is used for finding optimal weights and biases for each BPNN. To capture symmetry/asymmetry in the underlying data, three wavelet functions with different shapes are adopted. The proposed ensemble system was tested on three Asian stock markets: The Hang Seng, KOSPI, and Taiwan stock market data. Three statistical metrics were used to evaluate the forecasting accuracy; including, mean of absolute errors (MAE), root mean of squared errors (RMSE), and mean of absolute deviations (MADs). Experimental results showed that our proposed ensemble system outperformed the individual CWT-ANN models each with different wavelet function. In addition, the proposed ensemble system outperformed the conventional autoregressive moving average process. As a result, the proposed ensemble system is suitable to capture symmetry/asymmetry in financial data fluctuations for better prediction accuracy.

Statistical process control of mortality series in the Australian and New Zealand Intensive Care Society (ANZICS) adult patient database: implications of the data generating process

PubMed Central

2013-01-01

Background Statistical process control (SPC), an industrial sphere initiative, has recently been applied in health care and public health surveillance. SPC methods assume independent observations and process autocorrelation has been associated with increase in false alarm frequency. Methods Monthly mean raw mortality (at hospital discharge) time series, 1995–2009, at the individual Intensive Care unit (ICU) level, were generated from the Australia and New Zealand Intensive Care Society adult patient database. Evidence for series (i) autocorrelation and seasonality was demonstrated using (partial)-autocorrelation ((P)ACF) function displays and classical series decomposition and (ii) “in-control” status was sought using risk-adjusted (RA) exponentially weighted moving average (EWMA) control limits (3 sigma). Risk adjustment was achieved using a random coefficient (intercept as ICU site and slope as APACHE III score) logistic regression model, generating an expected mortality series. Application of time-series to an exemplar complete ICU series (1995-(end)2009) was via Box-Jenkins methodology: autoregressive moving average (ARMA) and (G)ARCH ((Generalised) Autoregressive Conditional Heteroscedasticity) models, the latter addressing volatility of the series variance. Results The overall data set, 1995-2009, consisted of 491324 records from 137 ICU sites; average raw mortality was 14.07%; average(SD) raw and expected mortalities ranged from 0.012(0.113) and 0.013(0.045) to 0.296(0.457) and 0.278(0.247) respectively. For the raw mortality series: 71 sites had continuous data for assessment up to or beyond lag40 and 35% had autocorrelation through to lag40; and of 36 sites with continuous data for ≥ 72 months, all demonstrated marked seasonality. Similar numbers and percentages were seen with the expected series. Out-of-control signalling was evident for the raw mortality series with respect to RA-EWMA control limits; a seasonal ARMA model, with GARCH effects, displayed white-noise residuals which were in-control with respect to EWMA control limits and one-step prediction error limits (3SE). The expected series was modelled with a multiplicative seasonal autoregressive model. Conclusions The data generating process of monthly raw mortality series at the ICU level displayed autocorrelation, seasonality and volatility. False-positive signalling of the raw mortality series was evident with respect to RA-EWMA control limits. A time series approach using residual control charts resolved these issues. PMID:23705957

Application of a Hybrid Model for Predicting the Incidence of Tuberculosis in Hubei, China

PubMed Central

Zhang, Guoliang; Huang, Shuqiong; Duan, Qionghong; Shu, Wen; Hou, Yongchun; Zhu, Shiyu; Miao, Xiaoping; Nie, Shaofa; Wei, Sheng; Guo, Nan; Shan, Hua; Xu, Yihua

2013-01-01

Background A prediction model for tuberculosis incidence is needed in China which may be used as a decision-supportive tool for planning health interventions and allocating health resources. Methods The autoregressive integrated moving average (ARIMA) model was first constructed with the data of tuberculosis report rate in Hubei Province from Jan 2004 to Dec 2011.The data from Jan 2012 to Jun 2012 were used to validate the model. Then the generalized regression neural network (GRNN)-ARIMA combination model was established based on the constructed ARIMA model. Finally, the fitting and prediction accuracy of the two models was evaluated. Results A total of 465,960 cases were reported between Jan 2004 and Dec 2011 in Hubei Province. The report rate of tuberculosis was highest in 2005 (119.932 per 100,000 population) and lowest in 2010 (84.724 per 100,000 population). The time series of tuberculosis report rate show a gradual secular decline and a striking seasonal variation. The ARIMA (2, 1, 0) × (0, 1, 1)12 model was selected from several plausible ARIMA models. The residual mean square error of the GRNN-ARIMA model and ARIMA model were 0.4467 and 0.6521 in training part, and 0.0958 and 0.1133 in validation part, respectively. The mean absolute error and mean absolute percentage error of the hybrid model were also less than the ARIMA model. Discussion and Conclusions The gradual decline in tuberculosis report rate may be attributed to the effect of intensive measures on tuberculosis. The striking seasonal variation may have resulted from several factors. We suppose that a delay in the surveillance system may also have contributed to the variation. According to the fitting and prediction accuracy, the hybrid model outperforms the traditional ARIMA model, which may facilitate the allocation of health resources in China. PMID:24223232

Short-term forecasting of electric loads using nonlinear autoregressive artificial neural networks with exogenous vector inputs

DOE PAGES

Buitrago, Jaime; Asfour, Shihab

2017-01-01

Short-term load forecasting is crucial for the operations planning of an electrical grid. Forecasting the next 24 h of electrical load in a grid allows operators to plan and optimize their resources. The purpose of this study is to develop a more accurate short-term load forecasting method utilizing non-linear autoregressive artificial neural networks (ANN) with exogenous multi-variable input (NARX). The proposed implementation of the network is new: the neural network is trained in open-loop using actual load and weather data, and then, the network is placed in closed-loop to generate a forecast using the predicted load as the feedback input.more » Unlike the existing short-term load forecasting methods using ANNs, the proposed method uses its own output as the input in order to improve the accuracy, thus effectively implementing a feedback loop for the load, making it less dependent on external data. Using the proposed framework, mean absolute percent errors in the forecast in the order of 1% have been achieved, which is a 30% improvement on the average error using feedforward ANNs, ARMAX and state space methods, which can result in large savings by avoiding commissioning of unnecessary power plants. Finally, the New England electrical load data are used to train and validate the forecast prediction.« less

Short-term forecasting of electric loads using nonlinear autoregressive artificial neural networks with exogenous vector inputs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Buitrago, Jaime; Asfour, Shihab

Short-term load forecasting is crucial for the operations planning of an electrical grid. Forecasting the next 24 h of electrical load in a grid allows operators to plan and optimize their resources. The purpose of this study is to develop a more accurate short-term load forecasting method utilizing non-linear autoregressive artificial neural networks (ANN) with exogenous multi-variable input (NARX). The proposed implementation of the network is new: the neural network is trained in open-loop using actual load and weather data, and then, the network is placed in closed-loop to generate a forecast using the predicted load as the feedback input.more » Unlike the existing short-term load forecasting methods using ANNs, the proposed method uses its own output as the input in order to improve the accuracy, thus effectively implementing a feedback loop for the load, making it less dependent on external data. Using the proposed framework, mean absolute percent errors in the forecast in the order of 1% have been achieved, which is a 30% improvement on the average error using feedforward ANNs, ARMAX and state space methods, which can result in large savings by avoiding commissioning of unnecessary power plants. Finally, the New England electrical load data are used to train and validate the forecast prediction.« less

Queuing Time Prediction Using WiFi Positioning Data in an Indoor Scenario.

PubMed

Shu, Hua; Song, Ci; Pei, Tao; Xu, Lianming; Ou, Yang; Zhang, Libin; Li, Tao

2016-11-22

Queuing is common in urban public places. Automatically monitoring and predicting queuing time can not only help individuals to reduce their wait time and alleviate anxiety but also help managers to allocate resources more efficiently and enhance their ability to address emergencies. This paper proposes a novel method to estimate and predict queuing time in indoor environments based on WiFi positioning data. First, we use a series of parameters to identify the trajectories that can be used as representatives of queuing time. Next, we divide the day into equal time slices and estimate individuals' average queuing time during specific time slices. Finally, we build a nonstandard autoregressive (NAR) model trained using the previous day's WiFi estimation results and actual queuing time to predict the queuing time in the upcoming time slice. A case study comparing two other time series analysis models shows that the NAR model has better precision. Random topological errors caused by the drift phenomenon of WiFi positioning technology (locations determined by a WiFi positioning system may drift accidently) and systematic topological errors caused by the positioning system are the main factors that affect the estimation precision. Therefore, we optimize the deployment strategy during the positioning system deployment phase and propose a drift ratio parameter pertaining to the trajectory screening phase to alleviate the impact of topological errors and improve estimates. The WiFi positioning data from an eight-day case study conducted at the T3-C entrance of Beijing Capital International Airport show that the mean absolute estimation error is 147 s, which is approximately 26.92% of the actual queuing time. For predictions using the NAR model, the proportion is approximately 27.49%. The theoretical predictions and the empirical case study indicate that the NAR model is an effective method to estimate and predict queuing time in indoor public areas.

Queuing Time Prediction Using WiFi Positioning Data in an Indoor Scenario

PubMed Central

Shu, Hua; Song, Ci; Pei, Tao; Xu, Lianming; Ou, Yang; Zhang, Libin; Li, Tao

2016-01-01

Queuing is common in urban public places. Automatically monitoring and predicting queuing time can not only help individuals to reduce their wait time and alleviate anxiety but also help managers to allocate resources more efficiently and enhance their ability to address emergencies. This paper proposes a novel method to estimate and predict queuing time in indoor environments based on WiFi positioning data. First, we use a series of parameters to identify the trajectories that can be used as representatives of queuing time. Next, we divide the day into equal time slices and estimate individuals’ average queuing time during specific time slices. Finally, we build a nonstandard autoregressive (NAR) model trained using the previous day’s WiFi estimation results and actual queuing time to predict the queuing time in the upcoming time slice. A case study comparing two other time series analysis models shows that the NAR model has better precision. Random topological errors caused by the drift phenomenon of WiFi positioning technology (locations determined by a WiFi positioning system may drift accidently) and systematic topological errors caused by the positioning system are the main factors that affect the estimation precision. Therefore, we optimize the deployment strategy during the positioning system deployment phase and propose a drift ratio parameter pertaining to the trajectory screening phase to alleviate the impact of topological errors and improve estimates. The WiFi positioning data from an eight-day case study conducted at the T3-C entrance of Beijing Capital International Airport show that the mean absolute estimation error is 147 s, which is approximately 26.92% of the actual queuing time. For predictions using the NAR model, the proportion is approximately 27.49%. The theoretical predictions and the empirical case study indicate that the NAR model is an effective method to estimate and predict queuing time in indoor public areas. PMID:27879663

Hydrological time series modeling: A comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques

NASA Astrophysics Data System (ADS)

Lohani, A. K.; Kumar, Rakesh; Singh, R. D.

2012-06-01

SummaryTime series modeling is necessary for the planning and management of reservoirs. More recently, the soft computing techniques have been used in hydrological modeling and forecasting. In this study, the potential of artificial neural networks and neuro-fuzzy system in monthly reservoir inflow forecasting are examined by developing and comparing monthly reservoir inflow prediction models, based on autoregressive (AR), artificial neural networks (ANNs) and adaptive neural-based fuzzy inference system (ANFIS). To take care the effect of monthly periodicity in the flow data, cyclic terms are also included in the ANN and ANFIS models. Working with time series flow data of the Sutlej River at Bhakra Dam, India, several ANN and adaptive neuro-fuzzy models are trained with different input vectors. To evaluate the performance of the selected ANN and adaptive neural fuzzy inference system (ANFIS) models, comparison is made with the autoregressive (AR) models. The ANFIS model trained with the input data vector including previous inflows and cyclic terms of monthly periodicity has shown a significant improvement in the forecast accuracy in comparison with the ANFIS models trained with the input vectors considering only previous inflows. In all cases ANFIS gives more accurate forecast than the AR and ANN models. The proposed ANFIS model coupled with the cyclic terms is shown to provide better representation of the monthly inflow forecasting for planning and operation of reservoir.

Performance of stochastic approaches for forecasting river water quality.

PubMed

Ahmad, S; Khan, I H; Parida, B P

2001-12-01

This study analysed water quality data collected from the river Ganges in India from 1981 to 1990 for forecasting using stochastic models. Initially the box and whisker plots and Kendall's tau test were used to identify the trends during the study period. For detecting the possible intervention in the data the time series plots and cusum charts were used. The three approaches of stochastic modelling which account for the effect of seasonality in different ways. i.e. multiplicative autoregressive integrated moving average (ARIMA) model. deseasonalised model and Thomas-Fiering model were used to model the observed pattern in water quality. The multiplicative ARIMA model having both nonseasonal and seasonal components were, in general, identified as appropriate models. In the deseasonalised modelling approach, the lower order ARIMA models were found appropriate for the stochastic component. The set of Thomas-Fiering models were formed for each month for all water quality parameters. These models were then used to forecast the future values. The error estimates of forecasts from the three approaches were compared to identify the most suitable approach for the reliable forecast. The deseasonalised modelling approach was recommended for forecasting of water quality parameters of a river.

Atmospheric mold spore counts in relation to meteorological parameters

NASA Astrophysics Data System (ADS)

Katial, R. K.; Zhang, Yiming; Jones, Richard H.; Dyer, Philip D.

Fungal spore counts of Cladosporium, Alternaria, and Epicoccum were studied during 8 years in Denver, Colorado. Fungal spore counts were obtained daily during the pollinating season by a Rotorod sampler. Weather data were obtained from the National Climatic Data Center. Daily averages of temperature, relative humidity, daily precipitation, barometric pressure, and wind speed were studied. A time series analysis was performed on the data to mathematically model the spore counts in relation to weather parameters. Using SAS PROC ARIMA software, a regression analysis was performed, regressing the spore counts on the weather variables assuming an autoregressive moving average (ARMA) error structure. Cladosporium was found to be positively correlated (P<0.02) with average daily temperature, relative humidity, and negatively correlated with precipitation. Alternaria and Epicoccum did not show increased predictability with weather variables. A mathematical model was derived for Cladosporium spore counts using the annual seasonal cycle and significant weather variables. The model for Alternaria and Epicoccum incorporated the annual seasonal cycle. Fungal spore counts can be modeled by time series analysis and related to meteorological parameters controlling for seasonallity; this modeling can provide estimates of exposure to fungal aeroallergens.

A temporal and spatial analysis of ground-water levels for effective monitoring in Huron County, Michigan

USGS Publications Warehouse

Holtschlag, David J.; Sweat, M.J.

1999-01-01

Quarterly water-level measurements were analyzed to assess the effectiveness of a monitoring network of 26 wells in Huron County, Michigan. Trends were identified as constant levels and autoregressive components were computed at all wells on the basis of data collected from 1993 to 1997, using structural time series analysis. Fixed seasonal components were identified at 22 wells and outliers were identified at 23 wells. The 95- percent confidence intervals were forecast for water-levels during the first and second quarters of 1998. Intervals in the first quarter were consistent with 92.3 percent of the measured values. In the second quarter, measured values were within the forecast intervals only 65.4 percent of the time. Unusually low precipitation during the second quarter is thought to have contributed to the reduced reliability of the second-quarter forecasts. Spatial interrelations among wells were investigated on the basis of the autoregressive components, which were filtered to create a set of innovation sequences that were temporally uncorrelated. The empirical covariance among the innovation sequences indicated both positive and negative spatial interrelations. The negative covariance components are considered to be physically implausible and to have resulted from random sampling error. Graphical modeling, a form of multivariate analysis, was used to model the covariance structure. Results indicate that only 29 of the 325 possible partial correlations among the water-level innovations were statistically significant. The model covariance matrix, corresponding to the model partial correlation structure, contained only positive elements. This model covariance was sequentially partitioned to compute a set of partial covariance matrices that were used to rank the effectiveness of the 26 monitoring wells from greatest to least. Results, for example, indicate that about 50 percent of the uncertainty of the water-level innovations currently monitored by the 26- well network could be described by the 6 most effective wells.

A comparison of multivariate and univariate time series approaches to modelling and forecasting emergency department demand in Western Australia.

PubMed

Aboagye-Sarfo, Patrick; Mai, Qun; Sanfilippo, Frank M; Preen, David B; Stewart, Louise M; Fatovich, Daniel M

2015-10-01

To develop multivariate vector-ARMA (VARMA) forecast models for predicting emergency department (ED) demand in Western Australia (WA) and compare them to the benchmark univariate autoregressive moving average (ARMA) and Winters' models. Seven-year monthly WA state-wide public hospital ED presentation data from 2006/07 to 2012/13 were modelled. Graphical and VARMA modelling methods were used for descriptive analysis and model fitting. The VARMA models were compared to the benchmark univariate ARMA and Winters' models to determine their accuracy to predict ED demand. The best models were evaluated by using error correction methods for accuracy. Descriptive analysis of all the dependent variables showed an increasing pattern of ED use with seasonal trends over time. The VARMA models provided a more precise and accurate forecast with smaller confidence intervals and better measures of accuracy in predicting ED demand in WA than the ARMA and Winters' method. VARMA models are a reliable forecasting method to predict ED demand for strategic planning and resource allocation. While the ARMA models are a closely competing alternative, they under-estimated future ED demand. Copyright © 2015 Elsevier Inc. All rights reserved.

Importance of spatial autocorrelation in modeling bird distributions at a continental scale

USGS Publications Warehouse

Bahn, V.; O'Connor, R.J.; Krohn, W.B.

2006-01-01

Spatial autocorrelation in species' distributions has been recognized as inflating the probability of a type I error in hypotheses tests, causing biases in variable selection, and violating the assumption of independence of error terms in models such as correlation or regression. However, it remains unclear whether these problems occur at all spatial resolutions and extents, and under which conditions spatially explicit modeling techniques are superior. Our goal was to determine whether spatial models were superior at large extents and across many different species. In addition, we investigated the importance of purely spatial effects in distribution patterns relative to the variation that could be explained through environmental conditions. We studied distribution patterns of 108 bird species in the conterminous United States using ten years of data from the Breeding Bird Survey. We compared the performance of spatially explicit regression models with non-spatial regression models using Akaike's information criterion. In addition, we partitioned the variance in species distributions into an environmental, a pure spatial and a shared component. The spatially-explicit conditional autoregressive regression models strongly outperformed the ordinary least squares regression models. In addition, partialling out the spatial component underlying the species' distributions showed that an average of 17% of the explained variation could be attributed to purely spatial effects independent of the spatial autocorrelation induced by the underlying environmental variables. We concluded that location in the range and neighborhood play an important role in the distribution of species. Spatially explicit models are expected to yield better predictions especially for mobile species such as birds, even in coarse-grained models with a large extent. ?? Ecography.

Using autoregressive integrated moving average (ARIMA) models to predict and monitor the number of beds occupied during a SARS outbreak in a tertiary hospital in Singapore.

PubMed

Earnest, Arul; Chen, Mark I; Ng, Donald; Sin, Leo Yee

2005-05-11

The main objective of this study is to apply autoregressive integrated moving average (ARIMA) models to make real-time predictions on the number of beds occupied in Tan Tock Seng Hospital, during the recent SARS outbreak. This is a retrospective study design. Hospital admission and occupancy data for isolation beds was collected from Tan Tock Seng hospital for the period 14th March 2003 to 31st May 2003. The main outcome measure was daily number of isolation beds occupied by SARS patients. Among the covariates considered were daily number of people screened, daily number of people admitted (including observation, suspect and probable cases) and days from the most recent significant event discovery. We utilized the following strategy for the analysis. Firstly, we split the outbreak data into two. Data from 14th March to 21st April 2003 was used for model development. We used structural ARIMA models in an attempt to model the number of beds occupied. Estimation is via the maximum likelihood method using the Kalman filter. For the ARIMA model parameters, we considered the simplest parsimonious lowest order model. We found that the ARIMA (1,0,3) model was able to describe and predict the number of beds occupied during the SARS outbreak well. The mean absolute percentage error (MAPE) for the training set and validation set were 5.7% and 8.6% respectively, which we found was reasonable for use in the hospital setting. Furthermore, the model also provided three-day forecasts of the number of beds required. Total number of admissions and probable cases admitted on the previous day were also found to be independent prognostic factors of bed occupancy. ARIMA models provide useful tools for administrators and clinicians in planning for real-time bed capacity during an outbreak of an infectious disease such as SARS. The model could well be used in planning for bed-capacity during outbreaks of other infectious diseases as well.

New Approach To Hour-By-Hour Weather Forecast

NASA Astrophysics Data System (ADS)

Liao, Q. Q.; Wang, B.

2017-12-01

Fine hourly forecast in single station weather forecast is required in many human production and life application situations. Most previous MOS (Model Output Statistics) which used a linear regression model are hard to solve nonlinear natures of the weather prediction and forecast accuracy has not been sufficient at high temporal resolution. This study is to predict the future meteorological elements including temperature, precipitation, relative humidity and wind speed in a local region over a relatively short period of time at hourly level. By means of hour-to-hour NWP (Numeral Weather Prediction)meteorological field from Forcastio (https://darksky.net/dev/docs/forecast) and real-time instrumental observation including 29 stations in Yunnan and 3 stations in Tianjin of China from June to October 2016, predictions are made of the 24-hour hour-by-hour ahead. This study presents an ensemble approach to combine the information of instrumental observation itself and NWP. Use autoregressive-moving-average (ARMA) model to predict future values of the observation time series. Put newest NWP products into the equations derived from the multiple linear regression MOS technique. Handle residual series of MOS outputs with autoregressive (AR) model for the linear property presented in time series. Due to the complexity of non-linear property of atmospheric flow, support vector machine (SVM) is also introduced . Therefore basic data quality control and cross validation makes it able to optimize the model function parameters , and do 24 hours ahead residual reduction with AR/SVM model. Results show that AR model technique is better than corresponding multi-variant MOS regression method especially at the early 4 hours when the predictor is temperature. MOS-AR combined model which is comparable to MOS-SVM model outperform than MOS. Both of their root mean square error and correlation coefficients for 2 m temperature are reduced to 1.6 degree Celsius and 0.91 respectively. The forecast accuracy of 24- hour forecast deviation no more than 2 degree Celsius is 78.75 % for MOS-AR model and 81.23 % for AR model.

Analysis of the Westland Data Set

NASA Technical Reports Server (NTRS)

Wen, Fang; Willett, Peter; Deb, Somnath

2001-01-01

The "Westland" set of empirical accelerometer helicopter data with seeded and labeled faults is analyzed with the aim of condition monitoring. The autoregressive (AR) coefficients from a simple linear model encapsulate a great deal of information in a relatively few measurements; and it has also been found that augmentation of these by harmonic and other parameters call improve classification significantly. Several techniques have been explored, among these restricted Coulomb energy (RCE) networks, learning vector quantization (LVQ), Gaussian mixture classifiers and decision trees. A problem with these approaches, and in common with many classification paradigms, is that augmentation of the feature dimension can degrade classification ability. Thus, we also introduce the Bayesian data reduction algorithm (BDRA), which imposes a Dirichlet prior oil training data and is thus able to quantify probability of error in all exact manner, such that features may be discarded or coarsened appropriately.

Utilization of Model Predictive Control to Balance Power Absorption Against Load Accumulation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Abbas, Nikhar; Tom, Nathan M

2017-06-03

Wave energy converter (WEC) control strategies have been primarily focused on maximizing power absorption. The use of model predictive control strategies allows for a finite-horizon, multiterm objective function to be solved. This work utilizes a multiterm objective function to maximize power absorption while minimizing the structural loads on the WEC system. Furthermore, a Kalman filter and autoregressive model were used to estimate and forecast the wave exciting force and predict the future dynamics of the WEC. The WEC's power-take-off time-averaged power and structural loads under a perfect forecast assumption in irregular waves were compared against results obtained from the Kalmanmore » filter and autoregressive model to evaluate model predictive control performance.« less

Utilization of Model Predictive Control to Balance Power Absorption Against Load Accumulation: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Abbas, Nikhar; Tom, Nathan

Wave energy converter (WEC) control strategies have been primarily focused on maximizing power absorption. The use of model predictive control strategies allows for a finite-horizon, multiterm objective function to be solved. This work utilizes a multiterm objective function to maximize power absorption while minimizing the structural loads on the WEC system. Furthermore, a Kalman filter and autoregressive model were used to estimate and forecast the wave exciting force and predict the future dynamics of the WEC. The WEC's power-take-off time-averaged power and structural loads under a perfect forecast assumption in irregular waves were compared against results obtained from the Kalmanmore » filter and autoregressive model to evaluate model predictive control performance.« less

On-line algorithms for forecasting hourly loads of an electric utility

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vemuri, S.; Huang, W.L.; Nelson, D.J.

A method that lends itself to on-line forecasting of hourly electric loads is presented, and the results of its use are compared to models developed using the Box-Jenkins method. The method consits of processing the historical hourly loads with a sequential least-squares estimator to identify a finite-order autoregressive model which, in turn, is used to obtain a parsimonious autoregressive-moving average model. The method presented has several advantages in comparison with the Box-Jenkins method including much-less human intervention, improved model identification, and better results. The method is also more robust in that greater confidence can be placed in the accuracy ofmore » models based upon the various measures available at the identification stage.« less

Do alcohol excise taxes affect traffic accidents? Evidence from Estonia.

PubMed

Saar, Indrek

2015-01-01

This article examines the association between alcohol excise tax rates and alcohol-related traffic accidents in Estonia. Monthly time series of traffic accidents involving drunken motor vehicle drivers from 1998 through 2013 were regressed on real average alcohol excise tax rates while controlling for changes in economic conditions and the traffic environment. Specifically, regression models with autoregressive integrated moving average (ARIMA) errors were estimated in order to deal with serial correlation in residuals. Counterfactual models were also estimated in order to check the robustness of the results, using the level of non-alcohol-related traffic accidents as a dependent variable. A statistically significant (P <.01) strong negative relationship between the real average alcohol excise tax rate and alcohol-related traffic accidents was disclosed under alternative model specifications. For instance, the regression model with ARIMA (0, 1, 1)(0, 1, 1) errors revealed that a 1-unit increase in the tax rate is associated with a 1.6% decrease in the level of accidents per 100,000 population involving drunk motor vehicle drivers. No similar association was found in the cases of counterfactual models for non-alcohol-related traffic accidents. This article indicates that the level of alcohol-related traffic accidents in Estonia has been affected by changes in real average alcohol excise taxes during the period 1998-2013. Therefore, in addition to other measures, the use of alcohol taxation is warranted as a policy instrument in tackling alcohol-related traffic accidents.

Microenvironment temperature prediction between body and seat interface using autoregressive data-driven model.

PubMed

Liu, Zhuofu; Wang, Lin; Luo, Zhongming; Heusch, Andrew I; Cascioli, Vincenzo; McCarthy, Peter W

2015-11-01

There is a need to develop a greater understanding of temperature at the skin-seat interface during prolonged seating from the perspectives of both industrial design (comfort/discomfort) and medical care (skin ulcer formation). Here we test the concept of predicting temperature at the seat surface and skin interface during prolonged sitting (such as required from wheelchair users). As caregivers are usually busy, such a method would give them warning ahead of a problem. This paper describes a data-driven model capable of predicting thermal changes and thus having the potential to provide an early warning (15- to 25-min ahead prediction) of an impending temperature that may increase the risk for potential skin damages for those subject to enforced sitting and who have little or no sensory feedback from this area. Initially, the oscillations of the original signal are suppressed using the reconstruction strategy of empirical mode decomposition (EMD). Consequentially, the autoregressive data-driven model can be used to predict future thermal trends based on a shorter period of acquisition, which reduces the possibility of introducing human errors and artefacts associated with longer duration "enforced" sitting by volunteers. In this study, the method had a maximum predictive error of <0.4 °C when used to predict the temperature at the seat and skin interface 15 min ahead, but required 45 min data prior to give this accuracy. Although the 45 min front loading of data appears large (in proportion to the 15 min prediction), a relative strength derives from the fact that the same algorithm could be used on the other 4 sitting datasets created by the same individual, suggesting that the period of 45 min required to train the algorithm is transferable to other data from the same individual. This approach might be developed (along with incorporation of other measures such as movement and humidity) into a system that can give caregivers prior warning to help avoid exacerbating the skin disorders of patients who suffer from low body insensitivity and disability requiring them to be immobile in seats for prolonged periods. Copyright © 2015 Tissue Viability Society. Published by Elsevier Ltd. All rights reserved.

Vibration based condition monitoring of a multistage epicyclic gearbox in lifting cranes

NASA Astrophysics Data System (ADS)

Assaad, Bassel; Eltabach, Mario; Antoni, Jérôme

2014-01-01

This paper proposes a model-based technique for detecting wear in a multistage planetary gearbox used by lifting cranes. The proposed method establishes a vibration signal model which deals with cyclostationary and autoregressive models. First-order cyclostationarity is addressed by the analysis of the time synchronous average (TSA) of the angular resampled vibration signal. Then an autoregressive model (AR) is applied to the TSA part in order to extract a residual signal containing pertinent fault signatures. The paper also explores a number of methods commonly used in vibration monitoring of planetary gearboxes, in order to make comparisons. In the experimental part of this study, these techniques are applied to accelerated lifetime test bench data for the lifting winch. After processing raw signals recorded with an accelerometer mounted on the outside of the gearbox, a number of condition indicators (CIs) are derived from the TSA signal, the residual autoregressive signal and other signals derived using standard signal processing methods. The goal is to check the evolution of the CIs during the accelerated lifetime test (ALT). Clarity and fluctuation level of the historical trends are finally considered as a criteria for comparing between the extracted CIs.

Exploring the Mechanisms of Ecological Land Change Based on the Spatial Autoregressive Model: A Case Study of the Poyang Lake Eco-Economic Zone, China

PubMed Central

Xie, Hualin; Liu, Zhifei; Wang, Peng; Liu, Guiying; Lu, Fucai

2013-01-01

Ecological land is one of the key resources and conditions for the survival of humans because it can provide ecosystem services and is particularly important to public health and safety. It is extremely valuable for effective ecological management to explore the evolution mechanisms of ecological land. Based on spatial statistical analyses, we explored the spatial disparities and primary potential drivers of ecological land change in the Poyang Lake Eco-economic Zone of China. The results demonstrated that the global Moran’s I value is 0.1646 during the 1990 to 2005 time period and indicated significant positive spatial correlation (p < 0.05). The results also imply that the clustering trend of ecological land changes weakened in the study area. Some potential driving forces were identified by applying the spatial autoregressive model in this study. The results demonstrated that the higher economic development level and industrialization rate were the main drivers for the faster change of ecological land in the study area. This study also tested the superiority of the spatial autoregressive model to study the mechanisms of ecological land change by comparing it with the traditional linear regressive model. PMID:24384778

An Accurate GPS-IMU/DR Data Fusion Method for Driverless Car Based on a Set of Predictive Models and Grid Constraints

PubMed Central

Wang, Shiyao; Deng, Zhidong; Yin, Gang

2016-01-01

A high-performance differential global positioning system (GPS)  receiver with real time kinematics provides absolute localization for driverless cars. However, it is not only susceptible to multipath effect but also unable to effectively fulfill precise error correction in a wide range of driving areas. This paper proposes an accurate GPS–inertial measurement unit (IMU)/dead reckoning (DR) data fusion method based on a set of predictive models and occupancy grid constraints. First, we employ a set of autoregressive and moving average (ARMA) equations that have different structural parameters to build maximum likelihood models of raw navigation. Second, both grid constraints and spatial consensus checks on all predictive results and current measurements are required to have removal of outliers. Navigation data that satisfy stationary stochastic process are further fused to achieve accurate localization results. Third, the standard deviation of multimodal data fusion can be pre-specified by grid size. Finally, we perform a lot of field tests on a diversity of real urban scenarios. The experimental results demonstrate that the method can significantly smooth small jumps in bias and considerably reduce accumulated position errors due to DR. With low computational complexity, the position accuracy of our method surpasses existing state-of-the-arts on the same dataset and the new data fusion method is practically applied in our driverless car. PMID:26927108

An Accurate GPS-IMU/DR Data Fusion Method for Driverless Car Based on a Set of Predictive Models and Grid Constraints.

PubMed

Wang, Shiyao; Deng, Zhidong; Yin, Gang

2016-02-24

A high-performance differential global positioning system (GPS)  receiver with real time kinematics provides absolute localization for driverless cars. However, it is not only susceptible to multipath effect but also unable to effectively fulfill precise error correction in a wide range of driving areas. This paper proposes an accurate GPS-inertial measurement unit (IMU)/dead reckoning (DR) data fusion method based on a set of predictive models and occupancy grid constraints. First, we employ a set of autoregressive and moving average (ARMA) equations that have different structural parameters to build maximum likelihood models of raw navigation. Second, both grid constraints and spatial consensus checks on all predictive results and current measurements are required to have removal of outliers. Navigation data that satisfy stationary stochastic process are further fused to achieve accurate localization results. Third, the standard deviation of multimodal data fusion can be pre-specified by grid size. Finally, we perform a lot of field tests on a diversity of real urban scenarios. The experimental results demonstrate that the method can significantly smooth small jumps in bias and considerably reduce accumulated position errors due to DR. With low computational complexity, the position accuracy of our method surpasses existing state-of-the-arts on the same dataset and the new data fusion method is practically applied in our driverless car.

Minimum entropy deconvolution optimized sinusoidal synthesis and its application to vibration based fault detection

NASA Astrophysics Data System (ADS)

Li, Gang; Zhao, Qing

2017-03-01

In this paper, a minimum entropy deconvolution based sinusoidal synthesis (MEDSS) filter is proposed to improve the fault detection performance of the regular sinusoidal synthesis (SS) method. The SS filter is an efficient linear predictor that exploits the frequency properties during model construction. The phase information of the harmonic components is not used in the regular SS filter. However, the phase relationships are important in differentiating noise from characteristic impulsive fault signatures. Therefore, in this work, the minimum entropy deconvolution (MED) technique is used to optimize the SS filter during the model construction process. A time-weighted-error Kalman filter is used to estimate the MEDSS model parameters adaptively. Three simulation examples and a practical application case study are provided to illustrate the effectiveness of the proposed method. The regular SS method and the autoregressive MED (ARMED) method are also implemented for comparison. The MEDSS model has demonstrated superior performance compared to the regular SS method and it also shows comparable or better performance with much less computational intensity than the ARMED method.

Urban Growth in a Fragmented Landscape: Estimating the Relationship between Landscape Pattern and Urban Land Use Change in Germany, 2000-2006

NASA Astrophysics Data System (ADS)

Keller, R.

2013-12-01

One of the highest priorities in the conservation and management of biodiversity, natural resources and other vital ecosystem services is the assessment of the mechanisms that drive urban land use change. Using key landscape indicators, this study addresses why urban land increased 6 percent overall in Germany from 2000-2006. Building on regional science and economic geography research, I develop a model of landscape change that integrates remotely sensed and other geospatial data, and socioeconomic data in a spatial autoregressive model to explain the variance in urban land use change observed in German kreise (counties) over the past decade. The results reveal three key landscape mechanisms that drive urban land use change across Germany, aligning with those observed in US studies: (1) the level of fragmentation, (2) the share of designated protected areas, and (3) the share of prime soil. First, as fragmentation of once continuous habitats in the landscape increases, extensive urban growth follows. Second, designated protected areas have the perverse effect of hastening urbanization in surrounding areas. Third, greater shares of prime, productive soil experienced less urban land take over the 6 year period, an effect that is stronger in the former East Germany, where the agricultural sector remains large. The results suggest that policy makers concentrate their conservation efforts on preexisting fragmented land with high shares of protected areas in Germany to effectively stem urban land take. Given that comparative studies of land use change are vital for the scientific community to grasp the wider global process of urbanization and coincident ecological impacts, the methodology employed here is easily exportable to land cover and land use research programs in other fields and geographic areas. Key words: Urban land use change, Ecosystem services, Landscape fragmentation, Remote sensing, Spatial regression models, GermanyOLS and Spatial Autoregressive Model Results N = 439; Standard error in ( ) . *p < .1, **p < .01, ***p < .001

Potential predictability and forecast skill in ensemble climate forecast: the skill-persistence rule

NASA Astrophysics Data System (ADS)

Jin, Y.; Rong, X.; Liu, Z.

2017-12-01

This study investigates the factors that impact the forecast skill for the real world (actual skill) and perfect model (perfect skill) in ensemble climate model forecast with a series of fully coupled general circulation model forecast experiments. It is found that the actual skill of sea surface temperature (SST) in seasonal forecast is substantially higher than the perfect skill on a large part of the tropical oceans, especially the tropical Indian Ocean and the central-eastern Pacific Ocean. The higher actual skill is found to be related to the higher observational SST persistence, suggesting a skill-persistence rule: a higher SST persistence in the real world than in the model could overwhelm the model bias to produce a higher forecast skill for the real world than for the perfect model. The relation between forecast skill and persistence is further examined using a first-order autoregressive model (AR1) analytically for theoretical solutions and numerically for analogue experiments. The AR1 model study shows that the skill-persistence rule is strictly valid in the case of infinite ensemble size, but can be distorted by the sampling error and non-AR1 processes.

[Study on the ARIMA model application to predict echinococcosis cases in China].

PubMed

En-Li, Tan; Zheng-Feng, Wang; Wen-Ce, Zhou; Shi-Zhu, Li; Yan, Lu; Lin, Ai; Yu-Chun, Cai; Xue-Jiao, Teng; Shun-Xian, Zhang; Zhi-Sheng, Dang; Chun-Li, Yang; Jia-Xu, Chen; Wei, Hu; Xiao-Nong, Zhou; Li-Guang, Tian

2018-02-26

To predict the monthly reported echinococcosis cases in China with the autoregressive integrated moving average (ARIMA) model, so as to provide a reference for prevention and control of echinococcosis. SPSS 24.0 software was used to construct the ARIMA models based on the monthly reported echinococcosis cases of time series from 2007 to 2015 and 2007 to 2014, respectively, and the accuracies of the two ARIMA models were compared. The model based on the data of the monthly reported cases of echinococcosis in China from 2007 to 2015 was ARIMA (1, 0, 0) (1, 1, 0) 12 , the relative error among reported cases and predicted cases was -13.97%, AR (1) = 0.367 ( t = 3.816, P < 0.001), SAR (1) = -0.328 ( t = -3.361, P = 0.001), and Ljung-Box Q = 14.119 ( df = 16, P = 0.590) . The model based on the data of the monthly reported cases of echinococcosis in China from 2007 to 2014 was ARIMA (1, 0, 0) (1, 0, 1) 12 , the relative error among reported cases and predicted cases was 0.56%, AR (1) = 0.413 ( t = 4.244, P < 0.001), SAR (1) = 0.809 ( t = 9.584, P < 0.001), SMA (1) = 0.356 ( t = 2.278, P = 0.025), and Ljung-Box Q = 18.924 ( df = 15, P = 0.217). The different time series may have different ARIMA models as for the same infectious diseases. It is needed to be further verified that the more data are accumulated, the shorter time of predication is, and the smaller the average of the relative error is. The establishment and prediction of an ARIMA model is a dynamic process that needs to be adjusted and optimized continuously according to the accumulated data, meantime, we should give full consideration to the intensity of the work related to infectious diseases reported (such as disease census and special investigation).

Self-esteem Is Mostly Stable Across Young Adulthood: Evidence from Latent STARTS Models.

PubMed

Wagner, Jenny; Lüdtke, Oliver; Trautwein, Ulrich

2016-08-01

How stable is self-esteem? This long-standing debate has led to different conclusions across different areas of psychology. Longitudinal data and up-to-date statistical models have recently indicated that self-esteem has stable and autoregressive trait-like components and state-like components. We applied latent STARTS models with the goal of replicating previous findings in a longitudinal sample of young adults (N = 4,532; Mage  = 19.60, SD = 0.85; 55% female). In addition, we applied multigroup models to extend previous findings on different patterns of stability for men versus women and for people with high versus low levels of depressive symptoms. We found evidence for the general pattern of a major proportion of stable and autoregressive trait variance and a smaller yet substantial amount of state variance in self-esteem across 10 years. Furthermore, multigroup models suggested substantial differences in the variance components: Females showed more state variability than males. Individuals with higher levels of depressive symptoms showed more state and less autoregressive trait variance in self-esteem. Results are discussed with respect to the ongoing trait-state debate and possible implications of the group differences that we found in the stability of self-esteem. © 2015 Wiley Periodicals, Inc.

A MISO-ARX-Based Method for Single-Trial Evoked Potential Extraction.

PubMed

Yu, Nannan; Wu, Lingling; Zou, Dexuan; Chen, Ying; Lu, Hanbing

2017-01-01

In this paper, we propose a novel method for solving the single-trial evoked potential (EP) estimation problem. In this method, the single-trial EP is considered as a complex containing many components, which may originate from different functional brain sites; these components can be distinguished according to their respective latencies and amplitudes and are extracted simultaneously by multiple-input single-output autoregressive modeling with exogenous input (MISO-ARX). The extraction process is performed in three stages: first, we use a reference EP as a template and decompose it into a set of components, which serve as subtemplates for the remaining steps. Then, a dictionary is constructed with these subtemplates, and EPs are preliminarily extracted by sparse coding in order to roughly estimate the latency of each component. Finally, the single-trial measurement is parametrically modeled by MISO-ARX while characterizing spontaneous electroencephalographic activity as an autoregression model driven by white noise and with each component of the EP modeled by autoregressive-moving-average filtering of the subtemplates. Once optimized, all components of the EP can be extracted. Compared with ARX, our method has greater tracking capabilities of specific components of the EP complex as each component is modeled individually in MISO-ARX. We provide exhaustive experimental results to show the effectiveness and feasibility of our method.

Modeling and roles of meteorological factors in outbreaks of highly pathogenic avian influenza H5N1.

PubMed

Biswas, Paritosh K; Islam, Md Zohorul; Debnath, Nitish C; Yamage, Mat

2014-01-01

The highly pathogenic avian influenza A virus subtype H5N1 (HPAI H5N1) is a deadly zoonotic pathogen. Its persistence in poultry in several countries is a potential threat: a mutant or genetically reassorted progenitor might cause a human pandemic. Its world-wide eradication from poultry is important to protect public health. The global trend of outbreaks of influenza attributable to HPAI H5N1 shows a clear seasonality. Meteorological factors might be associated with such trend but have not been studied. For the first time, we analyze the role of meteorological factors in the occurrences of HPAI outbreaks in Bangladesh. We employed autoregressive integrated moving average (ARIMA) and multiplicative seasonal autoregressive integrated moving average (SARIMA) to assess the roles of different meteorological factors in outbreaks of HPAI. Outbreaks were modeled best when multiplicative seasonality was incorporated. Incorporation of any meteorological variable(s) as inputs did not improve the performance of any multivariable models, but relative humidity (RH) was a significant covariate in several ARIMA and SARIMA models with different autoregressive and moving average orders. The variable cloud cover was also a significant covariate in two SARIMA models, but air temperature along with RH might be a predictor when moving average (MA) order at lag 1 month is considered.

Order Selection for General Expression of Nonlinear Autoregressive Model Based on Multivariate Stepwise Regression

NASA Astrophysics Data System (ADS)

Shi, Jinfei; Zhu, Songqing; Chen, Ruwen

2017-12-01

An order selection method based on multiple stepwise regressions is proposed for General Expression of Nonlinear Autoregressive model which converts the model order problem into the variable selection of multiple linear regression equation. The partial autocorrelation function is adopted to define the linear term in GNAR model. The result is set as the initial model, and then the nonlinear terms are introduced gradually. Statistics are chosen to study the improvements of both the new introduced and originally existed variables for the model characteristics, which are adopted to determine the model variables to retain or eliminate. So the optimal model is obtained through data fitting effect measurement or significance test. The simulation and classic time-series data experiment results show that the method proposed is simple, reliable and can be applied to practical engineering.

A nonlinear autoregressive Volterra model of the Hodgkin-Huxley equations.

PubMed

Eikenberry, Steffen E; Marmarelis, Vasilis Z

2013-02-01

We propose a new variant of Volterra-type model with a nonlinear auto-regressive (NAR) component that is a suitable framework for describing the process of AP generation by the neuron membrane potential, and we apply it to input-output data generated by the Hodgkin-Huxley (H-H) equations. Volterra models use a functional series expansion to describe the input-output relation for most nonlinear dynamic systems, and are applicable to a wide range of physiologic systems. It is difficult, however, to apply the Volterra methodology to the H-H model because is characterized by distinct subthreshold and suprathreshold dynamics. When threshold is crossed, an autonomous action potential (AP) is generated, the output becomes temporarily decoupled from the input, and the standard Volterra model fails. Therefore, in our framework, whenever membrane potential exceeds some threshold, it is taken as a second input to a dual-input Volterra model. This model correctly predicts membrane voltage deflection both within the subthreshold region and during APs. Moreover, the model naturally generates a post-AP afterpotential and refractory period. It is known that the H-H model converges to a limit cycle in response to a constant current injection. This behavior is correctly predicted by the proposed model, while the standard Volterra model is incapable of generating such limit cycle behavior. The inclusion of cross-kernels, which describe the nonlinear interactions between the exogenous and autoregressive inputs, is found to be absolutely necessary. The proposed model is general, non-parametric, and data-derived.

On the Stationarity of Multiple Autoregressive Approximants: Theory and Algorithms

DTIC Science & Technology

1976-08-01

a I (3.4) Hannan and Terrell (1972) consider problems of a similar nature. Efficient estimates A(1),... , A(p) , and i of A(1)... ,A(p) and...34Autoregressive model fitting for control, Ann . Inst. Statist. Math., 23, 163-180. Hannan, E. J. (1970), Multiple Time Series, New York, John Wiley...Hannan, E. J. and Terrell , R. D. (1972), "Time series regression with linear constraints, " International Economic Review, 13, 189-200. Masani, P

Coupling detrended fluctuation analysis of Asian stock markets

NASA Astrophysics Data System (ADS)

Wang, Qizhen; Zhu, Yingming; Yang, Liansheng; Mul, Remco A. H.

2017-04-01

This paper uses the coupling detrended fluctuation analysis (CDFA) method to investigate the multifractal characteristics of four Asian stock markets using three stock indices: stock price returns, trading volumes and the composite index. The results show that coupled correlations exist among the four stock markets and the coupled correlations have multifractal characteristics. We then use the chi square (χ2) test to identify the sources of multifractality. For the different stock indices, the contributions of a single series to multifractality are different. In other words, the contributions of each country to coupled correlations are different. The comparative analysis shows that the research on the combine effect of stock price returns and trading volumes may be more comprehensive than on an individual index. By comparing the strength of multifractality for original data with the residual errors of the vector autoregression (VAR) model, we find that the VAR model could not be used to describe the dynamics of the coupled correlations among four financial time series.

Low-dimensional recurrent neural network-based Kalman filter for speech enhancement.

PubMed

Xia, Youshen; Wang, Jun

2015-07-01

This paper proposes a new recurrent neural network-based Kalman filter for speech enhancement, based on a noise-constrained least squares estimate. The parameters of speech signal modeled as autoregressive process are first estimated by using the proposed recurrent neural network and the speech signal is then recovered from Kalman filtering. The proposed recurrent neural network is globally asymptomatically stable to the noise-constrained estimate. Because the noise-constrained estimate has a robust performance against non-Gaussian noise, the proposed recurrent neural network-based speech enhancement algorithm can minimize the estimation error of Kalman filter parameters in non-Gaussian noise. Furthermore, having a low-dimensional model feature, the proposed neural network-based speech enhancement algorithm has a much faster speed than two existing recurrent neural networks-based speech enhancement algorithms. Simulation results show that the proposed recurrent neural network-based speech enhancement algorithm can produce a good performance with fast computation and noise reduction. Copyright © 2015 Elsevier Ltd. All rights reserved.

Non performing loans (NPLs) in a crisis economy: Long-run equilibrium analysis with a real time VEC model for Greece (2001-2015)

NASA Astrophysics Data System (ADS)

Konstantakis, Konstantinos N.; Michaelides, Panayotis G.; Vouldis, Angelos T.

2016-06-01

As a result of domestic and international factors, the Greek economy faced a severe crisis which is directly comparable only to the Great Recession. In this context, a prominent victim of this situation was the country's banking system. This paper attempts to shed light on the determining factors of non-performing loans in the Greek banking sector. The analysis presents empirical evidence from the Greek economy, using aggregate data on a quarterly basis, in the time period 2001-2015, fully capturing the recent recession. In this work, we use a relevant econometric framework based on a real time Vector Autoregressive (VAR)-Vector Error Correction (VEC) model, which captures the dynamic interdependencies among the variables used. Consistent with international evidence, the empirical findings show that both macroeconomic and financial factors have a significant impact on non-performing loans in the country. Meanwhile, the deteriorating credit quality feeds back into the economy leading to a self-reinforcing negative loop.

Modeling Bivariate Change in Individual Differences: Prospective Associations Between Personality and Life Satisfaction.

PubMed

Hounkpatin, Hilda Osafo; Boyce, Christopher J; Dunn, Graham; Wood, Alex M

2017-09-18

A number of structural equation models have been developed to examine change in 1 variable or the longitudinal association between 2 variables. The most common of these are the latent growth model, the autoregressive cross-lagged model, the autoregressive latent trajectory model, and the latent change score model. The authors first overview each of these models through evaluating their different assumptions surrounding the nature of change and how these assumptions may result in different data interpretations. They then, to elucidate these issues in an empirical example, examine the longitudinal association between personality traits and life satisfaction. In a representative Dutch sample (N = 8,320), with participants providing data on both personality and life satisfaction measures every 2 years over an 8-year period, the authors reproduce findings from previous research. However, some of the structural equation models overviewed have not previously been applied to the personality-life satisfaction relation. The extended empirical examination suggests intraindividual changes in life satisfaction predict subsequent intraindividual changes in personality traits. The availability of data sets with 3 or more assessment waves allows the application of more advanced structural equation models such as the autoregressive latent trajectory or the extended latent change score model, which accounts for the complex dynamic nature of change processes and allows stronger inferences on the nature of the association between variables. However, the choice of model should be determined by theories of change processes in the variables being studied. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Dynamic regulation of heart rate during acute hypotension: new insight into baroreflex function

NASA Technical Reports Server (NTRS)

Zhang, R.; Behbehani, K.; Crandall, C. G.; Zuckerman, J. H.; Levine, B. D.; Blomqvist, C. G. (Principal Investigator)

2001-01-01

To examine the dynamic properties of baroreflex function, we measured beat-to-beat changes in arterial blood pressure (ABP) and heart rate (HR) during acute hypotension induced by thigh cuff deflation in 10 healthy subjects under supine resting conditions and during progressive lower body negative pressure (LBNP). The quantitative, temporal relationship between ABP and HR was fitted by a second-order autoregressive (AR) model. The frequency response was evaluated by transfer function analysis. Results: HR changes during acute hypotension appear to be controlled by an ABP error signal between baseline and induced hypotension. The quantitative relationship between changes in ABP and HR is characterized by a second-order AR model with a pure time delay of 0.75 s containing low-pass filter properties. During LBNP, the change in HR/change in ABP during induced hypotension significantly decreased, as did the numerator coefficients of the AR model and transfer function gain. Conclusions: 1) Beat-to-beat HR responses to dynamic changes in ABP may be controlled by an error signal rather than directional changes in pressure, suggesting a "set point" mechanism in short-term ABP control. 2) The quantitative relationship between dynamic changes in ABP and HR can be described by a second-order AR model with a pure time delay. 3) The ability of the baroreflex to evoke a HR response to transient changes in pressure was reduced during LBNP, which was due primarily to a reduction of the static gain of the baroreflex.

Hydrologic Model Selection using Markov chain Monte Carlo methods

NASA Astrophysics Data System (ADS)

Marshall, L.; Sharma, A.; Nott, D.

2002-12-01

Estimation of parameter uncertainty (and in turn model uncertainty) allows assessment of the risk in likely applications of hydrological models. Bayesian statistical inference provides an ideal means of assessing parameter uncertainty whereby prior knowledge about the parameter is combined with information from the available data to produce a probability distribution (the posterior distribution) that describes uncertainty about the parameter and serves as a basis for selecting appropriate values for use in modelling applications. Widespread use of Bayesian techniques in hydrology has been hindered by difficulties in summarizing and exploring the posterior distribution. These difficulties have been largely overcome by recent advances in Markov chain Monte Carlo (MCMC) methods that involve random sampling of the posterior distribution. This study presents an adaptive MCMC sampling algorithm which has characteristics that are well suited to model parameters with a high degree of correlation and interdependence, as is often evident in hydrological models. The MCMC sampling technique is used to compare six alternative configurations of a commonly used conceptual rainfall-runoff model, the Australian Water Balance Model (AWBM), using 11 years of daily rainfall runoff data from the Bass river catchment in Australia. The alternative configurations considered fall into two classes - those that consider model errors to be independent of prior values, and those that model the errors as an autoregressive process. Each such class consists of three formulations that represent increasing levels of complexity (and parameterisation) of the original model structure. The results from this study point both to the importance of using Bayesian approaches in evaluating model performance, as well as the simplicity of the MCMC sampling framework that has the ability to bring such approaches within the reach of the applied hydrological community.

Methodology for the AutoRegressive Planet Search (ARPS) Project

NASA Astrophysics Data System (ADS)

Feigelson, Eric; Caceres, Gabriel; ARPS Collaboration

2018-01-01

The detection of periodic signals of transiting exoplanets is often impeded by the presence of aperiodic photometric variations. This variability is intrinsic to the host star in space-based observations (typically arising from magnetic activity) and from observational conditions in ground-based observations. The most common statistical procedures to remove stellar variations are nonparametric, such as wavelet decomposition or Gaussian Processes regression. However, many stars display variability with autoregressive properties, wherein later flux values are correlated with previous ones. Providing the time series is evenly spaced, parametric autoregressive models can prove very effective. Here we present the methodology of the Autoregessive Planet Search (ARPS) project which uses Autoregressive Integrated Moving Average (ARIMA) models to treat a wide variety of stochastic short-memory processes, as well as nonstationarity. Additionally, we introduce a planet-search algorithm to detect periodic transits in the time-series residuals after application of ARIMA models. Our matched-filter algorithm, the Transit Comb Filter (TCF), replaces the traditional box-fitting step. We construct a periodogram based on the TCF to concentrate the signal of these periodic spikes. Various features of the original light curves, the ARIMA fits, the TCF periodograms, and folded light curves at peaks of the TCF periodogram can then be collected to provide constraints for planet detection. These features provide input into a multivariate classifier when a training set is available. The ARPS procedure has been applied NASA's Kepler mission observations of ~200,000 stars (Caceres, Dissertation Talk, this meeting) and will be applied in the future to other datasets.

Order-Constrained Reference Priors with Implications for Bayesian Isotonic Regression, Analysis of Covariance and Spatial Models

NASA Astrophysics Data System (ADS)

Gong, Maozhen

Selecting an appropriate prior distribution is a fundamental issue in Bayesian Statistics. In this dissertation, under the framework provided by Berger and Bernardo, I derive the reference priors for several models which include: Analysis of Variance (ANOVA)/Analysis of Covariance (ANCOVA) models with a categorical variable under common ordering constraints, the conditionally autoregressive (CAR) models and the simultaneous autoregressive (SAR) models with a spatial autoregression parameter rho considered. The performances of reference priors for ANOVA/ANCOVA models are evaluated by simulation studies with comparisons to Jeffreys' prior and Least Squares Estimation (LSE). The priors are then illustrated in a Bayesian model of the "Risk of Type 2 Diabetes in New Mexico" data, where the relationship between the type 2 diabetes risk (through Hemoglobin A1c) and different smoking levels is investigated. In both simulation studies and real data set modeling, the reference priors that incorporate internal order information show good performances and can be used as default priors. The reference priors for the CAR and SAR models are also illustrated in the "1999 SAT State Average Verbal Scores" data with a comparison to a Uniform prior distribution. Due to the complexity of the reference priors for both CAR and SAR models, only a portion (12 states in the Midwest) of the original data set is considered. The reference priors can give a different marginal posterior distribution compared to a Uniform prior, which provides an alternative for prior specifications for areal data in Spatial statistics.

Forecast of dengue incidence using temperature and rainfall.

PubMed

Hii, Yien Ling; Zhu, Huaiping; Ng, Nawi; Ng, Lee Ching; Rocklöv, Joacim

2012-01-01

An accurate early warning system to predict impending epidemics enhances the effectiveness of preventive measures against dengue fever. The aim of this study was to develop and validate a forecasting model that could predict dengue cases and provide timely early warning in Singapore. We developed a time series Poisson multivariate regression model using weekly mean temperature and cumulative rainfall over the period 2000-2010. Weather data were modeled using piecewise linear spline functions. We analyzed various lag times between dengue and weather variables to identify the optimal dengue forecasting period. Autoregression, seasonality and trend were considered in the model. We validated the model by forecasting dengue cases for week 1 of 2011 up to week 16 of 2012 using weather data alone. Model selection and validation were based on Akaike's Information Criterion, standardized Root Mean Square Error, and residuals diagnoses. A Receiver Operating Characteristics curve was used to analyze the sensitivity of the forecast of epidemics. The optimal period for dengue forecast was 16 weeks. Our model forecasted correctly with errors of 0.3 and 0.32 of the standard deviation of reported cases during the model training and validation periods, respectively. It was sensitive enough to distinguish between outbreak and non-outbreak to a 96% (CI = 93-98%) in 2004-2010 and 98% (CI = 95%-100%) in 2011. The model predicted the outbreak in 2011 accurately with less than 3% possibility of false alarm. We have developed a weather-based dengue forecasting model that allows warning 16 weeks in advance of dengue epidemics with high sensitivity and specificity. We demonstrate that models using temperature and rainfall could be simple, precise, and low cost tools for dengue forecasting which could be used to enhance decision making on the timing, scale of vector control operations, and utilization of limited resources.

Business cycles and fertility dynamics in the United States: a vector autoregressive model.

PubMed

Mocan, N H

1990-01-01

"Using vector-autoregressions...this paper shows that fertility moves countercyclically over the business cycle....[It] shows that the United States fertility is not governed by a deterministic trend as was assumed by previous studies. Rather, fertility evolves around a stochastic trend. It is shown that a bivariate analysis between fertility and unemployment yields a procyclical picture of fertility. However, when one considers the effects on fertility of early marriages and the divorce behavior as well as economic activity, fertility moves countercyclically." excerpt

iVAR: a program for imputing missing data in multivariate time series using vector autoregressive models.

PubMed

Liu, Siwei; Molenaar, Peter C M

2014-12-01

This article introduces iVAR, an R program for imputing missing data in multivariate time series on the basis of vector autoregressive (VAR) models. We conducted a simulation study to compare iVAR with three methods for handling missing data: listwise deletion, imputation with sample means and variances, and multiple imputation ignoring time dependency. The results showed that iVAR produces better estimates for the cross-lagged coefficients than do the other three methods. We demonstrate the use of iVAR with an empirical example of time series electrodermal activity data and discuss the advantages and limitations of the program.

Comparison of six methods for the detection of causality in a bivariate time series

NASA Astrophysics Data System (ADS)

Krakovská, Anna; Jakubík, Jozef; Chvosteková, Martina; Coufal, David; Jajcay, Nikola; Paluš, Milan

2018-04-01

In this comparative study, six causality detection methods were compared, namely, the Granger vector autoregressive test, the extended Granger test, the kernel version of the Granger test, the conditional mutual information (transfer entropy), the evaluation of cross mappings between state spaces, and an assessment of predictability improvement due to the use of mixed predictions. Seven test data sets were analyzed: linear coupling of autoregressive models, a unidirectional connection of two Hénon systems, a unidirectional connection of chaotic systems of Rössler and Lorenz type and of two different Rössler systems, an example of bidirectionally connected two-species systems, a fishery model as an example of two correlated observables without a causal relationship, and an example of mediated causality. We tested not only 20 000 points long clean time series but also noisy and short variants of the data. The standard and the extended Granger tests worked only for the autoregressive models. The remaining methods were more successful with the more complex test examples, although they differed considerably in their capability to reveal the presence and the direction of coupling and to distinguish causality from mere correlation.

Modelling malaria incidence by an autoregressive distributed lag model with spatial component.

PubMed

Laguna, Francisco; Grillet, María Eugenia; León, José R; Ludeña, Carenne

2017-08-01

The influence of climatic variables on the dynamics of human malaria has been widely highlighted. Also, it is known that this mosquito-borne infection varies in space and time. However, when the data is spatially incomplete most popular spatio-temporal methods of analysis cannot be applied directly. In this paper, we develop a two step methodology to model the spatio-temporal dependence of malaria incidence on local rainfall, temperature, and humidity as well as the regional sea surface temperatures (SST) in the northern coast of Venezuela. First, we fit an autoregressive distributed lag model (ARDL) to the weekly data, and then, we adjust a linear separable spacial vectorial autoregressive model (VAR) to the residuals of the ARDL. Finally, the model parameters are tuned using a Markov Chain Monte Carlo (MCMC) procedure derived from the Metropolis-Hastings algorithm. Our results show that the best model to account for the variations of malaria incidence from 2001 to 2008 in 10 endemic Municipalities in North-Eastern Venezuela is a logit model that included the accumulated local precipitation in combination with the local maximum temperature of the preceding month as positive regressors. Additionally, we show that although malaria dynamics is highly heterogeneous in space, a detailed analysis of the estimated spatial parameters in our model yield important insights regarding the joint behavior of the disease incidence across the different counties in our study. Copyright © 2017 Elsevier Ltd. All rights reserved.

Integrating geographic information systems and remote sensing with spatial econometric and mixed logit models for environmental valuation

NASA Astrophysics Data System (ADS)

Wells, Aaron Raymond

This research focuses on the Emory and Obed Watersheds in the Cumberland Plateau in Central Tennessee and the Lower Hatchie River Watershed in West Tennessee. A framework based on market and nonmarket valuation techniques was used to empirically estimate economic values for environmental amenities and negative externalities in these areas. The specific techniques employed include a variation of hedonic pricing and discrete choice conjoint analysis (i.e., choice modeling), in addition to geographic information systems (GIS) and remote sensing. Microeconomic models of agent behavior, including random utility theory and profit maximization, provide the principal theoretical foundation linking valuation techniques and econometric models. The generalized method of moments estimator for a first-order spatial autoregressive function and mixed logit models are the principal econometric methods applied within the framework. The dissertation is subdivided into three separate chapters written in a manuscript format. The first chapter provides the necessary theoretical and mathematical conditions that must be satisfied in order for a forest amenity enhancement program to be implemented. These conditions include utility, value, and profit maximization. The second chapter evaluates the effect of forest land cover and information about future land use change on respondent preferences and willingness to pay for alternative hypothetical forest amenity enhancement options. Land use change information and the amount of forest land cover significantly influenced respondent preferences, choices, and stated willingness to pay. Hicksian welfare estimates for proposed enhancement options ranged from 57.42 to 25.53, depending on the policy specification, information level, and econometric model. The third chapter presents economic values for negative externalities associated with channelization that affect the productivity and overall market value of forested wetlands. Results of robust, generalized moments estimation of a double logarithmic first-order spatial autoregressive error model (inverse distance weights with spatial dependence up to 1500m) indicate that the implicit cost of damages to forested wetlands caused by channelization equaled -$5,438 ha-1. Collectively, the results of this dissertation provide economic measures of the damages to and benefits of environmental assets, help private landowners and policy makers identify the amenity attributes preferred by the public, and improve the management of natural resources.

Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network

PubMed Central

Marcek, Dusan; Durisova, Maria

2016-01-01

This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process. PMID:26977450

Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network.

PubMed

Falat, Lukas; Marcek, Dusan; Durisova, Maria

2016-01-01

This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process.

Hierarchical time series bottom-up approach for forecast the export value in Central Java

NASA Astrophysics Data System (ADS)

Mahkya, D. A.; Ulama, B. S.; Suhartono

2017-10-01

The purpose of this study is Getting the best modeling and predicting the export value of Central Java using a Hierarchical Time Series. The export value is one variable injection in the economy of a country, meaning that if the export value of the country increases, the country’s economy will increase even more. Therefore, it is necessary appropriate modeling to predict the export value especially in Central Java. Export Value in Central Java are grouped into 21 commodities with each commodity has a different pattern. One approach that can be used time series is a hierarchical approach. Hierarchical Time Series is used Buttom-up. To Forecast the individual series at all levels using Autoregressive Integrated Moving Average (ARIMA), Radial Basis Function Neural Network (RBFNN), and Hybrid ARIMA-RBFNN. For the selection of the best models used Symmetric Mean Absolute Percentage Error (sMAPE). Results of the analysis showed that for the Export Value of Central Java, Bottom-up approach with Hybrid ARIMA-RBFNN modeling can be used for long-term predictions. As for the short and medium-term predictions, it can be used a bottom-up approach RBFNN modeling. Overall bottom-up approach with RBFNN modeling give the best result.

Flexible and scalable methods for quantifying stochastic variability in the era of massive time-domain astronomical data sets

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kelly, Brandon C.; Becker, Andrew C.; Sobolewska, Malgosia

2014-06-10

We present the use of continuous-time autoregressive moving average (CARMA) models as a method for estimating the variability features of a light curve, and in particular its power spectral density (PSD). CARMA models fully account for irregular sampling and measurement errors, making them valuable for quantifying variability, forecasting and interpolating light curves, and variability-based classification. We show that the PSD of a CARMA model can be expressed as a sum of Lorentzian functions, which makes them extremely flexible and able to model a broad range of PSDs. We present the likelihood function for light curves sampled from CARMA processes, placingmore » them on a statistically rigorous foundation, and we present a Bayesian method to infer the probability distribution of the PSD given the measured light curve. Because calculation of the likelihood function scales linearly with the number of data points, CARMA modeling scales to current and future massive time-domain data sets. We conclude by applying our CARMA modeling approach to light curves for an X-ray binary, two active galactic nuclei, a long-period variable star, and an RR Lyrae star in order to illustrate their use, applicability, and interpretation.« less

Essays in applied macroeconomics: Asymmetric price adjustment, exchange rate and treatment effect

NASA Astrophysics Data System (ADS)

Gu, Jingping

This dissertation consists of three essays. Chapter II examines the possible asymmetric response of gasoline prices to crude oil price changes using an error correction model with GARCH errors. Recent papers have looked at this issue. Some of these papers estimate a form of error correction model, but none of them accounts for autoregressive heteroskedasticity in estimation and testing for asymmetry and none of them takes the response of crude oil price into consideration. We find that time-varying volatility of gasoline price disturbances is an important feature of the data, and when we allow for asymmetric GARCH errors and investigate the system wide impulse response function, we find evidence of asymmetric adjustment to crude oil price changes in weekly retail gasoline prices. Chapter III discusses the relationship between fiscal deficit and exchange rate. Economic theory predicts that fiscal deficits can significantly affect real exchange rate movements, but existing empirical evidence reports only a weak impact of fiscal deficits on exchange rates. Based on US dollar-based real exchange rates in G5 countries and a flexible varying coefficient model, we show that the previously documented weak relationship between fiscal deficits and exchange rates may be the result of additive specifications, and that the relationship is stronger if we allow fiscal deficits to impact real exchange rates non-additively as well as nonlinearly. We find that the speed of exchange rate adjustment toward equilibrium depends on the state of the fiscal deficit; a fiscal contraction in the US can lead to less persistence in the deviation of exchange rates from fundamentals, and faster mean reversion to the equilibrium. Chapter IV proposes a kernel method to deal with the nonparametric regression model with only discrete covariates as regressors. This new approach is based on recently developed least squares cross-validation kernel smoothing method. It can not only automatically smooth the irrelevant variables out of the nonparametric regression model, but also avoid the problem of loss of efficiency related to the traditional nonparametric frequency-based method and the problem of misspecification based on parametric model.

The effects of spatial autoregressive dependencies on inference in ordinary least squares: a geometric approach

NASA Astrophysics Data System (ADS)

Smith, Tony E.; Lee, Ka Lok

2012-01-01

There is a common belief that the presence of residual spatial autocorrelation in ordinary least squares (OLS) regression leads to inflated significance levels in beta coefficients and, in particular, inflated levels relative to the more efficient spatial error model (SEM). However, our simulations show that this is not always the case. Hence, the purpose of this paper is to examine this question from a geometric viewpoint. The key idea is to characterize the OLS test statistic in terms of angle cosines and examine the geometric implications of this characterization. Our first result is to show that if the explanatory variables in the regression exhibit no spatial autocorrelation, then the distribution of test statistics for individual beta coefficients in OLS is independent of any spatial autocorrelation in the error term. Hence, inferences about betas exhibit all the optimality properties of the classic uncorrelated error case. However, a second more important series of results show that if spatial autocorrelation is present in both the dependent and explanatory variables, then the conventional wisdom is correct. In particular, even when an explanatory variable is statistically independent of the dependent variable, such joint spatial dependencies tend to produce "spurious correlation" that results in over-rejection of the null hypothesis. The underlying geometric nature of this problem is clarified by illustrative examples. The paper concludes with a brief discussion of some possible remedies for this problem.

Large signal-to-noise ratio quantification in MLE for ARARMAX models

NASA Astrophysics Data System (ADS)

Zou, Yiqun; Tang, Xiafei

2014-06-01

It has been shown that closed-loop linear system identification by indirect method can be generally transferred to open-loop ARARMAX (AutoRegressive AutoRegressive Moving Average with eXogenous input) estimation. For such models, the gradient-related optimisation with large enough signal-to-noise ratio (SNR) can avoid the potential local convergence in maximum likelihood estimation. To ease the application of this condition, the threshold SNR needs to be quantified. In this paper, we build the amplitude coefficient which is an equivalence to the SNR and prove the finiteness of the threshold amplitude coefficient within the stability region. The quantification of threshold is achieved by the minimisation of an elaborately designed multi-variable cost function which unifies all the restrictions on the amplitude coefficient. The corresponding algorithm based on two sets of physically realisable system input-output data details the minimisation and also points out how to use the gradient-related method to estimate ARARMAX parameters when local minimum is present as the SNR is small. Then, the algorithm is tested on a theoretical AutoRegressive Moving Average with eXogenous input model for the derivation of the threshold and a gas turbine engine real system for model identification, respectively. Finally, the graphical validation of threshold on a two-dimensional plot is discussed.

Performance of the Prognocean Plus system during the El Niño 2015/2016: predictions of sea level anomalies as tools for forecasting El Niño

NASA Astrophysics Data System (ADS)

Świerczyńska-Chlaściak, Małgorzata; Niedzielski, Tomasz; Miziński, Bartłomiej

2017-04-01

The aim of this paper is to present the performance of the Prognocean Plus system, which produces long-term predictions of sea level anomalies, during the El Niño 2015/2016. The main objective of work is to identify such ocean areas in which long-term forecasts of sea level anomalies during El Niño 2015/2016 reveal a considerable accuracy. At present, the system produces prognoses using four data-based models and their combinations: polynomial-harmonic model, autoregressive model, threshold autoregressive model and multivariate autoregressive model. The system offers weekly forecasts, with lead time up to 12 weeks. Several statistics that describe the efficiency of the available prediction models in four seasons used for estimating Oceanic Niño index (ONI) are calculated. The accuracies/skills of the predicting models were computed in the specific locations in the equatorial Pacific, namely the geometrically-determined central points of all Niño regions. For the said locations, we focused on the forecasts which targeted at the local maximum of sea level, driven by the El Niño 2015/2016. As a result, a series of the "spaghetti" graphs (for each point, season and model) as well as plots presenting the prognostic performance of every model - for all lead times, seasons and locations - were created. It is found that the Prognocean Plus system has a potential to become a new solution which may enhance the diagnostic discussions on the El Niño development. The forecasts produced by the threshold autoregressive model, for lead times of 5-6 weeks and 9 weeks, within the Niño1+2 region for the November-to-January (NDJ) season anticipated the culmination of the El Niño 2015/2016. The longest forecasts (8-12 weeks) were found to be the most accurate in the phase of transition from El Niño to normal conditions (the multivariate autoregressive model, central point of Niño1+2 region, the December-to-February season). The study was conducted to verify the ability and usefulness of sea level anomaly forecasts in predicting phenomena that are controlled by the ocean-atmosphere processes, such as El Niño Southern Oscillation or North Atlantic Oscillation. The results may support further investigations into long-term forecasting of the quantitative indices of these oscillations, solely based on prognoses of sea level change. In particular, comparing the accuracies of prognoses of the North Atlantic Oscillation index remains one of the tasks of the research project no. 2016/21/N/ST10/03231, financed by the National Science Center of Poland.

At the Frontiers of Modeling Intensive Longitudinal Data: Dynamic Structural Equation Models for the Affective Measurements from the COGITO Study.

PubMed

Hamaker, E L; Asparouhov, T; Brose, A; Schmiedek, F; Muthén, B

2018-04-06

With the growing popularity of intensive longitudinal research, the modeling techniques and software options for such data are also expanding rapidly. Here we use dynamic multilevel modeling, as it is incorporated in the new dynamic structural equation modeling (DSEM) toolbox in Mplus, to analyze the affective data from the COGITO study. These data consist of two samples of over 100 individuals each who were measured for about 100 days. We use composite scores of positive and negative affect and apply a multilevel vector autoregressive model to allow for individual differences in means, autoregressions, and cross-lagged effects. Then we extend the model to include random residual variances and covariance, and finally we investigate whether prior depression affects later depression scores through the random effects of the daily diary measures. We end with discussing several urgent-but mostly unresolved-issues in the area of dynamic multilevel modeling.

Toddlers’ transition to out-of-home day care: Settling into a new care environment

PubMed Central

Datler, Wilfried; Ereky-Stevens, Katharina; Hover-Reisner, Nina; Malmberg, Lars-Erik

2012-01-01

This study investigates toddlers’ initial reaction to day care entry and their behaviour change over the first few months in care. One hundred and four toddlers (10–33 months of age) in Viennese childcare centres participated in the study. One-hour video observations were carried out at 3 time points during the first 4 months in the setting and coded into a total of 36 5-min observation segments. Multilevel models (observation segments nested within children) with an autoregressive error structure fitted data well. Two weeks after entry into care, toddlers’ levels of affect and interaction were low. Overall, changes in all areas of observed behaviour were less than expected. There were considerable individual differences in change over time, mostly unrelated to child characteristics. Significant associations between children's positive affect, their dynamic interactions and their explorative and investigative interest were found. PMID:22721743

Causal relationship between CO₂ emissions, real GDP, energy consumption, financial development, trade openness, and urbanization in Tunisia.

PubMed

Farhani, Sahbi; Ozturk, Ilhan

2015-10-01

The aim of this paper is to examine the causal relationship between CO2 emissions, real GDP, energy consumption, financial development, trade openness, and urbanization in Tunisia over the period of 1971-2012. The long-run relationship is investigated by the auto-regressive distributed lag (ARDL) bounds testing approach to cointegration and error correction method (ECM). The results of the analysis reveal a positive sign for the coefficient of financial development, suggesting that the financial development in Tunisia has taken place at the expense of environmental pollution. The Tunisian case also shows a positive monotonic relationship between real GDP and CO2 emissions. This means that the results do not support the validity of environmental Kuznets curve (EKC) hypothesis. In addition, the paper explores causal relationship between the variables by using Granger causality models and it concludes that financial development plays a vital role in the Tunisian economy.

Autoregressive models for estimating phylogenetic and environmental effects: accounting for within-species variations.

PubMed

Cornillon, P A; Pontier, D; Rochet, M J

2000-02-21

Comparative methods are used to investigate the attributes of present species or higher taxa. Difficulties arise from the phylogenetic heritage: taxa are not independent and neglecting phylogenetic inertia can lead to inaccurate results. Within-species variations in life-history traits are also not negligible, but most comparative methods are not designed to take them into account. Taxa are generally described by a single value for each trait. We have developed a new model which permits the incorporation of both the phylogenetic relationships among populations and within-species variations. This is an extension of classical autoregressive models. This family of models was used to study the effect of fishing on six demographic traits measured on 77 populations of teleost fishes. Copyright 2000 Academic Press.

Estimating time-varying conditional correlations between stock and foreign exchange markets

NASA Astrophysics Data System (ADS)

Tastan, Hüseyin

2006-02-01

This study explores the dynamic interaction between stock market returns and changes in nominal exchange rates. Many financial variables are known to exhibit fat tails and autoregressive variance structure. It is well-known that unconditional covariance and correlation coefficients also vary significantly over time and multivariate generalized autoregressive model (MGARCH) is able to capture the time-varying variance-covariance matrix for stock market returns and changes in exchange rates. The model is applied to daily Euro-Dollar exchange rates and two stock market indexes from the US economy: Dow-Jones Industrial Average Index and S&P500 Index. The news impact surfaces are also drawn based on the model estimates to see the effects of idiosyncratic shocks in respective markets.

Forecasting influenza in Hong Kong with Google search queries and statistical model fusion.

PubMed

Xu, Qinneng; Gel, Yulia R; Ramirez Ramirez, L Leticia; Nezafati, Kusha; Zhang, Qingpeng; Tsui, Kwok-Leung

2017-01-01

The objective of this study is to investigate predictive utility of online social media and web search queries, particularly, Google search data, to forecast new cases of influenza-like-illness (ILI) in general outpatient clinics (GOPC) in Hong Kong. To mitigate the impact of sensitivity to self-excitement (i.e., fickle media interest) and other artifacts of online social media data, in our approach we fuse multiple offline and online data sources. Four individual models: generalized linear model (GLM), least absolute shrinkage and selection operator (LASSO), autoregressive integrated moving average (ARIMA), and deep learning (DL) with Feedforward Neural Networks (FNN) are employed to forecast ILI-GOPC both one week and two weeks in advance. The covariates include Google search queries, meteorological data, and previously recorded offline ILI. To our knowledge, this is the first study that introduces deep learning methodology into surveillance of infectious diseases and investigates its predictive utility. Furthermore, to exploit the strength from each individual forecasting models, we use statistical model fusion, using Bayesian model averaging (BMA), which allows a systematic integration of multiple forecast scenarios. For each model, an adaptive approach is used to capture the recent relationship between ILI and covariates. DL with FNN appears to deliver the most competitive predictive performance among the four considered individual models. Combing all four models in a comprehensive BMA framework allows to further improve such predictive evaluation metrics as root mean squared error (RMSE) and mean absolute predictive error (MAPE). Nevertheless, DL with FNN remains the preferred method for predicting locations of influenza peaks. The proposed approach can be viewed a feasible alternative to forecast ILI in Hong Kong or other countries where ILI has no constant seasonal trend and influenza data resources are limited. The proposed methodology is easily tractable and computationally efficient.

Generalized seasonal autoregressive integrated moving average models for count data with application to malaria time series with low case numbers.

PubMed

Briët, Olivier J T; Amerasinghe, Priyanie H; Vounatsou, Penelope

2013-01-01

With the renewed drive towards malaria elimination, there is a need for improved surveillance tools. While time series analysis is an important tool for surveillance, prediction and for measuring interventions' impact, approximations by commonly used Gaussian methods are prone to inaccuracies when case counts are low. Therefore, statistical methods appropriate for count data are required, especially during "consolidation" and "pre-elimination" phases. Generalized autoregressive moving average (GARMA) models were extended to generalized seasonal autoregressive integrated moving average (GSARIMA) models for parsimonious observation-driven modelling of non Gaussian, non stationary and/or seasonal time series of count data. The models were applied to monthly malaria case time series in a district in Sri Lanka, where malaria has decreased dramatically in recent years. The malaria series showed long-term changes in the mean, unstable variance and seasonality. After fitting negative-binomial Bayesian models, both a GSARIMA and a GARIMA deterministic seasonality model were selected based on different criteria. Posterior predictive distributions indicated that negative-binomial models provided better predictions than Gaussian models, especially when counts were low. The G(S)ARIMA models were able to capture the autocorrelation in the series. G(S)ARIMA models may be particularly useful in the drive towards malaria elimination, since episode count series are often seasonal and non-stationary, especially when control is increased. Although building and fitting GSARIMA models is laborious, they may provide more realistic prediction distributions than do Gaussian methods and may be more suitable when counts are low.

Generalized Seasonal Autoregressive Integrated Moving Average Models for Count Data with Application to Malaria Time Series with Low Case Numbers

PubMed Central

Briët, Olivier J. T.; Amerasinghe, Priyanie H.; Vounatsou, Penelope

2013-01-01

Introduction With the renewed drive towards malaria elimination, there is a need for improved surveillance tools. While time series analysis is an important tool for surveillance, prediction and for measuring interventions’ impact, approximations by commonly used Gaussian methods are prone to inaccuracies when case counts are low. Therefore, statistical methods appropriate for count data are required, especially during “consolidation” and “pre-elimination” phases. Methods Generalized autoregressive moving average (GARMA) models were extended to generalized seasonal autoregressive integrated moving average (GSARIMA) models for parsimonious observation-driven modelling of non Gaussian, non stationary and/or seasonal time series of count data. The models were applied to monthly malaria case time series in a district in Sri Lanka, where malaria has decreased dramatically in recent years. Results The malaria series showed long-term changes in the mean, unstable variance and seasonality. After fitting negative-binomial Bayesian models, both a GSARIMA and a GARIMA deterministic seasonality model were selected based on different criteria. Posterior predictive distributions indicated that negative-binomial models provided better predictions than Gaussian models, especially when counts were low. The G(S)ARIMA models were able to capture the autocorrelation in the series. Conclusions G(S)ARIMA models may be particularly useful in the drive towards malaria elimination, since episode count series are often seasonal and non-stationary, especially when control is increased. Although building and fitting GSARIMA models is laborious, they may provide more realistic prediction distributions than do Gaussian methods and may be more suitable when counts are low. PMID:23785448

Global CO2 flux inversions from remote-sensing data with systematic errors using hierarchical statistical models

NASA Astrophysics Data System (ADS)

Zammit-Mangion, Andrew; Stavert, Ann; Rigby, Matthew; Ganesan, Anita; Rayner, Peter; Cressie, Noel

2017-04-01

The Orbiting Carbon Observatory-2 (OCO-2) satellite was launched on 2 July 2014, and it has been a source of atmospheric CO2 data since September 2014. The OCO-2 dataset contains a number of variables, but the one of most interest for flux inversion has been the column-averaged dry-air mole fraction (in units of ppm). These global level-2 data offer the possibility of inferring CO2 fluxes at Earth's surface and tracking those fluxes over time. However, as well as having a component of random error, the OCO-2 data have a component of systematic error that is dependent on the instrument's mode, namely land nadir, land glint, and ocean glint. Our statistical approach to CO2-flux inversion starts with constructing a statistical model for the random and systematic errors with parameters that can be estimated from the OCO-2 data and possibly in situ sources from flasks, towers, and the Total Column Carbon Observing Network (TCCON). Dimension reduction of the flux field is achieved through the use of physical basis functions, while temporal evolution of the flux is captured by modelling the basis-function coefficients as a vector autoregressive process. For computational efficiency, flux inversion uses only three months of sensitivities of mole fraction to changes in flux, computed using MOZART; any residual variation is captured through the modelling of a stochastic process that varies smoothly as a function of latitude. The second stage of our statistical approach is to simulate from the posterior distribution of the basis-function coefficients and all unknown parameters given the data using a fully Bayesian Markov chain Monte Carlo (MCMC) algorithm. Estimates and posterior variances of the flux field can then be obtained straightforwardly from this distribution. Our statistical approach is different than others, as it simultaneously makes inference (and quantifies uncertainty) on both the error components' parameters and the CO2 fluxes. We compare it to more classical approaches through an Observing System Simulation Experiment (OSSE) on a global scale. By changing the size of the random and systematic errors in the OSSE, we can determine the corresponding spatial and temporal resolutions at which useful flux signals could be detected from the OCO-2 data.

Probabilistic estimation of splitting coefficients of normal modes of the Earth, and their uncertainties, using an autoregressive technique

NASA Astrophysics Data System (ADS)

Pachhai, S.; Masters, G.; Laske, G.

2017-12-01

Earth's normal-mode spectra are crucial to studying the long wavelength structure of the Earth. Such observations have been used extensively to estimate "splitting coefficients" which, in turn, can be used to determine the three-dimensional velocity and density structure. Most past studies apply a non-linear iterative inversion to estimate the splitting coefficients which requires that the earthquake source is known. However, it is challenging to know the source details, particularly for big events as used in normal-mode analyses. Additionally, the final solution of the non-linear inversion can depend on the choice of damping parameter and starting model. To circumvent the need to know the source, a two-step linear inversion has been developed and successfully applied to many mantle and core sensitive modes. The first step takes combinations of the data from a single event to produce spectra known as "receiver strips". The autoregressive nature of the receiver strips can then be used to estimate the structure coefficients without the need to know the source. Based on this approach, we recently employed a neighborhood algorithm to measure the splitting coefficients for an isolated inner-core sensitive mode (13S2). This approach explores the parameter space efficiently without any need of regularization and finds the structure coefficients which best fit the observed strips. Here, we implement a Bayesian approach to data collected for earthquakes from early 2000 and more recent. This approach combines the data (through likelihood) and prior information to provide rigorous parameter values and their uncertainties for both isolated and coupled modes. The likelihood function is derived from the inferred errors of the receiver strips which allows us to retrieve proper uncertainties. Finally, we apply model selection criteria that balance the trade-offs between fit (likelihood) and model complexity to investigate the degree and type of structure (elastic and anelastic) required to explain the data.

Principal Dynamic Mode Analysis of the Hodgkin–Huxley Equations

PubMed Central

Eikenberry, Steffen E.; Marmarelis, Vasilis Z.

2015-01-01

We develop an autoregressive model framework based on the concept of Principal Dynamic Modes (PDMs) for the process of action potential (AP) generation in the excitable neuronal membrane described by the Hodgkin–Huxley (H–H) equations. The model's exogenous input is injected current, and whenever the membrane potential output exceeds a specified threshold, it is fed back as a second input. The PDMs are estimated from the previously developed Nonlinear Autoregressive Volterra (NARV) model, and represent an efficient functional basis for Volterra kernel expansion. The PDM-based model admits a modular representation, consisting of the forward and feedback PDM bases as linear filterbanks for the exogenous and autoregressive inputs, respectively, whose outputs are then fed to a static nonlinearity composed of polynomials operating on the PDM outputs and cross-terms of pair-products of PDM outputs. A two-step procedure for model reduction is performed: first, influential subsets of the forward and feedback PDM bases are identified and selected as the reduced PDM bases. Second, the terms of the static nonlinearity are pruned. The first step reduces model complexity from a total of 65 coefficients to 27, while the second further reduces the model coefficients to only eight. It is demonstrated that the performance cost of model reduction in terms of out-of-sample prediction accuracy is minimal. Unlike the full model, the eight coefficient pruned model can be easily visualized to reveal the essential system components, and thus the data-derived PDM model can yield insight into the underlying system structure and function. PMID:25630480

Is a matrix exponential specification suitable for the modeling of spatial correlation structures?

PubMed Central

Strauß, Magdalena E.; Mezzetti, Maura; Leorato, Samantha

2018-01-01

This paper investigates the adequacy of the matrix exponential spatial specifications (MESS) as an alternative to the widely used spatial autoregressive models (SAR). To provide as complete a picture as possible, we extend the analysis to all the main spatial models governed by matrix exponentials comparing them with their spatial autoregressive counterparts. We propose a new implementation of Bayesian parameter estimation for the MESS model with vague prior distributions, which is shown to be precise and computationally efficient. Our implementations also account for spatially lagged regressors. We further allow for location-specific heterogeneity, which we model by including spatial splines. We conclude by comparing the performances of the different model specifications in applications to a real data set and by running simulations. Both the applications and the simulations suggest that the spatial splines are a flexible and efficient way to account for spatial heterogeneities governed by unknown mechanisms. PMID:29492375

Detection and classification of subject-generated artifacts in EEG signals using autoregressive models.

PubMed

Lawhern, Vernon; Hairston, W David; McDowell, Kaleb; Westerfield, Marissa; Robbins, Kay

2012-07-15

We examine the problem of accurate detection and classification of artifacts in continuous EEG recordings. Manual identification of artifacts, by means of an expert or panel of experts, can be tedious, time-consuming and infeasible for large datasets. We use autoregressive (AR) models for feature extraction and characterization of EEG signals containing several kinds of subject-generated artifacts. AR model parameters are scale-invariant features that can be used to develop models of artifacts across a population. We use a support vector machine (SVM) classifier to discriminate among artifact conditions using the AR model parameters as features. Results indicate reliable classification among several different artifact conditions across subjects (approximately 94%). These results suggest that AR modeling can be a useful tool for discriminating among artifact signals both within and across individuals. Copyright © 2012 Elsevier B.V. All rights reserved.

Clustering of financial time series

NASA Astrophysics Data System (ADS)

D'Urso, Pierpaolo; Cappelli, Carmela; Di Lallo, Dario; Massari, Riccardo

2013-05-01

This paper addresses the topic of classifying financial time series in a fuzzy framework proposing two fuzzy clustering models both based on GARCH models. In general clustering of financial time series, due to their peculiar features, needs the definition of suitable distance measures. At this aim, the first fuzzy clustering model exploits the autoregressive representation of GARCH models and employs, in the framework of a partitioning around medoids algorithm, the classical autoregressive metric. The second fuzzy clustering model, also based on partitioning around medoids algorithm, uses the Caiado distance, a Mahalanobis-like distance, based on estimated GARCH parameters and covariances that takes into account the information about the volatility structure of time series. In order to illustrate the merits of the proposed fuzzy approaches an application to the problem of classifying 29 time series of Euro exchange rates against international currencies is presented and discussed, also comparing the fuzzy models with their crisp version.

Nonlinear Autoregressive Exogenous modeling of a large anaerobic digester producing biogas from cattle waste.

PubMed

Dhussa, Anil K; Sambi, Surinder S; Kumar, Shashi; Kumar, Sandeep; Kumar, Surendra

2014-10-01

In waste-to-energy plants, there is every likelihood of variations in the quantity and characteristics of the feed. Although intermediate storage tanks are used, but many times these are of inadequate capacity to dampen the variations. In such situations an anaerobic digester treating waste slurry operates under dynamic conditions. In this work a special type of dynamic Artificial Neural Network model, called Nonlinear Autoregressive Exogenous model, is used to model the dynamics of anaerobic digesters by using about one year data collected on the operating digesters. The developed model consists of two hidden layers each having 10 neurons, and uses 18days delay. There are five neurons in input layer and one neuron in output layer for a day. Model predictions of biogas production rate are close to plant performance within ±8% deviation. Copyright © 2014 Elsevier Ltd. All rights reserved.

Microgrid optimal scheduling considering impact of high penetration wind generation

NASA Astrophysics Data System (ADS)

Alanazi, Abdulaziz

The objective of this thesis is to study the impact of high penetration wind energy in economic and reliable operation of microgrids. Wind power is variable, i.e., constantly changing, and nondispatchable, i.e., cannot be controlled by the microgrid controller. Thus an accurate forecasting of wind power is an essential task in order to study its impacts in microgrid operation. Two commonly used forecasting methods including Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) have been used in this thesis to improve the wind power forecasting. The forecasting error is calculated using a Mean Absolute Percentage Error (MAPE) and is improved using the ANN. The wind forecast is further used in the microgrid optimal scheduling problem. The microgrid optimal scheduling is performed by developing a viable model for security-constrained unit commitment (SCUC) based on mixed-integer linear programing (MILP) method. The proposed SCUC is solved for various wind penetration levels and the relationship between the total cost and the wind power penetration is found. In order to reduce microgrid power transfer fluctuations, an additional constraint is proposed and added to the SCUC formulation. The new constraint would control the time-based fluctuations. The impact of the constraint on microgrid SCUC results is tested and validated with numerical analysis. Finally, the applicability of proposed models is demonstrated through numerical simulations.

Estimation of effective brain connectivity with dual Kalman filter and EEG source localization methods.

PubMed

Rajabioun, Mehdi; Nasrabadi, Ali Motie; Shamsollahi, Mohammad Bagher

2017-09-01

Effective connectivity is one of the most important considerations in brain functional mapping via EEG. It demonstrates the effects of a particular active brain region on others. In this paper, a new method is proposed which is based on dual Kalman filter. In this method, firstly by using a brain active localization method (standardized low resolution brain electromagnetic tomography) and applying it to EEG signal, active regions are extracted, and appropriate time model (multivariate autoregressive model) is fitted to extracted brain active sources for evaluating the activity and time dependence between sources. Then, dual Kalman filter is used to estimate model parameters or effective connectivity between active regions. The advantage of this method is the estimation of different brain parts activity simultaneously with the calculation of effective connectivity between active regions. By combining dual Kalman filter with brain source localization methods, in addition to the connectivity estimation between parts, source activity is updated during the time. The proposed method performance has been evaluated firstly by applying it to simulated EEG signals with interacting connectivity simulation between active parts. Noisy simulated signals with different signal to noise ratios are used for evaluating method sensitivity to noise and comparing proposed method performance with other methods. Then the method is applied to real signals and the estimation error during a sweeping window is calculated. By comparing proposed method results in different simulation (simulated and real signals), proposed method gives acceptable results with least mean square error in noisy or real conditions.

Estimation of daily reference evapotranspiration (ETo) using artificial intelligence methods: Offering a new approach for lagged ETo data-based modeling

NASA Astrophysics Data System (ADS)

Mehdizadeh, Saeid

2018-04-01

Evapotranspiration (ET) is considered as a key factor in hydrological and climatological studies, agricultural water management, irrigation scheduling, etc. It can be directly measured using lysimeters. Moreover, other methods such as empirical equations and artificial intelligence methods can be used to model ET. In the recent years, artificial intelligence methods have been widely utilized to estimate reference evapotranspiration (ETo). In the present study, local and external performances of multivariate adaptive regression splines (MARS) and gene expression programming (GEP) were assessed for estimating daily ETo. For this aim, daily weather data of six stations with different climates in Iran, namely Urmia and Tabriz (semi-arid), Isfahan and Shiraz (arid), Yazd and Zahedan (hyper-arid) were employed during 2000-2014. Two types of input patterns consisting of weather data-based and lagged ETo data-based scenarios were considered to develop the models. Four statistical indicators including root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R2), and mean absolute percentage error (MAPE) were used to check the accuracy of models. The local performance of models revealed that the MARS and GEP approaches have the capability to estimate daily ETo using the meteorological parameters and the lagged ETo data as inputs. Nevertheless, the MARS had the best performance in the weather data-based scenarios. On the other hand, considerable differences were not observed in the models' accuracy for the lagged ETo data-based scenarios. In the innovation of this study, novel hybrid models were proposed in the lagged ETo data-based scenarios through combination of MARS and GEP models with autoregressive conditional heteroscedasticity (ARCH) time series model. It was concluded that the proposed novel models named MARS-ARCH and GEP-ARCH improved the performance of ETo modeling compared to the single MARS and GEP. In addition, the external analysis of the performance of models at stations with similar climatic conditions denoted the applicability of nearby station' data for estimation of the daily ETo at target station.

Analysis Monthly Import of Palm Oil Products Using Box-Jenkins Model

NASA Astrophysics Data System (ADS)

Ahmad, Nurul F. Y.; Khalid, Kamil; Saifullah Rusiman, Mohd; Ghazali Kamardan, M.; Roslan, Rozaini; Che-Him, Norziha

2018-04-01

The palm oil industry has been an important component of the national economy especially the agriculture sector. The aim of this study is to identify the pattern of import of palm oil products, to model the time series using Box-Jenkins model and to forecast the monthly import of palm oil products. The method approach is included in the statistical test for verifying the equivalence model and statistical measurement of three models, namely Autoregressive (AR) model, Moving Average (MA) model and Autoregressive Moving Average (ARMA) model. The model identification of all product import palm oil is different in which the AR(1) was found to be the best model for product import palm oil while MA(3) was found to be the best model for products import palm kernel oil. For the palm kernel, MA(4) was found to be the best model. The results forecast for the next four months for products import palm oil, palm kernel oil and palm kernel showed the most significant decrease compared to the actual data.

Corrected goodness-of-fit test in covariance structure analysis.

PubMed

Hayakawa, Kazuhiko

2018-05-17

Many previous studies report simulation evidence that the goodness-of-fit test in covariance structure analysis or structural equation modeling suffers from the overrejection problem when the number of manifest variables is large compared with the sample size. In this study, we demonstrate that one of the tests considered in Browne (1974) can address this long-standing problem. We also propose a simple modification of Satorra and Bentler's mean and variance adjusted test for non-normal data. A Monte Carlo simulation is carried out to investigate the performance of the corrected tests in the context of a confirmatory factor model, a panel autoregressive model, and a cross-lagged panel (panel vector autoregressive) model. The simulation results reveal that the corrected tests overcome the overrejection problem and outperform existing tests in most cases. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

What's in a Day? A Guide to Decomposing the Variance in Intensive Longitudinal Data

PubMed Central

de Haan-Rietdijk, Silvia; Kuppens, Peter; Hamaker, Ellen L.

2016-01-01

In recent years there has been a growing interest in the use of intensive longitudinal research designs to study within-person processes. Examples are studies that use experience sampling data and autoregressive modeling to investigate emotion dynamics and between-person differences therein. Such designs often involve multiple measurements per day and multiple days per person, and it is not clear how this nesting of the data should be accounted for: That is, should such data be considered as two-level data (which is common practice at this point), with occasions nested in persons, or as three-level data with beeps nested in days which are nested in persons. We show that a significance test of the day-level variance in an empty three-level model is not reliable when there is autocorrelation. Furthermore, we show that misspecifying the number of levels can lead to spurious or misleading findings, such as inflated variance or autoregression estimates. Throughout the paper we present instructions and R code for the implementation of the proposed models, which includes a novel three-level AR(1) model that estimates moment-to-moment inertia and day-to-day inertia. Based on our simulations we recommend model selection using autoregressive multilevel models in combination with the AIC. We illustrate this method using empirical emotion data from two independent samples, and discuss the implications and the relevance of the existence of a day level for the field. PMID:27378986

What's in a Day? A Guide to Decomposing the Variance in Intensive Longitudinal Data.

PubMed

de Haan-Rietdijk, Silvia; Kuppens, Peter; Hamaker, Ellen L

2016-01-01

In recent years there has been a growing interest in the use of intensive longitudinal research designs to study within-person processes. Examples are studies that use experience sampling data and autoregressive modeling to investigate emotion dynamics and between-person differences therein. Such designs often involve multiple measurements per day and multiple days per person, and it is not clear how this nesting of the data should be accounted for: That is, should such data be considered as two-level data (which is common practice at this point), with occasions nested in persons, or as three-level data with beeps nested in days which are nested in persons. We show that a significance test of the day-level variance in an empty three-level model is not reliable when there is autocorrelation. Furthermore, we show that misspecifying the number of levels can lead to spurious or misleading findings, such as inflated variance or autoregression estimates. Throughout the paper we present instructions and R code for the implementation of the proposed models, which includes a novel three-level AR(1) model that estimates moment-to-moment inertia and day-to-day inertia. Based on our simulations we recommend model selection using autoregressive multilevel models in combination with the AIC. We illustrate this method using empirical emotion data from two independent samples, and discuss the implications and the relevance of the existence of a day level for the field.

[Correlation coefficient-based classification method of hydrological dependence variability: With auto-regression model as example].

PubMed

Zhao, Yu Xi; Xie, Ping; Sang, Yan Fang; Wu, Zi Yi

2018-04-01

Hydrological process evaluation is temporal dependent. Hydrological time series including dependence components do not meet the data consistency assumption for hydrological computation. Both of those factors cause great difficulty for water researches. Given the existence of hydrological dependence variability, we proposed a correlationcoefficient-based method for significance evaluation of hydrological dependence based on auto-regression model. By calculating the correlation coefficient between the original series and its dependence component and selecting reasonable thresholds of correlation coefficient, this method divided significance degree of dependence into no variability, weak variability, mid variability, strong variability, and drastic variability. By deducing the relationship between correlation coefficient and auto-correlation coefficient in each order of series, we found that the correlation coefficient was mainly determined by the magnitude of auto-correlation coefficient from the 1 order to p order, which clarified the theoretical basis of this method. With the first-order and second-order auto-regression models as examples, the reasonability of the deduced formula was verified through Monte-Carlo experiments to classify the relationship between correlation coefficient and auto-correlation coefficient. This method was used to analyze three observed hydrological time series. The results indicated the coexistence of stochastic and dependence characteristics in hydrological process.

A downscaling scheme for atmospheric variables to drive soil-vegetation-atmosphere transfer models

NASA Astrophysics Data System (ADS)

Schomburg, A.; Venema, V.; Lindau, R.; Ament, F.; Simmer, C.

2010-09-01

For driving soil-vegetation-transfer models or hydrological models, high-resolution atmospheric forcing data is needed. For most applications the resolution of atmospheric model output is too coarse. To avoid biases due to the non-linear processes, a downscaling system should predict the unresolved variability of the atmospheric forcing. For this purpose we derived a disaggregation system consisting of three steps: (1) a bi-quadratic spline-interpolation of the low-resolution data, (2) a so-called `deterministic' part, based on statistical rules between high-resolution surface variables and the desired atmospheric near-surface variables and (3) an autoregressive noise-generation step. The disaggregation system has been developed and tested based on high-resolution model output (400m horizontal grid spacing). A novel automatic search-algorithm has been developed for deriving the deterministic downscaling rules of step 2. When applied to the atmospheric variables of the lowest layer of the atmospheric COSMO-model, the disaggregation is able to adequately reconstruct the reference fields. Applying downscaling step 1 and 2, root mean square errors are decreased. Step 3 finally leads to a close match of the subgrid variability and temporal autocorrelation with the reference fields. The scheme can be applied to the output of atmospheric models, both for stand-alone offline simulations, and a fully coupled model system.

Methodological development for selection of significant predictors explaining fatal road accidents.

PubMed

Dadashova, Bahar; Arenas-Ramírez, Blanca; Mira-McWilliams, José; Aparicio-Izquierdo, Francisco

2016-05-01

Identification of the most relevant factors for explaining road accident occurrence is an important issue in road safety research, particularly for future decision-making processes in transport policy. However model selection for this particular purpose is still an ongoing research. In this paper we propose a methodological development for model selection which addresses both explanatory variable and adequate model selection issues. A variable selection procedure, TIM (two-input model) method is carried out by combining neural network design and statistical approaches. The error structure of the fitted model is assumed to follow an autoregressive process. All models are estimated using Markov Chain Monte Carlo method where the model parameters are assigned non-informative prior distributions. The final model is built using the results of the variable selection. For the application of the proposed methodology the number of fatal accidents in Spain during 2000-2011 was used. This indicator has experienced the maximum reduction internationally during the indicated years thus making it an interesting time series from a road safety policy perspective. Hence the identification of the variables that have affected this reduction is of particular interest for future decision making. The results of the variable selection process show that the selected variables are main subjects of road safety policy measures. Published by Elsevier Ltd.

Essays on Commodity Prices and Macroeconomic Performance of Developing and Resources Rich Economies: Evidence from Kazakhstan

NASA Astrophysics Data System (ADS)

Bilgin, Ferhat I.

My dissertation consists of three essays in empirical macroeconomics. The objective of this research is to use rigorous time-series econometric analysis to investigate the impact of commodity prices on macroeconomic performance of a small, developing and resource-rich country, which is in the process of transition from a purely command and control economy to a market oriented one. Essay 1 studies the relationship between Kazakhstan's GDP, total government expenditure, real effective exchange rate and the world oil price. Specifically, I use the cointegrated vector autoregression (CVAR) and error correction modeling (ECM) approach to identify the long and short-run relations that may exist among these macroeconomic variables. I found a long-run relationship for Kazakhstan's GDP, which depends on government spending and the oil price positively, and on the real effective exchange rate negatively. In the short run, the growth rate of GDP depends on the growth rates of the oil price, investment and the magnitude of the deviation from the long-run equilibrium. Essay 2 studies the inflation process in Kazakhstan based on the analysis of price formation in the following sectors: monetary, external, labor and goods and services. The modeling is conducted from two different perspectives: the first is the monetary model of inflation framework and the second is the mark-up modeling framework. Encompassing test results show that the mark-up model performs better than the monetary model in explaining inflation in Kazakhstan. According to the mark-up inflation model, in the long run, the price level is positively related to unit labor costs, import prices and government administered prices as well the world oil prices. In the short run, the inflation is positively influenced by the previous quarter's inflation, the contemporaneous changes in the government administered prices, oil prices and by the changes of contemporaneous and lagged unit labor costs, and negatively affected by the previous quarter's mark-up. Essay 3 empirically examines the determinants of the trade balance for a small oil exporting country within the context of Kazakhstan. The dominant theory by Harberger-Lauren-Metzler (HML) predicts that positive terms of trade shocks will improve the trade balance in the short run, but will fade away in the long run. I estimate cointegrated vector autoregression (CVAR) and vector error correction model (VECM) to study the long-run and short-run impacts on the trade balance. The results suggest that, in the long run, an increase in the terms of trade has a positive effect on the trade balance, an increase in GDP and appreciation of the real effective exchange rate have negative effect on the trade balance. In the short run, the terms of trade has a direct positive impact on the trade balance, real income and real exchange rate. On the other hand, appreciation of the currency has a negative impact on the trade balance. The error correction term, which represents the deviation from the long- run equilibrium between the trade balance, real income, terms of trade and real exchange rate, has a negative effect on the growth rate of the trade balance. These results provide further evidence to the idea that, in the long run, the HML effect not only depends on the duration of the shock, but also depends on the structure of the economy.

Forecast of Frost Days Based on Monthly Temperatures

NASA Astrophysics Data System (ADS)

Castellanos, M. T.; Tarquis, A. M.; Morató, M. C.; Saa-Requejo, A.

2009-04-01

Although frost can cause considerable crop damage and mitigation practices against forecasted frost exist, frost forecasting technologies have not changed for many years. The paper reports a new method to forecast the monthly number of frost days (FD) for several meteorological stations at Community of Madrid (Spain) based on successive application of two models. The first one is a stochastic model, autoregressive integrated moving average (ARIMA), that forecasts monthly minimum absolute temperature (tmin) and monthly average of minimum temperature (tminav) following Box-Jenkins methodology. The second model relates these monthly temperatures to minimum daily temperature distribution during one month. Three ARIMA models were identified for the time series analyzed with a stational period correspondent to one year. They present the same stational behavior (moving average differenced model) and different non-stational part: autoregressive model (Model 1), moving average differenced model (Model 2) and autoregressive and moving average model (Model 3). At the same time, the results point out that minimum daily temperature (tdmin), for the meteorological stations studied, followed a normal distribution each month with a very similar standard deviation through years. This standard deviation obtained for each station and each month could be used as a risk index for cold months. The application of Model 1 to predict minimum monthly temperatures showed the best FD forecast. This procedure provides a tool for crop managers and crop insurance companies to asses the risk of frost frequency and intensity, so that they can take steps to mitigate against frost damage and estimated the damage that frost would cost. This research was supported by Comunidad de Madrid Research Project 076/92. The cooperation of the Spanish National Meteorological Institute and the Spanish Ministerio de Agricultura, Pesca y Alimentation (MAPA) is gratefully acknowledged.

Bayesian Model Selection under Time Constraints

NASA Astrophysics Data System (ADS)

Hoege, M.; Nowak, W.; Illman, W. A.

2017-12-01

Bayesian model selection (BMS) provides a consistent framework for rating and comparing models in multi-model inference. In cases where models of vastly different complexity compete with each other, we also face vastly different computational runtimes of such models. For instance, time series of a quantity of interest can be simulated by an autoregressive process model that takes even less than a second for one run, or by a partial differential equations-based model with runtimes up to several hours or even days. The classical BMS is based on a quantity called Bayesian model evidence (BME). It determines the model weights in the selection process and resembles a trade-off between bias of a model and its complexity. However, in practice, the runtime of models is another weight relevant factor for model selection. Hence, we believe that it should be included, leading to an overall trade-off problem between bias, variance and computing effort. We approach this triple trade-off from the viewpoint of our ability to generate realizations of the models under a given computational budget. One way to obtain BME values is through sampling-based integration techniques. We argue with the fact that more expensive models can be sampled much less under time constraints than faster models (in straight proportion to their runtime). The computed evidence in favor of a more expensive model is statistically less significant than the evidence computed in favor of a faster model, since sampling-based strategies are always subject to statistical sampling error. We present a straightforward way to include this misbalance into the model weights that are the basis for model selection. Our approach follows directly from the idea of insufficient significance. It is based on a computationally cheap bootstrapping error estimate of model evidence and is easy to implement. The approach is illustrated in a small synthetic modeling study.

Sales forecasting newspaper with ARIMA: A case study

NASA Astrophysics Data System (ADS)

Permatasari, Carina Intan; Sutopo, Wahyudi; Hisjam, Muh.

2018-02-01

People are beginning to switch to using digital media for their daily activities, including changes in newspaper reading patterns to electronic news. In uncertainty trend, the customers of printed newspaper also have switched to electronic news. It has some negative effects on the printed newspaper demand, where there is often an inaccuracy of supply with demand which means that many newspapers are returned. The aim of this paper is to predict printed newspaper demand as accurately as possible to minimize the number of returns, to keep off the missed sales and to restrain the oversupply. The autoregressive integrated moving average (ARIMA) models were adopted to predict the right number of newspapers for a real case study of a newspaper company in Surakarta. The model parameters were found using maximum likelihood method. Then, the software Eviews 9 were utilized to forecasting any particular variables in the newspaper industry. This paper finally presents the appropriate of modeling and sales forecasting newspaper based on the output of the ARIMA models. In particular, it can be recommended to use ARIMA (1, 1, 0) model in predicting the number of newspapers. ARIMA (1, 1, 0) model was chosen from three different models that it provides the smallest value of the mean absolute percentage error (MAPE).

Kepler AutoRegressive Planet Search

NASA Astrophysics Data System (ADS)

Caceres, Gabriel Antonio; Feigelson, Eric

2016-01-01

The Kepler AutoRegressive Planet Search (KARPS) project uses statistical methodology associated with autoregressive (AR) processes to model Kepler lightcurves in order to improve exoplanet transit detection in systems with high stellar variability. We also introduce a planet-search algorithm to detect transits in time-series residuals after application of the AR models. One of the main obstacles in detecting faint planetary transits is the intrinsic stellar variability of the host star. The variability displayed by many stars may have autoregressive properties, wherein later flux values are correlated with previous ones in some manner. Our analysis procedure consisting of three steps: pre-processing of the data to remove discontinuities, gaps and outliers; AR-type model selection and fitting; and transit signal search of the residuals using a new Transit Comb Filter (TCF) that replaces traditional box-finding algorithms. The analysis procedures of the project are applied to a portion of the publicly available Kepler light curve data for the full 4-year mission duration. Tests of the methods have been made on a subset of Kepler Objects of Interest (KOI) systems, classified both as planetary `candidates' and `false positives' by the Kepler Team, as well as a random sample of unclassified systems. We find that the ARMA-type modeling successfully reduces the stellar variability, by a factor of 10 or more in active stars and by smaller factors in more quiescent stars. A typical quiescent Kepler star has an interquartile range (IQR) of ~10 e-/sec, which may improve slightly after modeling, while those with IQR ranging from 20 to 50 e-/sec, have improvements from 20% up to 70%. High activity stars (IQR exceeding 100) markedly improve. A periodogram based on the TCF is constructed to concentrate the signal of these periodic spikes. When a periodic transit is found, the model is displayed on a standard period-folded averaged light curve. Our findings to date on real-data tests of the KARPS methodology will be discussed including confirmation of some Kepler Team `candidate' planets. We also present cases of new possible planetary signals.

Asymmetric impact of rainfall on India's food grain production: evidence from quantile autoregressive distributed lag model

NASA Astrophysics Data System (ADS)

Pal, Debdatta; Mitra, Subrata Kumar

2018-01-01

This study used a quantile autoregressive distributed lag (QARDL) model to capture asymmetric impact of rainfall on food production in India. It was found that the coefficient corresponding to the rainfall in the QARDL increased till the 75th quantile and started decreasing thereafter, though it remained in the positive territory. Another interesting finding is that at the 90th quantile and above the coefficients of rainfall though remained positive was not statistically significant and therefore, the benefit of high rainfall on crop production was not conclusive. However, the impact of other determinants, such as fertilizer and pesticide consumption, is quite uniform over the whole range of the distribution of food grain production.

Exploring the transformation and upgrading of China's economy using electricity consumption data: A VAR-VEC based model

NASA Astrophysics Data System (ADS)

Zhang, Chi; Zhou, Kaile; Yang, Shanlin; Shao, Zhen

2017-05-01

Since the reforming and opening up in 1978, China has experienced a miraculous development. To investigate the transformation and upgrading of China's economy, this study focuses on the relationship between economic growth and electricity consumption of the secondary and tertiary industry in China. This paper captures the dynamic interdependencies among the related variables using a theoretical framework based on a Vector Autoregressive (VAR)-Vector Error Correction (VEC) model. Using the macroeconomic and electricity consumption data, the results show that, for secondary industry, there is only a unidirectional Granger causality from electricity consumption to Gross Domestic Product (GDP) from 1980 to 2000. However, for the tertiary industry, it only occurs that GDP Granger causes electricity consumption from 2001 to 2014. All these conclusions are verified by the impulse response function and variance decomposition. This study has a great significance to reveal the relationship between industrial electricity consumption and the pattern of economic development. Meanwhile, it further suggests that, since China joined the World Trade Organization (WTO) in 2001, the trend of the economic transformation and upgrading has gradually appeared.

A Robust State Estimation Framework Considering Measurement Correlations and Imperfect Synchronization

DOE PAGES

Zhao, Junbo; Wang, Shaobu; Mili, Lamine; ...

2018-01-08

Here, this paper develops a robust power system state estimation framework with the consideration of measurement correlations and imperfect synchronization. In the framework, correlations of SCADA and Phasor Measurements (PMUs) are calculated separately through unscented transformation and a Vector Auto-Regression (VAR) model. In particular, PMU measurements during the waiting period of two SCADA measurement scans are buffered to develop the VAR model with robustly estimated parameters using projection statistics approach. The latter takes into account the temporal and spatial correlations of PMU measurements and provides redundant measurements to suppress bad data and mitigate imperfect synchronization. In case where the SCADAmore » and PMU measurements are not time synchronized, either the forecasted PMU measurements or the prior SCADA measurements from the last estimation run are leveraged to restore system observability. Then, a robust generalized maximum-likelihood (GM)-estimator is extended to integrate measurement error correlations and to handle the outliers in the SCADA and PMU measurements. Simulation results that stem from a comprehensive comparison with other alternatives under various conditions demonstrate the benefits of the proposed framework.« less

Modeling the impact of transport energy consumption on CO2 emission in Pakistan: Evidence from ARDL approach.

PubMed

Danish; Baloch, Muhammad Awais; Suad, Shah

2018-04-01

The objective of this research is to examine the relationship between transport energy consumption, economic growth, and carbon dioxide emission (CO 2 ) from transport sector incorporating foreign direct investment and urbanization. This study is carried out in Pakistan by applying autoregressive distributive lag (ARDL) and vector error correction model (VECM) over 1990-2015. The empirical results indicate a strong significant impact of transport energy consumption on CO 2 emissions from the transportation sector. Furthermore, foreign direct investment also contributes to CO 2 emission. Interestingly, the impact of economic growth and urbanization on transport CO 2 emission is statistically insignificant. Overall, transport energy consumption and foreign direct investment are not environmentally friendly. The new empirical evidence from this study provides a complete picture of the determinants of emissions from the transport sector and these novel findings not only help to advance the existing literature but also can be of special interest to the country's policymakers. So, we urge that government needs to focus on promoting the energy efficient means of transportation to improve environmental quality with less adverse influence on economic growth.

Hedonic approaches based on spatial econometrics and spatial statistics: application to evaluation of project benefits

NASA Astrophysics Data System (ADS)

Tsutsumi, Morito; Seya, Hajime

2009-12-01

This study discusses the theoretical foundation of the application of spatial hedonic approaches—the hedonic approach employing spatial econometrics or/and spatial statistics—to benefits evaluation. The study highlights the limitations of the spatial econometrics approach since it uses a spatial weight matrix that is not employed by the spatial statistics approach. Further, the study presents empirical analyses by applying the Spatial Autoregressive Error Model (SAEM), which is based on the spatial econometrics approach, and the Spatial Process Model (SPM), which is based on the spatial statistics approach. SPMs are conducted based on both isotropy and anisotropy and applied to different mesh sizes. The empirical analysis reveals that the estimated benefits are quite different, especially between isotropic and anisotropic SPM and between isotropic SPM and SAEM; the estimated benefits are similar for SAEM and anisotropic SPM. The study demonstrates that the mesh size does not affect the estimated amount of benefits. Finally, the study provides a confidence interval for the estimated benefits and raises an issue with regard to benefit evaluation.

A Robust State Estimation Framework Considering Measurement Correlations and Imperfect Synchronization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhao, Junbo; Wang, Shaobu; Mili, Lamine

Here, this paper develops a robust power system state estimation framework with the consideration of measurement correlations and imperfect synchronization. In the framework, correlations of SCADA and Phasor Measurements (PMUs) are calculated separately through unscented transformation and a Vector Auto-Regression (VAR) model. In particular, PMU measurements during the waiting period of two SCADA measurement scans are buffered to develop the VAR model with robustly estimated parameters using projection statistics approach. The latter takes into account the temporal and spatial correlations of PMU measurements and provides redundant measurements to suppress bad data and mitigate imperfect synchronization. In case where the SCADAmore » and PMU measurements are not time synchronized, either the forecasted PMU measurements or the prior SCADA measurements from the last estimation run are leveraged to restore system observability. Then, a robust generalized maximum-likelihood (GM)-estimator is extended to integrate measurement error correlations and to handle the outliers in the SCADA and PMU measurements. Simulation results that stem from a comprehensive comparison with other alternatives under various conditions demonstrate the benefits of the proposed framework.« less

Mapping the Spread of Methamphetamine Abuse in California From 1995 to 2008

PubMed Central

Ponicki, William R.; Remer, Lillian G.; Waller, Lance A.; Zhu, Li; Gorman, Dennis M.

2013-01-01

Objectives. From 1983 to 2008, the incidence of methamphetamine abuse and dependence (MA) presenting at hospitals in California increased 13-fold. We assessed whether this growth could be characterized as a drug epidemic. Methods. We geocoded MA discharges to residential zip codes from 1995 through 2008. We related discharges to population and environmental characteristics using Bayesian Poisson conditional autoregressive models, correcting for small area effects and spatial misalignment and enabling an assessment of contagion between areas. Results. MA incidence increased exponentially in 3 phases interrupted by implementation of laws limiting access to methamphetamine precursors. MA growth from 1999 through 2008 was 17% per year. MA was greatest in areas with larger White or Hispanic low-income populations, small household sizes, and good connections to highway systems. Spatial misalignment was a source of bias in estimated effects. Spatial autocorrelation was substantial, accounting for approximately 80% of error variance in the model. Conclusions. From 1995 through 2008, MA exhibited signs of growth and spatial spread characteristic of drug epidemics, spreading most rapidly through low-income White and Hispanic populations living outside dense urban areas. PMID:23078474

Multivariate Autoregressive Modeling and Granger Causality Analysis of Multiple Spike Trains

PubMed Central

Krumin, Michael; Shoham, Shy

2010-01-01

Recent years have seen the emergence of microelectrode arrays and optical methods allowing simultaneous recording of spiking activity from populations of neurons in various parts of the nervous system. The analysis of multiple neural spike train data could benefit significantly from existing methods for multivariate time-series analysis which have proven to be very powerful in the modeling and analysis of continuous neural signals like EEG signals. However, those methods have not generally been well adapted to point processes. Here, we use our recent results on correlation distortions in multivariate Linear-Nonlinear-Poisson spiking neuron models to derive generalized Yule-Walker-type equations for fitting ‘‘hidden” Multivariate Autoregressive models. We use this new framework to perform Granger causality analysis in order to extract the directed information flow pattern in networks of simulated spiking neurons. We discuss the relative merits and limitations of the new method. PMID:20454705

The Multigroup Multilevel Categorical Latent Growth Curve Models

ERIC Educational Resources Information Center

Hung, Lai-Fa

2010-01-01

Longitudinal data describe developmental patterns and enable predictions of individual changes beyond sampled time points. Major methodological issues in longitudinal data include modeling random effects, subject effects, growth curve parameters, and autoregressive residuals. This study embedded the longitudinal model within a multigroup…

The Use of an Autoregressive Integrated Moving Average Model for Prediction of the Incidence of Dysentery in Jiangsu, China.

PubMed

Wang, Kewei; Song, Wentao; Li, Jinping; Lu, Wu; Yu, Jiangang; Han, Xiaofeng

2016-05-01

The aim of this study is to forecast the incidence of bacillary dysentery with a prediction model. We collected the annual and monthly laboratory data of confirmed cases from January 2004 to December 2014. In this study, we applied an autoregressive integrated moving average (ARIMA) model to forecast bacillary dysentery incidence in Jiangsu, China. The ARIMA (1, 1, 1) × (1, 1, 2)12 model fitted exactly with the number of cases during January 2004 to December 2014. The fitted model was then used to predict bacillary dysentery incidence during the period January to August 2015, and the number of cases fell within the model's CI for the predicted number of cases during January-August 2015. This study shows that the ARIMA model fits the fluctuations in bacillary dysentery frequency, and it can be used for future forecasting when applied to bacillary dysentery prevention and control. © 2016 APJPH.

Palm oil price forecasting model: An autoregressive distributed lag (ARDL) approach

NASA Astrophysics Data System (ADS)

Hamid, Mohd Fahmi Abdul; Shabri, Ani

2017-05-01

Palm oil price fluctuated without any clear trend or cyclical pattern in the last few decades. The instability of food commodities price causes it to change rapidly over time. This paper attempts to develop Autoregressive Distributed Lag (ARDL) model in modeling and forecasting the price of palm oil. In order to use ARDL as a forecasting model, this paper modifies the data structure where we only consider lagged explanatory variables to explain the variation in palm oil price. We then compare the performance of this ARDL model with a benchmark model namely ARIMA in term of their comparative forecasting accuracy. This paper also utilize ARDL bound testing approach to co-integration in examining the short run and long run relationship between palm oil price and its determinant; production, stock, and price of soybean as the substitute of palm oil and price of crude oil. The comparative forecasting accuracy suggests that ARDL model has a better forecasting accuracy compared to ARIMA.

A comparison of LOD and UT1-UTC forecasts by different combined prediction techniques

NASA Astrophysics Data System (ADS)

Kosek, W.; Kalarus, M.; Johnson, T. J.; Wooden, W. H.; McCarthy, D. D.; Popiński, W.

Stochastic prediction techniques including autocovariance, autoregressive, autoregressive moving average, and neural networks were applied to the UT1-UTC and Length of Day (LOD) International Earth Rotation and Reference Systems Servive (IERS) EOPC04 time series to evaluate the capabilities of each method. All known effects such as leap seconds and solid Earth zonal tides were first removed from the observed values of UT1-UTC and LOD. Two combination procedures were applied to predict the resulting LODR time series: 1) the combination of the least-squares (LS) extrapolation with a stochastic predition method, and 2) the combination of the discrete wavelet transform (DWT) filtering and a stochastic prediction method. The results of the combination of the LS extrapolation with different stochastic prediction techniques were compared with the results of the UT1-UTC prediction method currently used by the IERS Rapid Service/Prediction Centre (RS/PC). It was found that the prediction accuracy depends on the starting prediction epochs, and for the combined forecast methods, the mean prediction errors for 1 to about 70 days in the future are of the same order as those of the method used by the IERS RS/PC.

Forecast of the number of patients with end-stage renal disease in the United States to the year 2010.

PubMed

Xue, J L; Ma, J Z; Louis, T A; Collins, A J

2001-12-01

As the United States end-stage renal disease (ESRD) program enters the new millennium, the continued growth of the ESRD population poses a challenge for policy makers, health care providers, and financial planners. To assist in future planning for the ESRD program, the growth of patient numbers and Medicare costs was forecasted to the year 2010 by modeling of historical data from 1982 through 1997. A stepwise autoregressive method and exponential smoothing models were used. The forecasting models for ESRD patient numbers demonstrated mean errors of -0.03 to 1.03%, relative to the observed values. The model for Medicare payments demonstrated -0.12% mean error. The R(2) values for the forecasting models ranged from 99.09 to 99.98%. On the basis of trends in patient numbers, this forecast projects average annual growth of the ESRD populations of approximately 4.1% for new patients, 6.4% for long-term ESRD patients, 7.1% for dialysis patients, 6.1% for patients with functioning transplants, and 8.2% for patients on waiting lists for transplants, as well as 7.7% for Medicare expenditures. The numbers of patients with ESRD in 2010 are forecasted to be 129,200 +/- 7742 (95% confidence limits) new patients, 651,330 +/- 15,874 long-term ESRD patients, 520,240 +/- 25,609 dialysis patients, 178,806 +/- 4349 patients with functioning transplants, and 95,550 +/- 5478 patients on waiting lists. The forecasted Medicare expenditures are projected to increase to $28.3 +/- 1.7 billion by 2010. These projections are subject to many factors that may alter the actual growth, compared with the historical patterns. They do, however, provide a basis for discussing the future growth of the ESRD program and how the ESRD community can meet the challenges ahead.

PRESS-based EFOR algorithm for the dynamic parametrical modeling of nonlinear MDOF systems

NASA Astrophysics Data System (ADS)

Liu, Haopeng; Zhu, Yunpeng; Luo, Zhong; Han, Qingkai

2017-09-01

In response to the identification problem concerning multi-degree of freedom (MDOF) nonlinear systems, this study presents the extended forward orthogonal regression (EFOR) based on predicted residual sums of squares (PRESS) to construct a nonlinear dynamic parametrical model. The proposed parametrical model is based on the non-linear autoregressive with exogenous inputs (NARX) model and aims to explicitly reveal the physical design parameters of the system. The PRESS-based EFOR algorithm is proposed to identify such a model for MDOF systems. By using the algorithm, we built a common-structured model based on the fundamental concept of evaluating its generalization capability through cross-validation. The resulting model aims to prevent over-fitting with poor generalization performance caused by the average error reduction ratio (AERR)-based EFOR algorithm. Then, a functional relationship is established between the coefficients of the terms and the design parameters of the unified model. Moreover, a 5-DOF nonlinear system is taken as a case to illustrate the modeling of the proposed algorithm. Finally, a dynamic parametrical model of a cantilever beam is constructed from experimental data. Results indicate that the dynamic parametrical model of nonlinear systems, which depends on the PRESS-based EFOR, can accurately predict the output response, thus providing a theoretical basis for the optimal design of modeling methods for MDOF nonlinear systems.

Heterogeneous autoregressive model with structural break using nearest neighbor truncation volatility estimators for DAX.

PubMed

Chin, Wen Cheong; Lee, Min Cherng; Yap, Grace Lee Ching

2016-01-01

High frequency financial data modelling has become one of the important research areas in the field of financial econometrics. However, the possible structural break in volatile financial time series often trigger inconsistency issue in volatility estimation. In this study, we propose a structural break heavy-tailed heterogeneous autoregressive (HAR) volatility econometric model with the enhancement of jump-robust estimators. The breakpoints in the volatility are captured by dummy variables after the detection by Bai-Perron sequential multi breakpoints procedure. In order to further deal with possible abrupt jump in the volatility, the jump-robust volatility estimators are composed by using the nearest neighbor truncation approach, namely the minimum and median realized volatility. Under the structural break improvements in both the models and volatility estimators, the empirical findings show that the modified HAR model provides the best performing in-sample and out-of-sample forecast evaluations as compared with the standard HAR models. Accurate volatility forecasts have direct influential to the application of risk management and investment portfolio analysis.

Modeling feeding behavior of swine to detect illness

USDA-ARS?s Scientific Manuscript database

Animal well-being may be improved by detecting disruptions in feeding behavior indicative of challenged animals. The objectives of this study were to 1) develop and optimize an autoregressive model by adjusting sensitivity of the model to detect disruptions in feeding time; 2) test the model on dail...

Evaluating simulations of daily discharge from large watersheds using autoregression and an index of flashiness

USDA-ARS?s Scientific Manuscript database

Watershed models are calibrated to simulate stream discharge as accurately as possible. Modelers will often calculate model validation statistics on aggregate (often monthly) time periods, rather than the daily step at which models typically operate. This is because daily hydrologic data exhibit lar...

Vector autoregressive model approach for forecasting outflow cash in Central Java

NASA Astrophysics Data System (ADS)

hoyyi, Abdul; Tarno; Maruddani, Di Asih I.; Rahmawati, Rita

2018-05-01

Multivariate time series model is more applied in economic and business problems as well as in other fields. Applications in economic problems one of them is the forecasting of outflow cash. This problem can be viewed globally in the sense that there is no spatial effect between regions, so the model used is the Vector Autoregressive (VAR) model. The data used in this research is data on the money supply in Bank Indonesia Semarang, Solo, Purwokerto and Tegal. The model used in this research is VAR (1), VAR (2) and VAR (3) models. Ordinary Least Square (OLS) is used to estimate parameters. The best model selection criteria use the smallest Akaike Information Criterion (AIC). The result of data analysis shows that the AIC value of VAR (1) model is equal to 42.72292, VAR (2) equals 42.69119 and VAR (3) equals 42.87662. The difference in AIC values is not significant. Based on the smallest AIC value criteria, the best model is the VAR (2) model. This model has satisfied the white noise assumption.

Prediction of UT1-UTC, LOD and AAM χ3 by combination of least-squares and multivariate stochastic methods

NASA Astrophysics Data System (ADS)

Niedzielski, Tomasz; Kosek, Wiesław

2008-02-01

This article presents the application of a multivariate prediction technique for predicting universal time (UT1-UTC), length of day (LOD) and the axial component of atmospheric angular momentum (AAM χ 3). The multivariate predictions of LOD and UT1-UTC are generated by means of the combination of (1) least-squares (LS) extrapolation of models for annual, semiannual, 18.6-year, 9.3-year oscillations and for the linear trend, and (2) multivariate autoregressive (MAR) stochastic prediction of LS residuals (LS + MAR). The MAR technique enables the use of the AAM χ 3 time-series as the explanatory variable for the computation of LOD or UT1-UTC predictions. In order to evaluate the performance of this approach, two other prediction schemes are also applied: (1) LS extrapolation, (2) combination of LS extrapolation and univariate autoregressive (AR) prediction of LS residuals (LS + AR). The multivariate predictions of AAM χ 3 data, however, are computed as a combination of the extrapolation of the LS model for annual and semiannual oscillations and the LS + MAR. The AAM χ 3 predictions are also compared with LS extrapolation and LS + AR prediction. It is shown that the predictions of LOD and UT1-UTC based on LS + MAR taking into account the axial component of AAM are more accurate than the predictions of LOD and UT1-UTC based on LS extrapolation or on LS + AR. In particular, the UT1-UTC predictions based on LS + MAR during El Niño/La Niña events exhibit considerably smaller prediction errors than those calculated by means of LS or LS + AR. The AAM χ 3 time-series is predicted using LS + MAR with higher accuracy than applying LS extrapolation itself in the case of medium-term predictions (up to 100 days in the future). However, the predictions of AAM χ 3 reveal the best accuracy for LS + AR.

Modelling thermal comfort of visitors at urban squares in hot and arid climate using NN-ARX soft computing method

NASA Astrophysics Data System (ADS)

Kariminia, Shahab; Motamedi, Shervin; Shamshirband, Shahaboddin; Piri, Jamshid; Mohammadi, Kasra; Hashim, Roslan; Roy, Chandrabhushan; Petković, Dalibor; Bonakdari, Hossein

2016-05-01

Visitors utilize the urban space based on their thermal perception and thermal environment. The thermal adaptation engages the user's behavioural, physiological and psychological aspects. These aspects play critical roles in user's ability to assess the thermal environments. Previous studies have rarely addressed the effects of identified factors such as gender, age and locality on outdoor thermal comfort, particularly in hot, dry climate. This study investigated the thermal comfort of visitors at two city squares in Iran based on their demographics as well as the role of thermal environment. Assessing the thermal comfort required taking physical measurement and questionnaire survey. In this study, a non-linear model known as the neural network autoregressive with exogenous input (NN-ARX) was employed. Five indices of physiological equivalent temperature (PET), predicted mean vote (PMV), standard effective temperature (SET), thermal sensation votes (TSVs) and mean radiant temperature ( T mrt) were trained and tested using the NN-ARX. Then, the results were compared to the artificial neural network (ANN) and the adaptive neuro-fuzzy inference system (ANFIS). The findings showed the superiority of the NN-ARX over the ANN and the ANFIS. For the NN-ARX model, the statistical indicators of the root mean square error (RMSE) and the mean absolute error (MAE) were 0.53 and 0.36 for the PET, 1.28 and 0.71 for the PMV, 2.59 and 1.99 for the SET, 0.29 and 0.08 for the TSV and finally 0.19 and 0.04 for the T mrt.

Comparisons of Four Methods for Estimating a Dynamic Factor Model

ERIC Educational Resources Information Center

Zhang, Zhiyong; Hamaker, Ellen L.; Nesselroade, John R.

2008-01-01

Four methods for estimating a dynamic factor model, the direct autoregressive factor score (DAFS) model, are evaluated and compared. The first method estimates the DAFS model using a Kalman filter algorithm based on its state space model representation. The second one employs the maximum likelihood estimation method based on the construction of a…

Potential predictability and forecast skill in ensemble climate forecast: a skill-persistence rule

NASA Astrophysics Data System (ADS)

Jin, Yishuai; Rong, Xinyao; Liu, Zhengyu

2017-12-01

This study investigates the factors relationship between the forecast skills for the real world (actual skill) and perfect model (perfect skill) in ensemble climate model forecast with a series of fully coupled general circulation model forecast experiments. It is found that the actual skill for sea surface temperature (SST) in seasonal forecast is substantially higher than the perfect skill on a large part of the tropical oceans, especially the tropical Indian Ocean and the central-eastern Pacific Ocean. The higher actual skill is found to be related to the higher observational SST persistence, suggesting a skill-persistence rule: a higher SST persistence in the real world than in the model could overwhelm the model bias to produce a higher forecast skill for the real world than for the perfect model. The relation between forecast skill and persistence is further proved using a first-order autoregressive model (AR1) analytically for theoretical solutions and numerically for analogue experiments. The AR1 model study shows that the skill-persistence rule is strictly valid in the case of infinite ensemble size, but could be distorted by sampling errors and non-AR1 processes. This study suggests that the so called "perfect skill" is model dependent and cannot serve as an accurate estimate of the true upper limit of real world prediction skill, unless the model can capture at least the persistence property of the observation.

Kepler AutoRegressive Planet Search

NASA Astrophysics Data System (ADS)

Feigelson, Eric

NASA's Kepler mission is the source of more exoplanets than any other instrument, but the discovery depends on complex statistical analysis procedures embedded in the Kepler pipeline. A particular challenge is mitigating irregular stellar variability without loss of sensitivity to faint periodic planetary transits. This proposal presents a two-stage alternative analysis procedure. First, parametric autoregressive ARFIMA models, commonly used in econometrics, remove most of the stellar variations. Second, a novel matched filter is used to create a periodogram from which transit-like periodicities are identified. This analysis procedure, the Kepler AutoRegressive Planet Search (KARPS), is confirming most of the Kepler Objects of Interest and is expected to identify additional planetary candidates. The proposed research will complete application of the KARPS methodology to the prime Kepler mission light curves of 200,000: stars, and compare the results with Kepler Objects of Interest obtained with the Kepler pipeline. We will then conduct a variety of astronomical studies based on the KARPS results. Important subsamples will be extracted including Habitable Zone planets, hot super-Earths, grazing-transit hot Jupiters, and multi-planet systems. Groundbased spectroscopy of poorly studied candidates will be performed to better characterize the host stars. Studies of stellar variability will then be pursued based on KARPS analysis. The autocorrelation function and nonstationarity measures will be used to identify spotted stars at different stages of autoregressive modeling. Periodic variables with folded light curves inconsistent with planetary transits will be identified; they may be eclipsing or mutually-illuminating binary star systems. Classification of stellar variables with KARPS-derived statistical properties will be attempted. KARPS procedures will then be applied to archived K2 data to identify planetary transits and characterize stellar variability.

Nonrandom variability in respiratory cycle parameters of humans during stage 2 sleep.

PubMed

Modarreszadeh, M; Bruce, E N; Gothe, B

1990-08-01

We analyzed breath-to-breath inspiratory time (TI), expiratory time (TE), inspiratory volume (VI), and minute ventilation (Vm) from 11 normal subjects during stage 2 sleep. The analysis consisted of 1) fitting first- and second-order autoregressive models (AR1 and AR2) and 2) obtaining the power spectra of the data by fast-Fourier transform. For the AR2 model, the only coefficients that were statistically different from zero were the average alpha 1 (a1) for TI, VI, and Vm (a1 = 0.19, 0.29, and 0.15, respectively). However, the power spectra of all parameters often exhibited peaks at low frequency (less than 0.2 cycles/breath) and/or at high frequency (greater than 0.2 cycles/breath), indicative of periodic oscillations. After accounting for the corrupting effects of added oscillations on the a1 estimates, we conclude that 1) breath-to-breath fluctuations of VI, and to a lesser extent TI and Vm, exhibit a first-order autoregressive structure such that fluctuations of each breath are positively correlated with those of immediately preceding breaths and 2) the correlated components of variability in TE are mostly due to discrete high- and/or low-frequency oscillations with no underlying autoregressive structure. We propose that the autoregressive structure of VI, TI, and Vm during spontaneous breathing in stage 2 sleep may reflect either a central neural mechanism or the effects of noise in respiratory chemical feedback loops; the presence of low-frequency oscillations, seen more often in Vm, suggests possible instability in the chemical feedback loops. Mechanisms of high-frequency periodicities, seen more often in TE, are unknown.

Self-organising mixture autoregressive model for non-stationary time series modelling.

PubMed

Ni, He; Yin, Hujun

2008-12-01

Modelling non-stationary time series has been a difficult task for both parametric and nonparametric methods. One promising solution is to combine the flexibility of nonparametric models with the simplicity of parametric models. In this paper, the self-organising mixture autoregressive (SOMAR) network is adopted as a such mixture model. It breaks time series into underlying segments and at the same time fits local linear regressive models to the clusters of segments. In such a way, a global non-stationary time series is represented by a dynamic set of local linear regressive models. Neural gas is used for a more flexible structure of the mixture model. Furthermore, a new similarity measure has been introduced in the self-organising network to better quantify the similarity of time series segments. The network can be used naturally in modelling and forecasting non-stationary time series. Experiments on artificial, benchmark time series (e.g. Mackey-Glass) and real-world data (e.g. numbers of sunspots and Forex rates) are presented and the results show that the proposed SOMAR network is effective and superior to other similar approaches.

Stock price forecasting based on time series analysis

NASA Astrophysics Data System (ADS)

Chi, Wan Le

2018-05-01

Using the historical stock price data to set up a sequence model to explain the intrinsic relationship of data, the future stock price can forecasted. The used models are auto-regressive model, moving-average model and autoregressive-movingaverage model. The original data sequence of unit root test was used to judge whether the original data sequence was stationary. The non-stationary original sequence as a first order difference needed further processing. Then the stability of the sequence difference was re-inspected. If it is still non-stationary, the second order differential processing of the sequence is carried out. Autocorrelation diagram and partial correlation diagram were used to evaluate the parameters of the identified ARMA model, including coefficients of the model and model order. Finally, the model was used to forecast the fitting of the shanghai composite index daily closing price with precision. Results showed that the non-stationary original data series was stationary after the second order difference. The forecast value of shanghai composite index daily closing price was closer to actual value, indicating that the ARMA model in the paper was a certain accuracy.

Are U.S. Military Interventions Contagious over Time? Intervention Timing and Its Implications for Force Planning

DTIC Science & Technology

2013-01-01

29 3.5. ARIMA Models , Temporal Clustering of Conflicts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.6...39 3.9. ARIMA Models ...variance across a distribution. Autoregressive integrated moving average ( ARIMA ) models are used with time-series data sets and are designed to capture

A High Precision Prediction Model Using Hybrid Grey Dynamic Model

ERIC Educational Resources Information Center

Li, Guo-Dong; Yamaguchi, Daisuke; Nagai, Masatake; Masuda, Shiro

2008-01-01

In this paper, we propose a new prediction analysis model which combines the first order one variable Grey differential equation Model (abbreviated as GM(1,1) model) from grey system theory and time series Autoregressive Integrated Moving Average (ARIMA) model from statistics theory. We abbreviate the combined GM(1,1) ARIMA model as ARGM(1,1)…

Relating Factor Models for Longitudinal Data to Quasi-Simplex and NARMA Models

ERIC Educational Resources Information Center

Rovine, Michael J.; Molenaar, Peter C. M.

2005-01-01

In this article we show the one-factor model can be rewritten as a quasi-simplex model. Using this result along with addition theorems from time series analysis, we describe a common general model, the nonstationary autoregressive moving average (NARMA) model, that includes as a special case, any latent variable model with continuous indicators…

A generalized least squares regression approach for computing effect sizes in single-case research: application examples.

PubMed

Maggin, Daniel M; Swaminathan, Hariharan; Rogers, Helen J; O'Keeffe, Breda V; Sugai, George; Horner, Robert H

2011-06-01

A new method for deriving effect sizes from single-case designs is proposed. The strategy is applicable to small-sample time-series data with autoregressive errors. The method uses Generalized Least Squares (GLS) to model the autocorrelation of the data and estimate regression parameters to produce an effect size that represents the magnitude of treatment effect from baseline to treatment phases in standard deviation units. In this paper, the method is applied to two published examples using common single case designs (i.e., withdrawal and multiple-baseline). The results from these studies are described, and the method is compared to ten desirable criteria for single-case effect sizes. Based on the results of this application, we conclude with observations about the use of GLS as a support to visual analysis, provide recommendations for future research, and describe implications for practice. Copyright © 2011 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.

Investigating Long-Range Dependence in American Treasury Bills Variations and Volatilities during Stable and Unstable Periods

NASA Astrophysics Data System (ADS)

Lahmiri, Salim

2016-05-01

Detrended fluctuation analysis (DFA) is used to examine long-range dependence in variations and volatilities of American treasury bills (TB) during periods of low and high movements in TB rates. Volatility series are estimated by generalized autoregressive conditional heteroskedasticity (GARCH) model under Gaussian, Student, and the generalized error distribution (GED) assumptions. The DFA-based Hurst exponents from 3-month, 6-month, and 1-year TB data indicates that in general the dynamics of the TB variations process is characterized by persistence during stable time period (before 2008 international financial crisis) and anti-persistence during unstable time period (post-2008 international financial crisis). For volatility series, it is found that; for stable period; 3-month volatility process is more likely random, 6-month volatility process is anti-persistent, and 1-year volatility process is persistent. For unstable period, estimation results show that the generating process is persistent for all maturities and for all distributional assumptions.

A study on industrial accident rate forecasting and program development of estimated zero accident time in Korea.

PubMed

Kim, Tae-gu; Kang, Young-sig; Lee, Hyung-won

2011-01-01

To begin a zero accident campaign for industry, the first thing is to estimate the industrial accident rate and the zero accident time systematically. This paper considers the social and technical change of the business environment after beginning the zero accident campaign through quantitative time series analysis methods. These methods include sum of squared errors (SSE), regression analysis method (RAM), exponential smoothing method (ESM), double exponential smoothing method (DESM), auto-regressive integrated moving average (ARIMA) model, and the proposed analytic function method (AFM). The program is developed to estimate the accident rate, zero accident time and achievement probability of an efficient industrial environment. In this paper, MFC (Microsoft Foundation Class) software of Visual Studio 2008 was used to develop a zero accident program. The results of this paper will provide major information for industrial accident prevention and be an important part of stimulating the zero accident campaign within all industrial environments.

Getting It Right Matters: Climate Spectra and Their Estimation

NASA Astrophysics Data System (ADS)

Privalsky, Victor; Yushkov, Vladislav

2018-06-01

In many recent publications, climate spectra estimated with different methods from observed, GCM-simulated, and reconstructed time series contain many peaks at time scales from a few years to many decades and even centuries. However, respective spectral estimates obtained with the autoregressive (AR) and multitapering (MTM) methods showed that spectra of climate time series are smooth and contain no evidence of periodic or quasi-periodic behavior. Four order selection criteria for the autoregressive models were studied and proven sufficiently reliable for 25 time series of climate observations at individual locations or spatially averaged at local-to-global scales. As time series of climate observations are short, an alternative reliable nonparametric approach is Thomson's MTM. These results agree with both the earlier climate spectral analyses and the Markovian stochastic model of climate.

On the maximum-entropy/autoregressive modeling of time series

NASA Technical Reports Server (NTRS)

Chao, B. F.

1984-01-01

The autoregressive (AR) model of a random process is interpreted in the light of the Prony's relation which relates a complex conjugate pair of poles of the AR process in the z-plane (or the z domain) on the one hand, to the complex frequency of one complex harmonic function in the time domain on the other. Thus the AR model of a time series is one that models the time series as a linear combination of complex harmonic functions, which include pure sinusoids and real exponentials as special cases. An AR model is completely determined by its z-domain pole configuration. The maximum-entropy/autogressive (ME/AR) spectrum, defined on the unit circle of the z-plane (or the frequency domain), is nothing but a convenient, but ambiguous visual representation. It is asserted that the position and shape of a spectral peak is determined by the corresponding complex frequency, and the height of the spectral peak contains little information about the complex amplitude of the complex harmonic functions.

Automatic load forecasting. Final report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nelson, D.J.; Vemuri, S.

A method which lends itself to on-line forecasting of hourly electric loads is presented and the results of its use are compared to models developed using the Box-Jenkins method. The method consists of processing the historical hourly loads with a sequential least-squares estimator to identify a finite order autoregressive model which in turn is used to obtain a parsimonious autoregressive-moving average model. A procedure is also defined for incorporating temperature as a variable to improve forecasts where loads are temperature dependent. The method presented has several advantages in comparison to the Box-Jenkins method including much less human intervention and improvedmore » model identification. The method has been tested using three-hourly data from the Lincoln Electric System, Lincoln, Nebraska. In the exhaustive analyses performed on this data base this method produced significantly better results than the Box-Jenkins method. The method also proved to be more robust in that greater confidence could be placed in the accuracy of models based upon the various measures available at the identification stage.« less

Alcohol and liver cirrhosis mortality in the United States: comparison of methods for the analyses of time-series panel data models.

PubMed

Ye, Yu; Kerr, William C

2011-01-01

To explore various model specifications in estimating relationships between liver cirrhosis mortality rates and per capita alcohol consumption in aggregate-level cross-section time-series data. Using a series of liver cirrhosis mortality rates from 1950 to 2002 for 47 U.S. states, the effects of alcohol consumption were estimated from pooled autoregressive integrated moving average (ARIMA) models and 4 types of panel data models: generalized estimating equation, generalized least square, fixed effect, and multilevel models. Various specifications of error term structure under each type of model were also examined. Different approaches controlling for time trends and for using concurrent or accumulated consumption as predictors were also evaluated. When cirrhosis mortality was predicted by total alcohol, highly consistent estimates were found between ARIMA and panel data analyses, with an average overall effect of 0.07 to 0.09. Less consistent estimates were derived using spirits, beer, and wine consumption as predictors. When multiple geographic time series are combined as panel data, none of existent models could accommodate all sources of heterogeneity such that any type of panel model must employ some form of generalization. Different types of panel data models should thus be estimated to examine the robustness of findings. We also suggest cautious interpretation when beverage-specific volumes are used as predictors. Copyright © 2010 by the Research Society on Alcoholism.

The importance of different frequency bands in predicting subcutaneous glucose concentration in type 1 diabetic patients.

PubMed

Lu, Yinghui; Gribok, Andrei V; Ward, W Kenneth; Reifman, Jaques

2010-08-01

We investigated the relative importance and predictive power of different frequency bands of subcutaneous glucose signals for the short-term (0-50 min) forecasting of glucose concentrations in type 1 diabetic patients with data-driven autoregressive (AR) models. The study data consisted of minute-by-minute glucose signals collected from nine deidentified patients over a five-day period using continuous glucose monitoring devices. AR models were developed using single and pairwise combinations of frequency bands of the glucose signal and compared with a reference model including all bands. The results suggest that: for open-loop applications, there is no need to explicitly represent exogenous inputs, such as meals and insulin intake, in AR models; models based on a single-frequency band, with periods between 60-120 min and 150-500 min, yield good predictive power (error <3 mg/dL) for prediction horizons of up to 25 min; models based on pairs of bands produce predictions that are indistinguishable from those of the reference model as long as the 60-120 min period band is included; and AR models can be developed on signals of short length (approximately 300 min), i.e., ignoring long circadian rhythms, without any detriment in prediction accuracy. Together, these findings provide insights into efficient development of more effective and parsimonious data-driven models for short-term prediction of glucose concentrations in diabetic patients.

Autoregressive statistical pattern recognition algorithms for damage detection in civil structures

NASA Astrophysics Data System (ADS)

Yao, Ruigen; Pakzad, Shamim N.

2012-08-01

Statistical pattern recognition has recently emerged as a promising set of complementary methods to system identification for automatic structural damage assessment. Its essence is to use well-known concepts in statistics for boundary definition of different pattern classes, such as those for damaged and undamaged structures. In this paper, several statistical pattern recognition algorithms using autoregressive models, including statistical control charts and hypothesis testing, are reviewed as potentially competitive damage detection techniques. To enhance the performance of statistical methods, new feature extraction techniques using model spectra and residual autocorrelation, together with resampling-based threshold construction methods, are proposed. Subsequently, simulated acceleration data from a multi degree-of-freedom system is generated to test and compare the efficiency of the existing and proposed algorithms. Data from laboratory experiments conducted on a truss and a large-scale bridge slab model are then used to further validate the damage detection methods and demonstrate the superior performance of proposed algorithms.

Comparison of two non-convex mixed-integer nonlinear programming algorithms applied to autoregressive moving average model structure and parameter estimation

NASA Astrophysics Data System (ADS)

Uilhoorn, F. E.

2016-10-01

In this article, the stochastic modelling approach proposed by Box and Jenkins is treated as a mixed-integer nonlinear programming (MINLP) problem solved with a mesh adaptive direct search and a real-coded genetic class of algorithms. The aim is to estimate the real-valued parameters and non-negative integer, correlated structure of stationary autoregressive moving average (ARMA) processes. The maximum likelihood function of the stationary ARMA process is embedded in Akaike's information criterion and the Bayesian information criterion, whereas the estimation procedure is based on Kalman filter recursions. The constraints imposed on the objective function enforce stability and invertibility. The best ARMA model is regarded as the global minimum of the non-convex MINLP problem. The robustness and computational performance of the MINLP solvers are compared with brute-force enumeration. Numerical experiments are done for existing time series and one new data set.

Nonlinear and Quasi-Simplex Patterns in Latent Growth Models

ERIC Educational Resources Information Center

Bianconcini, Silvia

2012-01-01

In the SEM literature, simplex and latent growth models have always been considered competing approaches for the analysis of longitudinal data, even if they are strongly connected and both of specific importance. General dynamic models, which simultaneously estimate autoregressive structures and latent curves, have been recently proposed in the…

An Integrated Enrollment Forecast Model. IR Applications, Volume 15, January 18, 2008

ERIC Educational Resources Information Center

Chen, Chau-Kuang

2008-01-01

Enrollment forecasting is the central component of effective budget and program planning. The integrated enrollment forecast model is developed to achieve a better understanding of the variables affecting student enrollment and, ultimately, to perform accurate forecasts. The transfer function model of the autoregressive integrated moving average…

Time Series ARIMA Models of Undergraduate Grade Point Average.

ERIC Educational Resources Information Center

Rogers, Bruce G.

The Auto-Regressive Integrated Moving Average (ARIMA) Models, often referred to as Box-Jenkins models, are regression methods for analyzing sequential dependent observations with large amounts of data. The Box-Jenkins approach, a three-stage procedure consisting of identification, estimation and diagnosis, was used to select the most appropriate…

Intercept Centering and Time Coding in Latent Difference Score Models

ERIC Educational Resources Information Center

Grimm, Kevin J.

2012-01-01

Latent difference score (LDS) models combine benefits derived from autoregressive and latent growth curve models allowing for time-dependent influences and systematic change. The specification and descriptions of LDS models include an initial level of ability or trait plus an accumulation of changes. A limitation of this specification is that the…

Autoregressive spatially varying coefficients model for predicting daily PM2.5 using VIIRS satellite AOT

NASA Astrophysics Data System (ADS)

Schliep, E. M.; Gelfand, A. E.; Holland, D. M.

2015-12-01

There is considerable demand for accurate air quality information in human health analyses. The sparsity of ground monitoring stations across the United States motivates the need for advanced statistical models to predict air quality metrics, such as PM2.5, at unobserved sites. Remote sensing technologies have the potential to expand our knowledge of PM2.5 spatial patterns beyond what we can predict from current PM2.5 monitoring networks. Data from satellites have an additional advantage in not requiring extensive emission inventories necessary for most atmospheric models that have been used in earlier data fusion models for air pollution. Statistical models combining monitoring station data with satellite-obtained aerosol optical thickness (AOT), also referred to as aerosol optical depth (AOD), have been proposed in the literature with varying levels of success in predicting PM2.5. The benefit of using AOT is that satellites provide complete gridded spatial coverage. However, the challenges involved with using it in fusion models are (1) the correlation between the two data sources varies both in time and in space, (2) the data sources are temporally and spatially misaligned, and (3) there is extensive missingness in the monitoring data and also in the satellite data due to cloud cover. We propose a hierarchical autoregressive spatially varying coefficients model to jointly model the two data sources, which addresses the foregoing challenges. Additionally, we offer formal model comparison for competing models in terms of model fit and out of sample prediction of PM2.5. The models are applied to daily observations of PM2.5 and AOT in the summer months of 2013 across the conterminous United States. Most notably, during this time period, we find small in-sample improvement incorporating AOT into our autoregressive model but little out-of-sample predictive improvement.

Forecasting influenza in Hong Kong with Google search queries and statistical model fusion

PubMed Central

Ramirez Ramirez, L. Leticia; Nezafati, Kusha; Zhang, Qingpeng; Tsui, Kwok-Leung

2017-01-01

Background The objective of this study is to investigate predictive utility of online social media and web search queries, particularly, Google search data, to forecast new cases of influenza-like-illness (ILI) in general outpatient clinics (GOPC) in Hong Kong. To mitigate the impact of sensitivity to self-excitement (i.e., fickle media interest) and other artifacts of online social media data, in our approach we fuse multiple offline and online data sources. Methods Four individual models: generalized linear model (GLM), least absolute shrinkage and selection operator (LASSO), autoregressive integrated moving average (ARIMA), and deep learning (DL) with Feedforward Neural Networks (FNN) are employed to forecast ILI-GOPC both one week and two weeks in advance. The covariates include Google search queries, meteorological data, and previously recorded offline ILI. To our knowledge, this is the first study that introduces deep learning methodology into surveillance of infectious diseases and investigates its predictive utility. Furthermore, to exploit the strength from each individual forecasting models, we use statistical model fusion, using Bayesian model averaging (BMA), which allows a systematic integration of multiple forecast scenarios. For each model, an adaptive approach is used to capture the recent relationship between ILI and covariates. Results DL with FNN appears to deliver the most competitive predictive performance among the four considered individual models. Combing all four models in a comprehensive BMA framework allows to further improve such predictive evaluation metrics as root mean squared error (RMSE) and mean absolute predictive error (MAPE). Nevertheless, DL with FNN remains the preferred method for predicting locations of influenza peaks. Conclusions The proposed approach can be viewed a feasible alternative to forecast ILI in Hong Kong or other countries where ILI has no constant seasonal trend and influenza data resources are limited. The proposed methodology is easily tractable and computationally efficient. PMID:28464015

An Intelligent Decision Support System for Workforce Forecast

DTIC Science & Technology

2011-01-01

ARIMA ) model to forecast the demand for construction skills in Hong Kong. This model was based...Decision Trees ARIMA Rule Based Forecasting Segmentation Forecasting Regression Analysis Simulation Modeling Input-Output Models LP and NLP Markovian...data • When results are needed as a set of easily interpretable rules 4.1.4 ARIMA Auto-regressive, integrated, moving-average ( ARIMA ) models

Value-at-Risk forecasts by a spatiotemporal model in Chinese stock market

NASA Astrophysics Data System (ADS)

Gong, Pu; Weng, Yingliang

2016-01-01

This paper generalizes a recently proposed spatial autoregressive model and introduces a spatiotemporal model for forecasting stock returns. We support the view that stock returns are affected not only by the absolute values of factors such as firm size, book-to-market ratio and momentum but also by the relative values of factors like trading volume ranking and market capitalization ranking in each period. This article studies a new method for constructing stocks' reference groups; the method is called quartile method. Applying the method empirically to the Shanghai Stock Exchange 50 Index, we compare the daily volatility forecasting performance and the out-of-sample forecasting performance of Value-at-Risk (VaR) estimated by different models. The empirical results show that the spatiotemporal model performs surprisingly well in terms of capturing spatial dependences among individual stocks, and it produces more accurate VaR forecasts than the other three models introduced in the previous literature. Moreover, the findings indicate that both allowing for serial correlation in the disturbances and using time-varying spatial weight matrices can greatly improve the predictive accuracy of a spatial autoregressive model.

A Multilevel AR(1) Model: Allowing for Inter-Individual Differences in Trait-Scores, Inertia, and Innovation Variance.

PubMed

Jongerling, Joran; Laurenceau, Jean-Philippe; Hamaker, Ellen L

2015-01-01

In this article we consider a multilevel first-order autoregressive [AR(1)] model with random intercepts, random autoregression, and random innovation variance (i.e., the level 1 residual variance). Including random innovation variance is an important extension of the multilevel AR(1) model for two reasons. First, between-person differences in innovation variance are important from a substantive point of view, in that they capture differences in sensitivity and/or exposure to unmeasured internal and external factors that influence the process. Second, using simulation methods we show that modeling the innovation variance as fixed across individuals, when it should be modeled as a random effect, leads to biased parameter estimates. Additionally, we use simulation methods to compare maximum likelihood estimation to Bayesian estimation of the multilevel AR(1) model and investigate the trade-off between the number of individuals and the number of time points. We provide an empirical illustration by applying the extended multilevel AR(1) model to daily positive affect ratings from 89 married women over the course of 42 consecutive days.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Santini, Danilo J.; Poyer, David A.

Vector error correction (VEC) was used to test the importance of a theoretical causal chain from transportation fuel cost to vehicle sales to macroeconomic activity. Real transportation fuel cost was broken into two cost components: real gasoline price (rpgas) and real personal consumption of gasoline and other goods (gas). Real personal consumption expenditure on vehicles (RMVE) represented vehicle sales. Real gross domestic product (rGDP) was used as the measure of macroeconomic activity. The VEC estimates used quarterly data from the third quarter of 1952 to the first quarter of 2014. Controlling for the financial causes of the recent Great Recession,more » real homeowners’ equity (equity) and real credit market instruments liability (real consumer debt, rcmdebt) were included. Results supported the primary hypothesis of the research, but also introduced evidence that another financial path through equity is important, and that use of the existing fleet of vehicles (not just sales of vehicles) is an important transport-related contributor to macroeconomic activity. Consumer debt reduction is estimated to be a powerful short-run force reducing vehicle sales. Findings are interpreted in the context of the recent Greene, Lee, and Hopson (2012) (hereafter GLH) estimation of the magnitude of three distinct macroeconomic damage effects that result from dependence on imported oil, the price of which is manipulated by the Organization of Petroleum Exporting Countries (OPEC). The three negative macroeconomic impacts are due to (1) dislocation (positive oil price shock), (2) high oil price levels, and (3) a high value of the quantity of oil imports times an oil price delta (cartel price less competitive price). The third of these is the wealth effect. The VEC model addresses the first two, but the software output from the model (impulse response plots) does not isolate them. Nearly all prior statistical tests in the literature have used vector autoregression (VAR) and autoregressive distributed lag models that considered effects of oil price changes, but did not account for effects of oil price levels. Gasoline prices were rarely examined. The tests conducted in this report evaluate gasoline instead of oil.« less

How are you feeling?: A personalized methodology for predicting mental states from temporally observable physical and behavioral information.

PubMed

Tuarob, Suppawong; Tucker, Conrad S; Kumara, Soundar; Giles, C Lee; Pincus, Aaron L; Conroy, David E; Ram, Nilam

2017-04-01

It is believed that anomalous mental states such as stress and anxiety not only cause suffering for the individuals, but also lead to tragedies in some extreme cases. The ability to predict the mental state of an individual at both current and future time periods could prove critical to healthcare practitioners. Currently, the practical way to predict an individual's mental state is through mental examinations that involve psychological experts performing the evaluations. However, such methods can be time and resource consuming, mitigating their broad applicability to a wide population. Furthermore, some individuals may also be unaware of their mental states or may feel uncomfortable to express themselves during the evaluations. Hence, their anomalous mental states could remain undetected for a prolonged period of time. The objective of this work is to demonstrate the ability of using advanced machine learning based approaches to generate mathematical models that predict current and future mental states of an individual. The problem of mental state prediction is transformed into the time series forecasting problem, where an individual is represented as a multivariate time series stream of monitored physical and behavioral attributes. A personalized mathematical model is then automatically generated to capture the dependencies among these attributes, which is used for prediction of mental states for each individual. In particular, we first illustrate the drawbacks of traditional multivariate time series forecasting methodologies such as vector autoregression. Then, we show that such issues could be mitigated by using machine learning regression techniques which are modified for capturing temporal dependencies in time series data. A case study using the data from 150 human participants illustrates that the proposed machine learning based forecasting methods are more suitable for high-dimensional psychological data than the traditional vector autoregressive model in terms of both magnitude of error and directional accuracy. These results not only present a successful usage of machine learning techniques in psychological studies, but also serve as a building block for multiple medical applications that could rely on an automated system to gauge individuals' mental states. Copyright © 2017 Elsevier Inc. All rights reserved.

Zero-inflated spatio-temporal models for disease mapping.

PubMed

Torabi, Mahmoud

2017-05-01

In this paper, our aim is to analyze geographical and temporal variability of disease incidence when spatio-temporal count data have excess zeros. To that end, we consider random effects in zero-inflated Poisson models to investigate geographical and temporal patterns of disease incidence. Spatio-temporal models that employ conditionally autoregressive smoothing across the spatial dimension and B-spline smoothing over the temporal dimension are proposed. The analysis of these complex models is computationally difficult from the frequentist perspective. On the other hand, the advent of the Markov chain Monte Carlo algorithm has made the Bayesian analysis of complex models computationally convenient. Recently developed data cloning method provides a frequentist approach to mixed models that is also computationally convenient. We propose to use data cloning, which yields to maximum likelihood estimation, to conduct frequentist analysis of zero-inflated spatio-temporal modeling of disease incidence. One of the advantages of the data cloning approach is that the prediction and corresponding standard errors (or prediction intervals) of smoothing disease incidence over space and time is easily obtained. We illustrate our approach using a real dataset of monthly children asthma visits to hospital in the province of Manitoba, Canada, during the period April 2006 to March 2010. Performance of our approach is also evaluated through a simulation study. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Model Identification of Integrated ARMA Processes

ERIC Educational Resources Information Center

Stadnytska, Tetiana; Braun, Simone; Werner, Joachim

2008-01-01

This article evaluates the Smallest Canonical Correlation Method (SCAN) and the Extended Sample Autocorrelation Function (ESACF), automated methods for the Autoregressive Integrated Moving-Average (ARIMA) model selection commonly available in current versions of SAS for Windows, as identification tools for integrated processes. SCAN and ESACF can…

Image interpolation by adaptive 2-D autoregressive modeling and soft-decision estimation.

PubMed

Zhang, Xiangjun; Wu, Xiaolin

2008-06-01

The challenge of image interpolation is to preserve spatial details. We propose a soft-decision interpolation technique that estimates missing pixels in groups rather than one at a time. The new technique learns and adapts to varying scene structures using a 2-D piecewise autoregressive model. The model parameters are estimated in a moving window in the input low-resolution image. The pixel structure dictated by the learnt model is enforced by the soft-decision estimation process onto a block of pixels, including both observed and estimated. The result is equivalent to that of a high-order adaptive nonseparable 2-D interpolation filter. This new image interpolation approach preserves spatial coherence of interpolated images better than the existing methods, and it produces the best results so far over a wide range of scenes in both PSNR measure and subjective visual quality. Edges and textures are well preserved, and common interpolation artifacts (blurring, ringing, jaggies, zippering, etc.) are greatly reduced.

Noise source and reactor stability estimation in a boiling water reactor using a multivariate autoregressive model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kanemoto, S.; Andoh, Y.; Sandoz, S.A.

1984-10-01

A method for evaluating reactor stability in boiling water reactors has been developed. The method is based on multivariate autoregressive (M-AR) modeling of steady-state neutron and process noise signals. In this method, two kinds of power spectral densities (PSDs) for the measured neutron signal and the corresponding noise source signal are separately identified by the M-AR modeling. The closed- and open-loop stability parameters are evaluated from these PSDs. The method is applied to actual plant noise data that were measured together with artificial perturbation test data. Stability parameters identified from noise data are compared to those from perturbation test data,more » and it is shown that both results are in good agreement. In addition to these stability estimations, driving noise sources for the neutron signal are evaluated by the M-AR modeling. Contributions from void, core flow, and pressure noise sources are quantitatively evaluated, and the void noise source is shown to be the most dominant.« less

Gas Chromatography Data Classification Based on Complex Coefficients of an Autoregressive Model

DOE PAGES

Zhao, Weixiang; Morgan, Joshua T.; Davis, Cristina E.

2008-01-01

This paper introduces autoregressive (AR) modeling as a novel method to classify outputs from gas chromatography (GC). The inverse Fourier transformation was applied to the original sensor data, and then an AR model was applied to transform data to generate AR model complex coefficients. This series of coefficients effectively contains a compressed version of all of the information in the original GC signal output. We applied this method to chromatograms resulting from proliferating bacteria species grown in culture. Three types of neural networks were used to classify the AR coefficients: backward propagating neural network (BPNN), radial basis function-principal component analysismore » (RBF-PCA) approach, and radial basis function-partial least squares regression (RBF-PLSR) approach. This exploratory study demonstrates the feasibility of using complex root coefficient patterns to distinguish various classes of experimental data, such as those from the different bacteria species. This cognition approach also proved to be robust and potentially useful for freeing us from time alignment of GC signals.« less

Comparing lagged linear correlation, lagged regression, Granger causality, and vector autoregression for uncovering associations in EHR data.

PubMed

Levine, Matthew E; Albers, David J; Hripcsak, George

2016-01-01

Time series analysis methods have been shown to reveal clinical and biological associations in data collected in the electronic health record. We wish to develop reliable high-throughput methods for identifying adverse drug effects that are easy to implement and produce readily interpretable results. To move toward this goal, we used univariate and multivariate lagged regression models to investigate associations between twenty pairs of drug orders and laboratory measurements. Multivariate lagged regression models exhibited higher sensitivity and specificity than univariate lagged regression in the 20 examples, and incorporating autoregressive terms for labs and drugs produced more robust signals in cases of known associations among the 20 example pairings. Moreover, including inpatient admission terms in the model attenuated the signals for some cases of unlikely associations, demonstrating how multivariate lagged regression models' explicit handling of context-based variables can provide a simple way to probe for health-care processes that confound analyses of EHR data.

A novel framework to simulating non-stationary, non-linear, non-Normal hydrological time series using Markov Switching Autoregressive Models

NASA Astrophysics Data System (ADS)

Birkel, C.; Paroli, R.; Spezia, L.; Tetzlaff, D.; Soulsby, C.

2012-12-01

In this paper we present a novel model framework using the class of Markov Switching Autoregressive Models (MSARMs) to examine catchments as complex stochastic systems that exhibit non-stationary, non-linear and non-Normal rainfall-runoff and solute dynamics. Hereby, MSARMs are pairs of stochastic processes, one observed and one unobserved, or hidden. We model the unobserved process as a finite state Markov chain and assume that the observed process, given the hidden Markov chain, is conditionally autoregressive, which means that the current observation depends on its recent past (system memory). The model is fully embedded in a Bayesian analysis based on Markov Chain Monte Carlo (MCMC) algorithms for model selection and uncertainty assessment. Hereby, the autoregressive order and the dimension of the hidden Markov chain state-space are essentially self-selected. The hidden states of the Markov chain represent unobserved levels of variability in the observed process that may result from complex interactions of hydroclimatic variability on the one hand and catchment characteristics affecting water and solute storage on the other. To deal with non-stationarity, additional meteorological and hydrological time series along with a periodic component can be included in the MSARMs as covariates. This extension allows identification of potential underlying drivers of temporal rainfall-runoff and solute dynamics. We applied the MSAR model framework to streamflow and conservative tracer (deuterium and oxygen-18) time series from an intensively monitored 2.3 km2 experimental catchment in eastern Scotland. Statistical time series analysis, in the form of MSARMs, suggested that the streamflow and isotope tracer time series are not controlled by simple linear rules. MSARMs showed that the dependence of current observations on past inputs observed by transport models often in form of the long-tailing of travel time and residence time distributions can be efficiently explained by non-stationarity either of the system input (climatic variability) and/or the complexity of catchment storage characteristics. The statistical model is also capable of reproducing short (event) and longer-term (inter-event) and wet and dry dynamical "hydrological states". These reflect the non-linear transport mechanisms of flow pathways induced by transient climatic and hydrological variables and modified by catchment characteristics. We conclude that MSARMs are a powerful tool to analyze the temporal dynamics of hydrological data, allowing for explicit integration of non-stationary, non-linear and non-Normal characteristics.

Estimating error statistics for Chambon-la-Forêt observatory definitive data

NASA Astrophysics Data System (ADS)

Lesur, Vincent; Heumez, Benoît; Telali, Abdelkader; Lalanne, Xavier; Soloviev, Anatoly

2017-08-01

We propose a new algorithm for calibrating definitive observatory data with the goal of providing users with estimates of the data error standard deviations (SDs). The algorithm has been implemented and tested using Chambon-la-Forêt observatory (CLF) data. The calibration process uses all available data. It is set as a large, weakly non-linear, inverse problem that ultimately provides estimates of baseline values in three orthogonal directions, together with their expected standard deviations. For this inverse problem, absolute data error statistics are estimated from two series of absolute measurements made within a day. Similarly, variometer data error statistics are derived by comparing variometer data time series between different pairs of instruments over few years. The comparisons of these time series led us to use an autoregressive process of order 1 (AR1 process) as a prior for the baselines. Therefore the obtained baselines do not vary smoothly in time. They have relatively small SDs, well below 300 pT when absolute data are recorded twice a week - i.e. within the daily to weekly measures recommended by INTERMAGNET. The algorithm was tested against the process traditionally used to derive baselines at CLF observatory, suggesting that statistics are less favourable when this latter process is used. Finally, two sets of definitive data were calibrated using the new algorithm. Their comparison shows that the definitive data SDs are less than 400 pT and may be slightly overestimated by our process: an indication that more work is required to have proper estimates of absolute data error statistics. For magnetic field modelling, the results show that even on isolated sites like CLF observatory, there are very localised signals over a large span of temporal frequencies that can be as large as 1 nT. The SDs reported here encompass signals of a few hundred metres and less than a day wavelengths.

Effect of formal and informal likelihood functions on uncertainty assessment in a single event rainfall-runoff model

NASA Astrophysics Data System (ADS)

Nourali, Mahrouz; Ghahraman, Bijan; Pourreza-Bilondi, Mohsen; Davary, Kamran

2016-09-01

In the present study, DREAM(ZS), Differential Evolution Adaptive Metropolis combined with both formal and informal likelihood functions, is used to investigate uncertainty of parameters of the HEC-HMS model in Tamar watershed, Golestan province, Iran. In order to assess the uncertainty of 24 parameters used in HMS, three flood events were used to calibrate and one flood event was used to validate the posterior distributions. Moreover, performance of seven different likelihood functions (L1-L7) was assessed by means of DREAM(ZS)approach. Four likelihood functions, L1-L4, Nash-Sutcliffe (NS) efficiency, Normalized absolute error (NAE), Index of agreement (IOA), and Chiew-McMahon efficiency (CM), is considered as informal, whereas remaining (L5-L7) is represented in formal category. L5 focuses on the relationship between the traditional least squares fitting and the Bayesian inference, and L6, is a hetereoscedastic maximum likelihood error (HMLE) estimator. Finally, in likelihood function L7, serial dependence of residual errors is accounted using a first-order autoregressive (AR) model of the residuals. According to the results, sensitivities of the parameters strongly depend on the likelihood function, and vary for different likelihood functions. Most of the parameters were better defined by formal likelihood functions L5 and L7 and showed a high sensitivity to model performance. Posterior cumulative distributions corresponding to the informal likelihood functions L1, L2, L3, L4 and the formal likelihood function L6 are approximately the same for most of the sub-basins, and these likelihood functions depict almost a similar effect on sensitivity of parameters. 95% total prediction uncertainty bounds bracketed most of the observed data. Considering all the statistical indicators and criteria of uncertainty assessment, including RMSE, KGE, NS, P-factor and R-factor, results showed that DREAM(ZS) algorithm performed better under formal likelihood functions L5 and L7, but likelihood function L5 may result in biased and unreliable estimation of parameters due to violation of the residualerror assumptions. Thus, likelihood function L7 provides posterior distribution of model parameters credibly and therefore can be employed for further applications.

Drought Patterns Forecasting using an Auto-Regressive Logistic Model

NASA Astrophysics Data System (ADS)

del Jesus, M.; Sheffield, J.; Méndez Incera, F. J.; Losada, I. J.; Espejo, A.

2014-12-01

Drought is characterized by a water deficit that may manifest across a large range of spatial and temporal scales. Drought may create important socio-economic consequences, many times of catastrophic dimensions. A quantifiable definition of drought is elusive because depending on its impacts, consequences and generation mechanism, different water deficit periods may be identified as a drought by virtue of some definitions but not by others. Droughts are linked to the water cycle and, although a climate change signal may not have emerged yet, they are also intimately linked to climate.In this work we develop an auto-regressive logistic model for drought prediction at different temporal scales that makes use of a spatially explicit framework. Our model allows to include covariates, continuous or categorical, to improve the performance of the auto-regressive component.Our approach makes use of dimensionality reduction (principal component analysis) and classification techniques (K-Means and maximum dissimilarity) to simplify the representation of complex climatic patterns, such as sea surface temperature (SST) and sea level pressure (SLP), while including information on their spatial structure, i.e. considering their spatial patterns. This procedure allows us to include in the analysis multivariate representation of complex climatic phenomena, as the El Niño-Southern Oscillation. We also explore the impact of other climate-related variables such as sun spots. The model allows to quantify the uncertainty of the forecasts and can be easily adapted to make predictions under future climatic scenarios. The framework herein presented may be extended to other applications such as flash flood analysis, or risk assessment of natural hazards.

Explanation of power law behavior of autoregressive conditional duration processes based on the random multiplicative process

NASA Astrophysics Data System (ADS)

Sato, Aki-Hiro

2004-04-01

Autoregressive conditional duration (ACD) processes, which have the potential to be applied to power law distributions of complex systems found in natural science, life science, and social science, are analyzed both numerically and theoretically. An ACD(1) process exhibits the singular second order moment, which suggests that its probability density function (PDF) has a power law tail. It is verified that the PDF of the ACD(1) has a power law tail with an arbitrary exponent depending on a model parameter. On the basis of theory of the random multiplicative process a relation between the model parameter and the power law exponent is theoretically derived. It is confirmed that the relation is valid from numerical simulations. An application of the ACD(1) to intervals between two successive transactions in a foreign currency market is shown.

Autoregressive-model-based missing value estimation for DNA microarray time series data.

PubMed

Choong, Miew Keen; Charbit, Maurice; Yan, Hong

2009-01-01

Missing value estimation is important in DNA microarray data analysis. A number of algorithms have been developed to solve this problem, but they have several limitations. Most existing algorithms are not able to deal with the situation where a particular time point (column) of the data is missing entirely. In this paper, we present an autoregressive-model-based missing value estimation method (ARLSimpute) that takes into account the dynamic property of microarray temporal data and the local similarity structures in the data. ARLSimpute is especially effective for the situation where a particular time point contains many missing values or where the entire time point is missing. Experiment results suggest that our proposed algorithm is an accurate missing value estimator in comparison with other imputation methods on simulated as well as real microarray time series datasets.

Explanation of power law behavior of autoregressive conditional duration processes based on the random multiplicative process.

PubMed

Sato, Aki-Hiro

2004-04-01

Autoregressive conditional duration (ACD) processes, which have the potential to be applied to power law distributions of complex systems found in natural science, life science, and social science, are analyzed both numerically and theoretically. An ACD(1) process exhibits the singular second order moment, which suggests that its probability density function (PDF) has a power law tail. It is verified that the PDF of the ACD(1) has a power law tail with an arbitrary exponent depending on a model parameter. On the basis of theory of the random multiplicative process a relation between the model parameter and the power law exponent is theoretically derived. It is confirmed that the relation is valid from numerical simulations. An application of the ACD(1) to intervals between two successive transactions in a foreign currency market is shown.

Neonatal heart rate prediction.

PubMed

Abdel-Rahman, Yumna; Jeremic, Aleksander; Tan, Kenneth

2009-01-01

Technological advances have caused a decrease in the number of infant deaths. Pre-term infants now have a substantially increased chance of survival. One of the mechanisms that is vital to saving the lives of these infants is continuous monitoring and early diagnosis. With continuous monitoring huge amounts of data are collected with so much information embedded in them. By using statistical analysis this information can be extracted and used to aid diagnosis and to understand development. In this study we have a large dataset containing over 180 pre-term infants whose heart rates were recorded over the length of their stay in the Neonatal Intensive Care Unit (NICU). We test two types of models, empirical bayesian and autoregressive moving average. We then attempt to predict future values. The autoregressive moving average model showed better results but required more computation.

On The Value at Risk Using Bayesian Mixture Laplace Autoregressive Approach for Modelling the Islamic Stock Risk Investment

NASA Astrophysics Data System (ADS)

Miftahurrohmah, Brina; Iriawan, Nur; Fithriasari, Kartika

2017-06-01

Stocks are known as the financial instruments traded in the capital market which have a high level of risk. Their risks are indicated by their uncertainty of their return which have to be accepted by investors in the future. The higher the risk to be faced, the higher the return would be gained. Therefore, the measurements need to be made against the risk. Value at Risk (VaR) as the most popular risk measurement method, is frequently ignore when the pattern of return is not uni-modal Normal. The calculation of the risks using VaR method with the Normal Mixture Autoregressive (MNAR) approach has been considered. This paper proposes VaR method couple with the Mixture Laplace Autoregressive (MLAR) that would be implemented for analysing the first three biggest capitalization Islamic stock return in JII, namely PT. Astra International Tbk (ASII), PT. Telekomunikasi Indonesia Tbk (TLMK), and PT. Unilever Indonesia Tbk (UNVR). Parameter estimation is performed by employing Bayesian Markov Chain Monte Carlo (MCMC) approaches.

Kepler AutoRegressive Planet Search (KARPS)

NASA Astrophysics Data System (ADS)

Caceres, Gabriel

2018-01-01

One of the main obstacles in detecting faint planetary transits is the intrinsic stellar variability of the host star. The Kepler AutoRegressive Planet Search (KARPS) project implements statistical methodology associated with autoregressive processes (in particular, ARIMA and ARFIMA) to model stellar lightcurves in order to improve exoplanet transit detection. We also develop a novel Transit Comb Filter (TCF) applied to the AR residuals which provides a periodogram analogous to the standard Box-fitting Least Squares (BLS) periodogram. We train a random forest classifier on known Kepler Objects of Interest (KOIs) using select features from different stages of this analysis, and then use ROC curves to define and calibrate the criteria to recover the KOI planet candidates with high fidelity. These statistical methods are detailed in a contributed poster (Feigelson et al., this meeting).These procedures are applied to the full DR25 dataset of NASA’s Kepler mission. Using the classification criteria, a vast majority of known KOIs are recovered and dozens of new KARPS Candidate Planets (KCPs) discovered, including ultra-short period exoplanets. The KCPs will be briefly presented and discussed.

A two-model hydrologic ensemble prediction of hydrograph: case study from the upper Nysa Klodzka river basin (SW Poland)

NASA Astrophysics Data System (ADS)

Niedzielski, Tomasz; Mizinski, Bartlomiej

2016-04-01

The HydroProg system has been elaborated in frame of the research project no. 2011/01/D/ST10/04171 of the National Science Centre of Poland and is steadily producing multimodel ensemble predictions of hydrograph in real time. Although there are six ensemble members available at present, the longest record of predictions and their statistics is available for two data-based models (uni- and multivariate autoregressive models). Thus, we consider 3-hour predictions of water levels, with lead times ranging from 15 to 180 minutes, computed every 15 minutes since August 2013 for the Nysa Klodzka basin (SW Poland) using the two approaches and their two-model ensemble. Since the launch of the HydroProg system there have been 12 high flow episodes, and the objective of this work is to present the performance of the two-model ensemble in the process of forecasting these events. For a sake of brevity, we limit our investigation to a single gauge located at the Nysa Klodzka river in the town of Klodzko, which is centrally located in the studied basin. We identified certain regular scenarios of how the models perform in predicting the high flows in Klodzko. At the initial phase of the high flow, well before the rising limb of hydrograph, the two-model ensemble is found to provide the most skilful prognoses of water levels. However, while forecasting the rising limb of hydrograph, either the two-model solution or the vector autoregressive model offers the best predictive performance. In addition, it is hypothesized that along with the development of the rising limb phase, the vector autoregression becomes the most skilful approach amongst the scrutinized ones. Our simple two-model exercise confirms that multimodel hydrologic ensemble predictions cannot be treated as universal solutions suitable for forecasting the entire high flow event, but their superior performance may hold only for certain phases of a high flow.

The Effects of Autocorrelation on the Curve-of-Factors Growth Model

ERIC Educational Resources Information Center

Murphy, Daniel L.; Beretvas, S. Natasha; Pituch, Keenan A.

2011-01-01

This simulation study examined the performance of the curve-of-factors model (COFM) when autocorrelation and growth processes were present in the first-level factor structure. In addition to the standard curve-of factors growth model, 2 new models were examined: one COFM that included a first-order autoregressive autocorrelation parameter, and a…

Maximum Likelihood Dynamic Factor Modeling for Arbitrary "N" and "T" Using SEM

ERIC Educational Resources Information Center

Voelkle, Manuel C.; Oud, Johan H. L.; von Oertzen, Timo; Lindenberger, Ulman

2012-01-01

This article has 3 objectives that build on each other. First, we demonstrate how to obtain maximum likelihood estimates for dynamic factor models (the direct autoregressive factor score model) with arbitrary "T" and "N" by means of structural equation modeling (SEM) and compare the approach to existing methods. Second, we go beyond standard time…

Circular Conditional Autoregressive Modeling of Vector Fields.

PubMed

Modlin, Danny; Fuentes, Montse; Reich, Brian

2012-02-01

As hurricanes approach landfall, there are several hazards for which coastal populations must be prepared. Damaging winds, torrential rains, and tornadoes play havoc with both the coast and inland areas; but, the biggest seaside menace to life and property is the storm surge. Wind fields are used as the primary forcing for the numerical forecasts of the coastal ocean response to hurricane force winds, such as the height of the storm surge and the degree of coastal flooding. Unfortunately, developments in deterministic modeling of these forcings have been hindered by computational expenses. In this paper, we present a multivariate spatial model for vector fields, that we apply to hurricane winds. We parameterize the wind vector at each site in polar coordinates and specify a circular conditional autoregressive (CCAR) model for the vector direction, and a spatial CAR model for speed. We apply our framework for vector fields to hurricane surface wind fields for Hurricane Floyd of 1999 and compare our CCAR model to prior methods that decompose wind speed and direction into its N-S and W-E cardinal components.

Circular Conditional Autoregressive Modeling of Vector Fields*

PubMed Central

Modlin, Danny; Fuentes, Montse; Reich, Brian

2013-01-01

As hurricanes approach landfall, there are several hazards for which coastal populations must be prepared. Damaging winds, torrential rains, and tornadoes play havoc with both the coast and inland areas; but, the biggest seaside menace to life and property is the storm surge. Wind fields are used as the primary forcing for the numerical forecasts of the coastal ocean response to hurricane force winds, such as the height of the storm surge and the degree of coastal flooding. Unfortunately, developments in deterministic modeling of these forcings have been hindered by computational expenses. In this paper, we present a multivariate spatial model for vector fields, that we apply to hurricane winds. We parameterize the wind vector at each site in polar coordinates and specify a circular conditional autoregressive (CCAR) model for the vector direction, and a spatial CAR model for speed. We apply our framework for vector fields to hurricane surface wind fields for Hurricane Floyd of 1999 and compare our CCAR model to prior methods that decompose wind speed and direction into its N-S and W-E cardinal components. PMID:24353452

Short-term forecasts gain in accuracy. [Regression technique using ''Box-Jenkins'' analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

Box-Jenkins time-series models offer accuracy for short-term forecasts that compare with large-scale macroeconomic forecasts. Utilities need to be able to forecast peak demand in order to plan their generating, transmitting, and distribution systems. This new method differs from conventional models by not assuming specific data patterns, but by fitting available data into a tentative pattern on the basis of auto-correlations. Three types of models (autoregressive, moving average, or mixed autoregressive/moving average) can be used according to which provides the most appropriate combination of autocorrelations and related derivatives. Major steps in choosing a model are identifying potential models, estimating the parametersmore » of the problem, and running a diagnostic check to see if the model fits the parameters. The Box-Jenkins technique is well suited for seasonal patterns, which makes it possible to have as short as hourly forecasts of load demand. With accuracy up to two years, the method will allow electricity price-elasticity forecasting that can be applied to facility planning and rate design. (DCK)« less

Hybrid wavelet-support vector machine approach for modelling rainfall-runoff process.

PubMed

Komasi, Mehdi; Sharghi, Soroush

2016-01-01

Because of the importance of water resources management, the need for accurate modeling of the rainfall-runoff process has rapidly grown in the past decades. Recently, the support vector machine (SVM) approach has been used by hydrologists for rainfall-runoff modeling and the other fields of hydrology. Similar to the other artificial intelligence models, such as artificial neural network (ANN) and adaptive neural fuzzy inference system, the SVM model is based on the autoregressive properties. In this paper, the wavelet analysis was linked to the SVM model concept for modeling the rainfall-runoff process of Aghchai and Eel River watersheds. In this way, the main time series of two variables, rainfall and runoff, were decomposed to multiple frequent time series by wavelet theory; then, these time series were imposed as input data on the SVM model in order to predict the runoff discharge one day ahead. The obtained results show that the wavelet SVM model can predict both short- and long-term runoff discharges by considering the seasonality effects. Also, the proposed hybrid model is relatively more appropriate than classical autoregressive ones such as ANN and SVM because it uses the multi-scale time series of rainfall and runoff data in the modeling process.

Structural Equation Modeling of Multivariate Time Series

ERIC Educational Resources Information Center

du Toit, Stephen H. C.; Browne, Michael W.

2007-01-01

The covariance structure of a vector autoregressive process with moving average residuals (VARMA) is derived. It differs from other available expressions for the covariance function of a stationary VARMA process and is compatible with current structural equation methodology. Structural equation modeling programs, such as LISREL, may therefore be…

ARMA-Based SEM When the Number of Time Points T Exceeds the Number of Cases N: Raw Data Maximum Likelihood.

ERIC Educational Resources Information Center

Hamaker, Ellen L.; Dolan, Conor V.; Molenaar, Peter C. M.

2003-01-01

Demonstrated, through simulation, that stationary autoregressive moving average (ARMA) models may be fitted readily when T>N, using normal theory raw maximum likelihood structural equation modeling. Also provides some illustrations based on real data. (SLD)

Three essays on price dynamics and causations among energy markets and macroeconomic information

NASA Astrophysics Data System (ADS)

Hong, Sung Wook

This dissertation examines three important issues in energy markets: price dynamics, information flow, and structural change. We discuss each issue in detail, building empirical time series models, analyzing the results, and interpreting the findings. First, we examine the contemporaneous interdependencies and information flows among crude oil, natural gas, and electricity prices in the United States (US) through the multivariate generalized autoregressive conditional heteroscedasticity (MGARCH) model, Directed Acyclic Graph (DAG) for contemporaneous causal structures and Bernanke factorization for price dynamic processes. Test results show that the DAG from residuals of out-of-sample-forecast is consistent with the DAG from residuals of within-sample-fit. The result supports innovation accounting analysis based on DAGs using residuals of out-of-sample-forecast. Second, we look at the effects of the federal fund rate and/or WTI crude oil price shock on US macroeconomic and financial indicators by using a Factor Augmented Vector Autoregression (FAVAR) model and a graphical model without any deductive assumption. The results show that, in contemporaneous time, the federal fund rate shock is exogenous as the identifying assumption in the Vector Autoregression (VAR) framework of the monetary shock transmission mechanism, whereas the WTI crude oil price return is not exogenous. Third, we examine price dynamics and contemporaneous causality among the price returns of WTI crude oil, gasoline, corn, and the S&P 500. We look for structural break points and then build an econometric model to find the consistent sub-periods having stable parameters in a given VAR framework and to explain recent movements and interdependency among returns. We found strong evidence of two structural breaks and contemporaneous causal relationships among the residuals, but also significant differences between contemporaneous causal structures for each sub-period.

Environmental filtering and land-use history drive patterns in biomass accumulation in a mediterranean-type landscape.

PubMed

Dahlin, Kyla M; Asner, Gregory P; Field, Christopher B

2012-01-01

Aboveground biomass (AGB) reflects multiple and often undetermined ecological and land-use processes, yet detailed landscape-level studies of AGB are uncommon due to the difficulty in making consistent measurements at ecologically relevant scales. Working in a protected mediterranean-type landscape (Jasper Ridge Biological Preserve, California, USA), we combined field measurements with remotely sensed data from the Carnegie Airborne Observatory's light detection and ranging (lidar) system to create a detailed AGB map. We then developed a predictive model using a maximum of 56 explanatory variables derived from geologic and historic-ownership maps, a digital elevation model, and geographic coordinates to evaluate possible controls over currently observed AGB patterns. We tested both ordinary least-squares regression (OLS) and autoregressive approaches. OLS explained 44% of the variation in AGB, and simultaneous autoregression with a 100-m neighborhood improved the fit to an r2 = 0.72, while reducing the number of significant predictor variables from 27 variables in the OLS model to 11 variables in the autoregressive model. We also compared the results from these approaches to a more typical field-derived data set; we randomly sampled 5% of the data 1000 times and used the same OLS approach each time. Environmental filters including incident solar radiation, substrate type, and topographic position were significant predictors of AGB in all models. Past ownership was a minor but significant predictor, despite the long history of conservation at the site. The weak predictive power of these environmental variables, and the significant improvement when spatial autocorrelation was incorporated, highlight the importance of land-use history, disturbance regime, and population dynamics as controllers of AGB.

Reciprocal Associations between Negative Affect, Binge Eating, and Purging in the Natural Environment in Women with Bulimia Nervosa

PubMed Central

Lavender, Jason M.; Utzinger, Linsey M.; Cao, Li; Wonderlich, Stephen A.; Engel, Scott G.; Mitchell, James E.; Crosby, Ross D.

2016-01-01

Although negative affect (NA) has been identified as a common trigger for bulimic behaviors, findings regarding NA following such behaviors have been mixed. This study examined reciprocal associations between NA and bulimic behaviors using real-time, naturalistic data. Participants were 133 women with DSM-IV bulimia nervosa (BN) who completed a two-week ecological momentary assessment (EMA) protocol in which they recorded bulimic behaviors and provided multiple daily ratings of NA. A multilevel autoregressive cross-lagged analysis was conducted to examine concurrent, first-order autoregressive, and prospective associations between NA, binge eating, and purging across the day. Results revealed positive concurrent associations between all variables across all time points, as well as numerous autoregressive associations. For prospective associations, higher NA predicted subsequent bulimic symptoms at multiple time points; conversely, binge eating predicted lower NA at multiple time points, and purging predicted higher NA at one time point. Several autoregressive and prospective associations were also found between binge eating and purging. This study used a novel approach to examine NA in relation to bulimic symptoms, contributing to the existing literature by directly examining the magnitude of the associations, examining differences in the associations across the day, and controlling for other associations in testing each effect in the model. These findings may have relevance for understanding the etiology and/or maintenance of bulimic symptoms, as well as potentially informing psychological interventions for BN. PMID:26692122

Assessing the performance of eight real-time updating models and procedures for the Brosna River

NASA Astrophysics Data System (ADS)

Goswami, M.; O'Connor, K. M.; Bhattarai, K. P.; Shamseldin, A. Y.

2005-10-01

The flow forecasting performance of eight updating models, incorporated in the Galway River Flow Modelling and Forecasting System (GFMFS), was assessed using daily data (rainfall, evaporation and discharge) of the Irish Brosna catchment (1207 km2), considering their one to six days lead-time discharge forecasts. The Perfect Forecast of Input over the Forecast Lead-time scenario was adopted, where required, in place of actual rainfall forecasts. The eight updating models were: (i) the standard linear Auto-Regressive (AR) model, applied to the forecast errors (residuals) of a simulation (non-updating) rainfall-runoff model; (ii) the Neural Network Updating (NNU) model, also using such residuals as input; (iii) the Linear Transfer Function (LTF) model, applied to the simulated and the recently observed discharges; (iv) the Non-linear Auto-Regressive eXogenous-Input Model (NARXM), also a neural network-type structure, but having wide options of using recently observed values of one or more of the three data series, together with non-updated simulated outflows, as inputs; (v) the Parametric Simple Linear Model (PSLM), of LTF-type, using recent rainfall and observed discharge data; (vi) the Parametric Linear perturbation Model (PLPM), also of LTF-type, using recent rainfall and observed discharge data, (vii) n-AR, an AR model applied to the observed discharge series only, as a naïve updating model; and (viii) n-NARXM, a naive form of the NARXM, using only the observed discharge data, excluding exogenous inputs. The five GFMFS simulation (non-updating) models used were the non-parametric and parametric forms of the Simple Linear Model and of the Linear Perturbation Model, the Linearly-Varying Gain Factor Model, the Artificial Neural Network Model, and the conceptual Soil Moisture Accounting and Routing (SMAR) model. As the SMAR model performance was found to be the best among these models, in terms of the Nash-Sutcliffe R2 value, both in calibration and in verification, the simulated outflows of this model only were selected for the subsequent exercise of producing updated discharge forecasts. All the eight forms of updating models for producing lead-time discharge forecasts were found to be capable of producing relatively good lead-1 (1-day ahead) forecasts, with R2 values almost 90% or above. However, for higher lead time forecasts, only three updating models, viz., NARXM, LTF, and NNU, were found to be suitable, with lead-6 values of R2 about 90% or higher. Graphical comparisons were made of the lead-time forecasts for the two largest floods, one in the calibration period and the other in the verification period.

On the relationship between health, education and economic growth: Time series evidence from Malaysia

NASA Astrophysics Data System (ADS)

Khan, Habib Nawaz; Razali, Radzuan B.; Shafei, Afza Bt.

2016-11-01

The objectives of this paper is two-fold: First, to empirically investigate the effects of an enlarged number of healthy and well-educated people on economic growth in Malaysia within the Endogeneous Growth Model framework. Second, to examine the causal links between education, health and economic growth using annual time series data from 1981 to 2014 for Malaysia. Data series were checked for the time series properties by using ADF and KPSS tests. Long run co-integration relationship was investigated with the help of vector autoregressive (VAR) method. For short and long run dynamic relationship investigation vector error correction model (VECM) was applied. Causality analysis was performed through Engle-Granger technique. The study results showed long run co-integration relation and positively significant effects of education and health on economic growth in Malaysia. The reported results also confirmed a feedback hypothesis between the variables in the case of Malaysia. The study results have policy relevance of the importance of human capital (health and education) to the growth process of the Malaysia. Thus, it is suggested that policy makers focus on education and health sectors for sustainable economic growth in Malaysia.

Estimating linear temporal trends from aggregated environmental monitoring data

USGS Publications Warehouse

Erickson, Richard A.; Gray, Brian R.; Eager, Eric A.

2017-01-01

Trend estimates are often used as part of environmental monitoring programs. These trends inform managers (e.g., are desired species increasing or undesired species decreasing?). Data collected from environmental monitoring programs is often aggregated (i.e., averaged), which confounds sampling and process variation. State-space models allow sampling variation and process variations to be separated. We used simulated time-series to compare linear trend estimations from three state-space models, a simple linear regression model, and an auto-regressive model. We also compared the performance of these five models to estimate trends from a long term monitoring program. We specifically estimated trends for two species of fish and four species of aquatic vegetation from the Upper Mississippi River system. We found that the simple linear regression had the best performance of all the given models because it was best able to recover parameters and had consistent numerical convergence. Conversely, the simple linear regression did the worst job estimating populations in a given year. The state-space models did not estimate trends well, but estimated population sizes best when the models converged. We found that a simple linear regression performed better than more complex autoregression and state-space models when used to analyze aggregated environmental monitoring data.

Climate variations and salmonellosis transmission in Adelaide, South Australia: a comparison between regression models

NASA Astrophysics Data System (ADS)

Zhang, Ying; Bi, Peng; Hiller, Janet

2008-01-01

This is the first study to identify appropriate regression models for the association between climate variation and salmonellosis transmission. A comparison between different regression models was conducted using surveillance data in Adelaide, South Australia. By using notified salmonellosis cases and climatic variables from the Adelaide metropolitan area over the period 1990-2003, four regression methods were examined: standard Poisson regression, autoregressive adjusted Poisson regression, multiple linear regression, and a seasonal autoregressive integrated moving average (SARIMA) model. Notified salmonellosis cases in 2004 were used to test the forecasting ability of the four models. Parameter estimation, goodness-of-fit and forecasting ability of the four regression models were compared. Temperatures occurring 2 weeks prior to cases were positively associated with cases of salmonellosis. Rainfall was also inversely related to the number of cases. The comparison of the goodness-of-fit and forecasting ability suggest that the SARIMA model is better than the other three regression models. Temperature and rainfall may be used as climatic predictors of salmonellosis cases in regions with climatic characteristics similar to those of Adelaide. The SARIMA model could, thus, be adopted to quantify the relationship between climate variations and salmonellosis transmission.

Spatio-temporal wildland arson crime functions

Treesearch

David T. Butry; Jeffrey P. Prestemon

2005-01-01

Wildland arson creates damages to structures and timber and affects the health and safety of people living in rural and wildland urban interface areas. We develop a model that incorporates temporal autocorrelations and spatial correlations in wildland arson ignitions in Florida. A Poisson autoregressive model of order p, or PAR(p)...

On the Nature of SEM Estimates of ARMA Parameters.

ERIC Educational Resources Information Center

Hamaker, Ellen L.; Dolan, Conor V.; Molenaar, Peter C. M.

2002-01-01

Reexamined the nature of structural equation modeling (SEM) estimates of autoregressive moving average (ARMA) models, replicated the simulation experiments of P. Molenaar, and examined the behavior of the log-likelihood ratio test. Simulation studies indicate that estimates of ARMA parameters observed with SEM software are identical to those…

The Mathematical Analysis of Style: A Correlation-Based Approach.

ERIC Educational Resources Information Center

Oppenheim, Rosa

1988-01-01

Examines mathematical models of style analysis, focusing on the pattern in which literary characteristics occur. Describes an autoregressive integrated moving average model (ARIMA) for predicting sentence length in different works by the same author and comparable works by different authors. This technique is valuable in characterizing stylistic…

Modelling of cayenne production in Central Java using ARIMA-GARCH

NASA Astrophysics Data System (ADS)

Tarno; Sudarno; Ispriyanti, Dwi; Suparti

2018-05-01

Some regencies/cities in Central Java Province are known as producers of horticultural crops in Indonesia, for example, Brebes which is the largest area of shallot producer in Central Java, while the others, such as Cilacap and Wonosobo are the areas of cayenne commodities production. Currently, cayenne is a strategic commodity and it has broad impact to Indonesian economic development. Modelling the cayenne production is necessary to predict about the commodity to meet the need for society. The needs fulfillment of society will affect stability of the concerned commodity price. Based on the reality, the decreasing of cayenne production will cause the increasing of society’s basic needs price, and finally it will affect the inflation level at that area. This research focused on autoregressive integrated moving average (ARIMA) modelling by considering the effect of autoregressive conditional heteroscedasticity (ARCH) to study about cayenne production in Central Java. The result of empirical study of ARIMA-GARCH modelling for cayenne production in Central Java from January 2003 to November 2015 is ARIMA([1,3],0,0)-GARCH(1,0) as the best model.

A time series model: First-order integer-valued autoregressive (INAR(1))

NASA Astrophysics Data System (ADS)

Simarmata, D. M.; Novkaniza, F.; Widyaningsih, Y.

2017-07-01

Nonnegative integer-valued time series arises in many applications. A time series model: first-order Integer-valued AutoRegressive (INAR(1)) is constructed by binomial thinning operator to model nonnegative integer-valued time series. INAR (1) depends on one period from the process before. The parameter of the model can be estimated by Conditional Least Squares (CLS). Specification of INAR(1) is following the specification of (AR(1)). Forecasting in INAR(1) uses median or Bayesian forecasting methodology. Median forecasting methodology obtains integer s, which is cumulative density function (CDF) until s, is more than or equal to 0.5. Bayesian forecasting methodology forecasts h-step-ahead of generating the parameter of the model and parameter of innovation term using Adaptive Rejection Metropolis Sampling within Gibbs sampling (ARMS), then finding the least integer s, where CDF until s is more than or equal to u . u is a value taken from the Uniform(0,1) distribution. INAR(1) is applied on pneumonia case in Penjaringan, Jakarta Utara, January 2008 until April 2016 monthly.

Male and female development of delinquency during adolescence and early adulthood: a differential autoregressive model of delinquency using an overlapping cohort design.

PubMed

Landsheer, Johannes A; Oud, Johan H L; van Dijkum, Cor

2008-01-01

Although it is well known that during adolescence the delinquent involvement of females is consistently less when compared to male involvement, it remains an important question whether the development of delinquency has a similar trajectory for both sexes. The main hypothesis tested is whether sex differences in delinquency, specifically growth, peak age, and decline, are constant. An autoregression model in continuous time, implemented as a structural equation model, is used for the description of the development of delinquency in males and females. The data are collected in an overlapping cohort design, and both within-person and between-persons data are integrated into a single model. The result shows that the involvement with delinquency over time is different for males and females. The main difference increases up to the age of 16, and decreases thereafter. The model indicates that both sexes reach the maximum in delinquency at the same age. It is concluded that males and females differ both in their start level at age 12 and in the amount of change with age.

Autoregressive-moving-average hidden Markov model for vision-based fall prediction-An application for walker robot.

PubMed

Taghvaei, Sajjad; Jahanandish, Mohammad Hasan; Kosuge, Kazuhiro

2017-01-01

Population aging of the societies requires providing the elderly with safe and dependable assistive technologies in daily life activities. Improving the fall detection algorithms can play a major role in achieving this goal. This article proposes a real-time fall prediction algorithm based on the acquired visual data of a user with walking assistive system from a depth sensor. In the lack of a coupled dynamic model of the human and the assistive walker a hybrid "system identification-machine learning" approach is used. An autoregressive-moving-average (ARMA) model is fitted on the time-series walking data to forecast the upcoming states, and a hidden Markov model (HMM) based classifier is built on the top of the ARMA model to predict falling in the upcoming time frames. The performance of the algorithm is evaluated through experiments with four subjects including an experienced physiotherapist while using a walker robot in five different falling scenarios; namely, fall forward, fall down, fall back, fall left, and fall right. The algorithm successfully predicts the fall with a rate of 84.72%.

Modeling turbidity and flow at daily steps in karst using ARIMA/ARFIMA-GARCH error models

NASA Astrophysics Data System (ADS)

Massei, N.

2013-12-01

Hydrological and physico-chemical variations recorded at karst springs usually reflect highly non-linear processes and the corresponding time series are then very often also highly non-linear. Among others, turbidity, as an important parameter regarding water quality and management, is a very complex response of karst systems to rain events, involving direct transfer of particles from point-source recharge as well as resuspension of particles previously deposited and stored within the system. For those reasons, turbidity modeling has not been well taken in karst hydrological models so far. Most of the time, the modeling approaches would involve stochastic linear models such ARIMA-type models and their derivatives (ARMA, ARMAX, ARIMAX, ARFIMA...). Yet, linear models usually fail to represent well the whole (stochastic) process variability, and their residuals still contain useful information that can be used to either understand the whole variability or to enhance short-term predictability and forecasting. Model residuals are actually not i.i.d., which can be identified by the fact that squared residuals still present clear and significant serial correlation. Indeed, high (low) amplitudes are followed in time by high (low) amplitudes, which can be seen on residuals time series as periods of time during which amplitudes are higher (lower) then the mean amplitude. This is known as the ARCH effet (AutoRegressive Conditional Heteroskedasticity), and the corresponding non-linear process affecting residuals of a linear model can be modeled using ARCH or generalized ARCH (GARCH) non-linear modeling, which approaches are very well known in econometrics. Here we investigated the capability of ARIMA-GARCH error models to represent a ~20-yr daily turbidity time series recorded at a karst spring used for water supply of the city of Le Havre (Upper Normandy, France). ARIMA and ARFIMA models were used to represent the mean behavior of the time series and the residuals clearly appeared to present a pronounced ARCH effect, as confirmed by Ljung-Box and McLeod-Li tests. We then identified and fitted GARCH models to the residuals of ARIMA and ARFIMA models in order to model the conditional variance and volatility of the turbidity time series. The results eventually showed that serial correlation was succesfully removed in the last standardized residuals of the GARCH model, and hence that the ARIMA-GARCH error model appeared consistent for modeling such time series. The approach finally improved short-term (e.g a few steps-ahead) turbidity forecasting.

Predicting Rehabilitation Success Rate Trends among Ethnic Minorities Served by State Vocational Rehabilitation Agencies: A National Time Series Forecast Model Demonstration Study

ERIC Educational Resources Information Center

Moore, Corey L.; Wang, Ningning; Washington, Janique Tynez

2017-01-01

Purpose: This study assessed and demonstrated the efficacy of two select empirical forecast models (i.e., autoregressive integrated moving average [ARIMA] model vs. grey model [GM]) in accurately predicting state vocational rehabilitation agency (SVRA) rehabilitation success rate trends across six different racial and ethnic population cohorts…

On the Trajectories of the Predetermined ALT Model: What Are We Really Modeling?

ERIC Educational Resources Information Center

Jongerling, Joran; Hamaker, Ellen L.

2011-01-01

This article shows that the mean and covariance structure of the predetermined autoregressive latent trajectory (ALT) model are very flexible. As a result, the shape of the modeled growth curve can be quite different from what one might expect at first glance. This is illustrated with several numerical examples that show that, for example, a…

Nonlinear information fusion algorithms for data-efficient multi-fidelity modelling.

PubMed

Perdikaris, P; Raissi, M; Damianou, A; Lawrence, N D; Karniadakis, G E

2017-02-01

Multi-fidelity modelling enables accurate inference of quantities of interest by synergistically combining realizations of low-cost/low-fidelity models with a small set of high-fidelity observations. This is particularly effective when the low- and high-fidelity models exhibit strong correlations, and can lead to significant computational gains over approaches that solely rely on high-fidelity models. However, in many cases of practical interest, low-fidelity models can only be well correlated to their high-fidelity counterparts for a specific range of input parameters, and potentially return wrong trends and erroneous predictions if probed outside of their validity regime. Here we put forth a probabilistic framework based on Gaussian process regression and nonlinear autoregressive schemes that is capable of learning complex nonlinear and space-dependent cross-correlations between models of variable fidelity, and can effectively safeguard against low-fidelity models that provide wrong trends. This introduces a new class of multi-fidelity information fusion algorithms that provide a fundamental extension to the existing linear autoregressive methodologies, while still maintaining the same algorithmic complexity and overall computational cost. The performance of the proposed methods is tested in several benchmark problems involving both synthetic and real multi-fidelity datasets from computational fluid dynamics simulations.

Modeling time-series count data: the unique challenges facing political communication studies.

PubMed

Fogarty, Brian J; Monogan, James E

2014-05-01

This paper demonstrates the importance of proper model specification when analyzing time-series count data in political communication studies. It is common for scholars of media and politics to investigate counts of coverage of an issue as it evolves over time. Many scholars rightly consider the issues of time dependence and dynamic causality to be the most important when crafting a model. However, to ignore the count features of the outcome variable overlooks an important feature of the data. This is particularly the case when modeling data with a low number of counts. In this paper, we argue that the Poisson autoregressive model (Brandt and Williams, 2001) accurately meets the needs of many media studies. We replicate the analyses of Flemming et al. (1997), Peake and Eshbaugh-Soha (2008), and Ura (2009) and demonstrate that models missing some of the assumptions of the Poisson autoregressive model often yield invalid inferences. We also demonstrate that the effect of any of these models can be illustrated dynamically with estimates of uncertainty through a simulation procedure. The paper concludes with implications of these findings for the practical researcher. Copyright © 2013 Elsevier Inc. All rights reserved.

Stochastic Price Models and Optimal Tree Cutting: Results for Loblolly Pine

Treesearch

Robert G. Haight; Thomas P. Holmes

1991-01-01

An empirical investigation of stumpage price models and optimal harvest policies is conducted for loblolly pine plantations in the southeastern United States. The stationarity of monthly and quarterly series of sawtimber prices is analyzed using a unit root test. The statistical evidence supports stationary autoregressive models for the monthly series and for the...

Latent Transition Analysis of Pre-Service Teachers' Efficacy in Mathematics and Science

ERIC Educational Resources Information Center

Ward, Elizabeth Kennedy

2009-01-01

This study modeled changes in pre-service teacher efficacy in mathematics and science over the course of the final year of teacher preparation using latent transition analysis (LTA), a longitudinal form of analysis that builds on two modeling traditions (latent class analysis (LCA) and auto-regressive modeling). Data were collected using the…

Wind power application research on the fusion of the determination and ensemble prediction

NASA Astrophysics Data System (ADS)

Lan, Shi; Lina, Xu; Yuzhu, Hao

2017-07-01

The fused product of wind speed for the wind farm is designed through the use of wind speed products of ensemble prediction from the European Centre for Medium-Range Weather Forecasts (ECMWF) and professional numerical model products on wind power based on Mesoscale Model5 (MM5) and Beijing Rapid Update Cycle (BJ-RUC), which are suitable for short-term wind power forecasting and electric dispatch. The single-valued forecast is formed by calculating the different ensemble statistics of the Bayesian probabilistic forecasting representing the uncertainty of ECMWF ensemble prediction. Using autoregressive integrated moving average (ARIMA) model to improve the time resolution of the single-valued forecast, and based on the Bayesian model averaging (BMA) and the deterministic numerical model prediction, the optimal wind speed forecasting curve and the confidence interval are provided. The result shows that the fusion forecast has made obvious improvement to the accuracy relative to the existing numerical forecasting products. Compared with the 0-24 h existing deterministic forecast in the validation period, the mean absolute error (MAE) is decreased by 24.3 % and the correlation coefficient (R) is increased by 12.5 %. In comparison with the ECMWF ensemble forecast, the MAE is reduced by 11.7 %, and R is increased 14.5 %. Additionally, MAE did not increase with the prolongation of the forecast ahead.

Intra- and Interseasonal Autoregressive Prediction of Dengue Outbreaks Using Local Weather and Regional Climate for a Tropical Environment in Colombia

PubMed Central

Eastin, Matthew D.; Delmelle, Eric; Casas, Irene; Wexler, Joshua; Self, Cameron

2014-01-01

Dengue fever transmission results from complex interactions between the virus, human hosts, and mosquito vectors—all of which are influenced by environmental factors. Predictive models of dengue incidence rate, based on local weather and regional climate parameters, could benefit disease mitigation efforts. Time series of epidemiological and meteorological data for the urban environment of Cali, Colombia are analyzed from January of 2000 to December of 2011. Significant dengue outbreaks generally occur during warm-dry periods with extreme daily temperatures confined between 18°C and 32°C—the optimal range for mosquito survival and viral transmission. Two environment-based, multivariate, autoregressive forecast models are developed that allow dengue outbreaks to be anticipated from 2 weeks to 6 months in advance. These models have the potential to enhance existing dengue early warning systems, ultimately supporting public health decisions on the timing and scale of vector control efforts. PMID:24957546

Spatial pattern of diarrhea based on regional economic and environment by spatial autoregressive model

NASA Astrophysics Data System (ADS)

Bekti, Rokhana Dwi; Nurhadiyanti, Gita; Irwansyah, Edy

2014-10-01

The diarrhea case pattern information, especially for toddler, is very important. It is used to show the distribution of diarrhea in every region, relationship among that locations, and regional economic characteristic or environmental behavior. So, this research uses spatial pattern to perform them. This method includes: Moran's I, Spatial Autoregressive Models (SAR), and Local Indicator of Spatial Autocorrelation (LISA). It uses sample from 23 sub districts of Bekasi Regency, West Java, Indonesia. Diarrhea case, regional economic, and environmental behavior of households have a spatial relationship among sub district. SAR shows that the percentage of Regional Gross Domestic Product is significantly effect on diarrhea at α = 10%. Therefore illiteracy and health center facilities are significant at α = 5%. With LISA test, sub districts in southern Bekasi have high dependencies with Cikarang Selatan, Serang Baru, and Setu. This research also builds development application that is based on java and R to support data analysis.

Mean-variance portfolio optimization by using time series approaches based on logarithmic utility function

NASA Astrophysics Data System (ADS)

Soeryana, E.; Fadhlina, N.; Sukono; Rusyaman, E.; Supian, S.

2017-01-01

Investments in stocks investors are also faced with the issue of risk, due to daily price of stock also fluctuate. For minimize the level of risk, investors usually forming an investment portfolio. Establishment of a portfolio consisting of several stocks are intended to get the optimal composition of the investment portfolio. This paper discussed about optimizing investment portfolio of Mean-Variance to stocks by using mean and volatility is not constant based on logarithmic utility function. Non constant mean analysed using models Autoregressive Moving Average (ARMA), while non constant volatility models are analysed using the Generalized Autoregressive Conditional heteroscedastic (GARCH). Optimization process is performed by using the Lagrangian multiplier technique. As a numerical illustration, the method is used to analyse some Islamic stocks in Indonesia. The expected result is to get the proportion of investment in each Islamic stock analysed.

Mean-Variance portfolio optimization by using non constant mean and volatility based on the negative exponential utility function

NASA Astrophysics Data System (ADS)

Soeryana, Endang; Halim, Nurfadhlina Bt Abdul; Sukono, Rusyaman, Endang; Supian, Sudradjat

2017-03-01

Investments in stocks investors are also faced with the issue of risk, due to daily price of stock also fluctuate. For minimize the level of risk, investors usually forming an investment portfolio. Establishment of a portfolio consisting of several stocks are intended to get the optimal composition of the investment portfolio. This paper discussed about optimizing investment portfolio of Mean-Variance to stocks by using mean and volatility is not constant based on the Negative Exponential Utility Function. Non constant mean analyzed using models Autoregressive Moving Average (ARMA), while non constant volatility models are analyzed using the Generalized Autoregressive Conditional heteroscedastic (GARCH). Optimization process is performed by using the Lagrangian multiplier technique. As a numerical illustration, the method is used to analyze some stocks in Indonesia. The expected result is to get the proportion of investment in each stock analyzed

Marital satisfaction and maternal depressive symptoms among Korean mothers transitioning to parenthood.

PubMed

Choi, Eunsil

2016-06-01

Although many empirical findings support associations between marital satisfaction and depressive symptoms, gaps remain in our understanding of the magnitude and direction of the associations between marital satisfaction and depressive symptoms as well as the associations in a collectivistic culture. The present study examined autoregressive cross-lagged associations between marital satisfaction and maternal depressive symptoms across a 3-year investigation in a sample of Korean mothers transitioning to parenthood. The sample consisted of 2,078 mothers in the Panel Study of Korean Children. The mothers reported marital satisfaction and maternal depressive symptoms annually for 3 years. The results of an autoregressive cross-lagged model revealed bidirectional associations between marital satisfaction and maternal depressive symptoms. The findings provide evidence of an interactional model of depression in a sample of Korean mothers. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

A multimodel approach to interannual and seasonal prediction of Danube discharge anomalies

NASA Astrophysics Data System (ADS)

Rimbu, Norel; Ionita, Monica; Patrut, Simona; Dima, Mihai

2010-05-01

Interannual and seasonal predictability of Danube river discharge is investigated using three model types: 1) time series models 2) linear regression models of discharge with large-scale climate mode indices and 3) models based on stable teleconnections. All models are calibrated using discharge and climatic data for the period 1901-1977 and validated for the period 1978-2008 . Various time series models, like autoregressive (AR), moving average (MA), autoregressive and moving average (ARMA) or singular spectrum analysis and autoregressive moving average (SSA+ARMA) models have been calibrated and their skills evaluated. The best results were obtained using SSA+ARMA models. SSA+ARMA models proved to have the highest forecast skill also for other European rivers (Gamiz-Fortis et al. 2008). Multiple linear regression models using large-scale climatic mode indices as predictors have a higher forecast skill than the time series models. The best predictors for Danube discharge are the North Atlantic Oscillation (NAO) and the East Atlantic/Western Russia patterns during winter and spring. Other patterns, like Polar/Eurasian or Tropical Northern Hemisphere (TNH) are good predictors for summer and autumn discharge. Based on stable teleconnection approach (Ionita et al. 2008) we construct prediction models through a combination of sea surface temperature (SST), temperature (T) and precipitation (PP) from the regions where discharge and SST, T and PP variations are stable correlated. Forecast skills of these models are higher than forecast skills of the time series and multiple regression models. The models calibrated and validated in our study can be used for operational prediction of interannual and seasonal Danube discharge anomalies. References Gamiz-Fortis, S., D. Pozo-Vazquez, R.M. Trigo, and Y. Castro-Diez, Quantifying the predictability of winter river flow in Iberia. Part I: intearannual predictability. J. Climate, 2484-2501, 2008. Gamiz-Fortis, S., D. Pozo-Vazquez, R.M. Trigo, and Y. Castro-Diez, Quantifying the predictability of winter river flow in Iberia. Part II: seasonal predictability. J. Climate, 2503-2518, 2008. Ionita, M., G. Lohmann, and N. Rimbu, Prediction of spring Elbe river discharge based on stable teleconnections with global temperature and precipitation. J. Climate. 6215-6226, 2008.

Gaussian Process Autoregression for Simultaneous Proportional Multi-Modal Prosthetic Control With Natural Hand Kinematics.

PubMed

Xiloyannis, Michele; Gavriel, Constantinos; Thomik, Andreas A C; Faisal, A Aldo

2017-10-01

Matching the dexterity, versatility, and robustness of the human hand is still an unachieved goal in bionics, robotics, and neural engineering. A major limitation for hand prosthetics lies in the challenges of reliably decoding user intention from muscle signals when controlling complex robotic hands. Most of the commercially available prosthetic hands use muscle-related signals to decode a finite number of predefined motions and some offer proportional control of open/close movements of the whole hand. Here, in contrast, we aim to offer users flexible control of individual joints of their artificial hand. We propose a novel framework for decoding neural information that enables a user to independently control 11 joints of the hand in a continuous manner-much like we control our natural hands. Toward this end, we instructed six able-bodied subjects to perform everyday object manipulation tasks combining both dynamic, free movements (e.g., grasping) and isometric force tasks (e.g., squeezing). We recorded the electromyographic and mechanomyographic activities of five extrinsic muscles of the hand in the forearm, while simultaneously monitoring 11 joints of hand and fingers using a sensorized data glove that tracked the joints of the hand. Instead of learning just a direct mapping from current muscle activity to intended hand movement, we formulated a novel autoregressive approach that combines the context of previous hand movements with instantaneous muscle activity to predict future hand movements. Specifically, we evaluated a linear vector autoregressive moving average model with exogenous inputs and a novel Gaussian process ( ) autoregressive framework to learn the continuous mapping from hand joint dynamics and muscle activity to decode intended hand movement. Our approach achieves high levels of performance (RMSE of 8°/s and ). Crucially, we use a small set of sensors that allows us to control a larger set of independently actuated degrees of freedom of a hand. This novel undersensored control is enabled through the combination of nonlinear autoregressive continuous mapping between muscle activity and joint angles. The system evaluates the muscle signals in the context of previous natural hand movements. This enables us to resolve ambiguities in situations, where muscle signals alone cannot determine the correct action as we evaluate the muscle signals in their context of natural hand movements. autoregression is a particularly powerful approach which makes not only a prediction based on the context but also represents the associated uncertainty of its predictions, thus enabling the novel notion of risk-based control in neuroprosthetics. Our results suggest that autoregressive approaches with exogenous inputs lend themselves for natural, intuitive, and continuous control in neurotechnology, with the particular focus on prosthetic restoration of natural limb function, where high dexterity is required for complex movements.

Real-time processing of radar return on a parallel computer

NASA Technical Reports Server (NTRS)

Aalfs, David D.

1992-01-01

NASA is working with the FAA to demonstrate the feasibility of pulse Doppler radar as a candidate airborne sensor to detect low altitude windshears. The need to provide the pilot with timely information about possible hazards has motivated a demand for real-time processing of a radar return. Investigated here is parallel processing as a means of accommodating the high data rates required. A PC based parallel computer, called the transputer, is used to investigate issues in real time concurrent processing of radar signals. A transputer network is made up of an array of single instruction stream processors that can be networked in a variety of ways. They are easily reconfigured and software development is largely independent of the particular network topology. The performance of the transputer is evaluated in light of the computational requirements. A number of algorithms have been implemented on the transputers in OCCAM, a language specially designed for parallel processing. These include signal processing algorithms such as the Fast Fourier Transform (FFT), pulse-pair, and autoregressive modelling, as well as routing software to support concurrency. The most computationally intensive task is estimating the spectrum. Two approaches have been taken on this problem, the first and most conventional of which is to use the FFT. By using table look-ups for the basis function and other optimizing techniques, an algorithm has been developed that is sufficient for real time. The other approach is to model the signal as an autoregressive process and estimate the spectrum based on the model coefficients. This technique is attractive because it does not suffer from the spectral leakage problem inherent in the FFT. Benchmark tests indicate that autoregressive modeling is feasible in real time.

Fuzzy neural network technique for system state forecasting.

PubMed

Li, Dezhi; Wang, Wilson; Ismail, Fathy

2013-10-01

In many system state forecasting applications, the prediction is performed based on multiple datasets, each corresponding to a distinct system condition. The traditional methods dealing with multiple datasets (e.g., vector autoregressive moving average models and neural networks) have some shortcomings, such as limited modeling capability and opaque reasoning operations. To tackle these problems, a novel fuzzy neural network (FNN) is proposed in this paper to effectively extract information from multiple datasets, so as to improve forecasting accuracy. The proposed predictor consists of both autoregressive (AR) nodes modeling and nonlinear nodes modeling; AR models/nodes are used to capture the linear correlation of the datasets, and the nonlinear correlation of the datasets are modeled with nonlinear neuron nodes. A novel particle swarm technique [i.e., Laplace particle swarm (LPS) method] is proposed to facilitate parameters estimation of the predictor and improve modeling accuracy. The effectiveness of the developed FNN predictor and the associated LPS method is verified by a series of tests related to Mackey-Glass data forecast, exchange rate data prediction, and gear system prognosis. Test results show that the developed FNN predictor and the LPS method can capture the dynamics of multiple datasets effectively and track system characteristics accurately.

Reciprocal associations between negative affect, binge eating, and purging in the natural environment in women with bulimia nervosa.

PubMed

Lavender, Jason M; Utzinger, Linsey M; Cao, Li; Wonderlich, Stephen A; Engel, Scott G; Mitchell, James E; Crosby, Ross D

2016-04-01

Although negative affect (NA) has been identified as a common trigger for bulimic behaviors, findings regarding NA following such behaviors have been mixed. This study examined reciprocal associations between NA and bulimic behaviors using real-time, naturalistic data. Participants were 133 women with bulimia nervosa (BN) according to the 4th edition of the Diagnostic and Statistical Manual of Mental Disorders who completed a 2-week ecological momentary assessment protocol in which they recorded bulimic behaviors and provided multiple daily ratings of NA. A multilevel autoregressive cross-lagged analysis was conducted to examine concurrent, first-order autoregressive, and prospective associations between NA, binge eating, and purging across the day. Results revealed positive concurrent associations between all variables across all time points, as well as numerous autoregressive associations. For prospective associations, higher NA predicted subsequent bulimic symptoms at multiple time points; conversely, binge eating predicted lower NA at multiple time points, and purging predicted higher NA at 1 time point. Several autoregressive and prospective associations were also found between binge eating and purging. This study used a novel approach to examine NA in relation to bulimic symptoms, contributing to the existing literature by directly examining the magnitude of the associations, examining differences in the associations across the day, and controlling for other associations in testing each effect in the model. These findings may have relevance for understanding the etiology and/or maintenance of bulimic symptoms, as well as potentially informing psychological interventions for BN. (c) 2016 APA, all rights reserved).

Analyses of global sea surface temperature 1856-1991

NASA Astrophysics Data System (ADS)

Kaplan, Alexey; Cane, Mark A.; Kushnir, Yochanan; Clement, Amy C.; Blumenthal, M. Benno; Rajagopalan, Balaji

1998-08-01

Global analyses of monthly sea surface temperature (SST) anomalies from 1856 to 1991 are produced using three statistically based methods: optimal smoothing (OS), the Kaiman filter (KF) and optimal interpolation (OI). Each of these is accompanied by estimates of the error covariance of the analyzed fields. The spatial covariance function these methods require is estimated from the available data; the timemarching model is a first-order autoregressive model again estimated from data. The data input for the analyses are monthly anomalies from the United Kingdom Meteorological Office historical sea surface temperature data set (MOHSST5) [Parker et al., 1994] of the Global Ocean Surface Temperature Atlas (GOSTA) [Bottomley et al., 1990]. These analyses are compared with each other, with GOSTA, and with an analysis generated by projection (P) onto a set of empirical orthogonal functions (as in Smith et al. [1996]). In theory, the quality of the analyses should rank in the order OS, KF, OI, P, and GOSTA. It is found that the first four give comparable results in the data-rich periods (1951-1991), but at times when data is sparse the first three differ significantly from P and GOSTA. At these times the latter two often have extreme and fluctuating values, prima facie evidence of error. The statistical schemes are also verified against data not used in any of the analyses (proxy records derived from corals and air temperature records from coastal and island stations). We also present evidence that the analysis error estimates are indeed indicative of the quality of the products. At most times the OS and KF products are close to the OI product, but at times of especially poor coverage their use of information from other times is advantageous. The methods appear to reconstruct the major features of the global SST field from very sparse data. Comparison with other indications of the El Niño-Southern Oscillation cycle show that the analyses provide usable information on interannual variability as far back as the 1860s.

Beyond Rating Curves: Time Series Models for in-Stream Turbidity Prediction

NASA Astrophysics Data System (ADS)

Wang, L.; Mukundan, R.; Zion, M.; Pierson, D. C.

2012-12-01

The New York City Department of Environmental Protection (DEP) manages New York City's water supply, which is comprised of over 20 reservoirs and supplies over 1 billion gallons of water per day to more than 9 million customers. DEP's "West of Hudson" reservoirs located in the Catskill Mountains are unfiltered per a renewable filtration avoidance determination granted by the EPA. While water quality is usually pristine, high volume storm events occasionally cause the reservoirs to become highly turbid. A logical strategy for turbidity control is to temporarily remove the turbid reservoirs from service. While effective in limiting delivery of turbid water and reducing the need for in-reservoir alum flocculation, this strategy runs the risk of negatively impacting water supply reliability. Thus, it is advantageous for DEP to understand how long a particular turbidity event will affect their system. In order to understand the duration, intensity and total load of a turbidity event, predictions of future in-stream turbidity values are important. Traditionally, turbidity predictions have been carried out by applying streamflow observations/forecasts to a flow-turbidity rating curve. However, predictions from rating curves are often inaccurate due to inter- and intra-event variability in flow-turbidity relationships. Predictions can be improved by applying an autoregressive moving average (ARMA) time series model in combination with a traditional rating curve. Since 2003, DEP and the Upstate Freshwater Institute have compiled a relatively consistent set of 15-minute turbidity observations at various locations on Esopus Creek above Ashokan Reservoir. Using daily averages of this data and streamflow observations at nearby USGS gauges, flow-turbidity rating curves were developed via linear regression. Time series analysis revealed that the linear regression residuals may be represented using an ARMA(1,2) process. Based on this information, flow-turbidity regressions with ARMA(1,2) errors were fit to the observations. Preliminary model validation exercises at a 30-day forecast horizon show that the ARMA error models generally improve the predictive skill of the linear regression rating curves. Skill seems to vary based on the ambient hydrologic conditions at the onset of the forecast. For example, ARMA error model forecasts issued before a high flow/turbidity event do not show significant improvements over the rating curve approach. However, ARMA error model forecasts issued during the "falling limb" of the hydrograph are significantly more accurate than rating curves for both single day and accumulated event predictions. In order to assist in reservoir operations decisions associated with turbidity events and general water supply reliability, DEP has initiated design of an Operations Support Tool (OST). OST integrates a reservoir operations model with 2D hydrodynamic water quality models and a database compiling near-real-time data sources and hydrologic forecasts. Currently, OST uses conventional flow-turbidity rating curves and hydrologic forecasts for predictive turbidity inputs. Given the improvements in predictive skill over traditional rating curves, the ARMA error models are currently being evaluated as an addition to DEP's Operations Support Tool.

An INAR(1) Negative Multinomial Regression Model for Longitudinal Count Data.

ERIC Educational Resources Information Center

Bockenholt, Ulf

1999-01-01

Discusses a regression model for the analysis of longitudinal count data in a panel study by adapting an integer-valued first-order autoregressive (INAR(1)) Poisson process to represent time-dependent correlation between counts. Derives a new negative multinomial distribution by combining INAR(1) representation with a random effects approach.…

Robust Spatial Autoregressive Modeling for Hardwood Log Inspection

Treesearch

Dongping Zhu; A.A. Beex

1994-01-01

We explore the application of a stochastic texture modeling method toward a machine vision system for log inspection in the forest products industry. This machine vision system uses computerized tomography (CT) imaging to locate and identify internal defects in hardwood logs. The application of CT to such industrial vision problems requires efficient and robust image...

Reconstruction of late Holocene climate based on tree growth and mechanistic hierarchical models

USGS Publications Warehouse

Tipton, John; Hooten, Mevin B.; Pederson, Neil; Tingley, Martin; Bishop, Daniel

2016-01-01

Reconstruction of pre-instrumental, late Holocene climate is important for understanding how climate has changed in the past and how climate might change in the future. Statistical prediction of paleoclimate from tree ring widths is challenging because tree ring widths are a one-dimensional summary of annual growth that represents a multi-dimensional set of climatic and biotic influences. We develop a Bayesian hierarchical framework using a nonlinear, biologically motivated tree ring growth model to jointly reconstruct temperature and precipitation in the Hudson Valley, New York. Using a common growth function to describe the response of a tree to climate, we allow for species-specific parameterizations of the growth response. To enable predictive backcasts, we model the climate variables with a vector autoregressive process on an annual timescale coupled with a multivariate conditional autoregressive process that accounts for temporal correlation and cross-correlation between temperature and precipitation on a monthly scale. Our multi-scale temporal model allows for flexibility in the climate response through time at different temporal scales and predicts reasonable climate scenarios given tree ring width data.

Identification of multivariable nonlinear systems in the presence of colored noises using iterative hierarchical least squares algorithm.

PubMed

Jafari, Masoumeh; Salimifard, Maryam; Dehghani, Maryam

2014-07-01

This paper presents an efficient method for identification of nonlinear Multi-Input Multi-Output (MIMO) systems in the presence of colored noises. The method studies the multivariable nonlinear Hammerstein and Wiener models, in which, the nonlinear memory-less block is approximated based on arbitrary vector-based basis functions. The linear time-invariant (LTI) block is modeled by an autoregressive moving average with exogenous (ARMAX) model which can effectively describe the moving average noises as well as the autoregressive and the exogenous dynamics. According to the multivariable nature of the system, a pseudo-linear-in-the-parameter model is obtained which includes two different kinds of unknown parameters, a vector and a matrix. Therefore, the standard least squares algorithm cannot be applied directly. To overcome this problem, a Hierarchical Least Squares Iterative (HLSI) algorithm is used to simultaneously estimate the vector and the matrix of unknown parameters as well as the noises. The efficiency of the proposed identification approaches are investigated through three nonlinear MIMO case studies. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

A Novel Multilevel-SVD Method to Improve Multistep Ahead Forecasting in Traffic Accidents Domain.

PubMed

Barba, Lida; Rodríguez, Nibaldo

2017-01-01

Here is proposed a novel method for decomposing a nonstationary time series in components of low and high frequency. The method is based on Multilevel Singular Value Decomposition (MSVD) of a Hankel matrix. The decomposition is used to improve the forecasting accuracy of Multiple Input Multiple Output (MIMO) linear and nonlinear models. Three time series coming from traffic accidents domain are used. They represent the number of persons with injuries in traffic accidents of Santiago, Chile. The data were continuously collected by the Chilean Police and were weekly sampled from 2000:1 to 2014:12. The performance of MSVD is compared with the decomposition in components of low and high frequency of a commonly accepted method based on Stationary Wavelet Transform (SWT). SWT in conjunction with the Autoregressive model (SWT + MIMO-AR) and SWT in conjunction with an Autoregressive Neural Network (SWT + MIMO-ANN) were evaluated. The empirical results have shown that the best accuracy was achieved by the forecasting model based on the proposed decomposition method MSVD, in comparison with the forecasting models based on SWT.

A Novel Multilevel-SVD Method to Improve Multistep Ahead Forecasting in Traffic Accidents Domain

PubMed Central

Rodríguez, Nibaldo

2017-01-01

Here is proposed a novel method for decomposing a nonstationary time series in components of low and high frequency. The method is based on Multilevel Singular Value Decomposition (MSVD) of a Hankel matrix. The decomposition is used to improve the forecasting accuracy of Multiple Input Multiple Output (MIMO) linear and nonlinear models. Three time series coming from traffic accidents domain are used. They represent the number of persons with injuries in traffic accidents of Santiago, Chile. The data were continuously collected by the Chilean Police and were weekly sampled from 2000:1 to 2014:12. The performance of MSVD is compared with the decomposition in components of low and high frequency of a commonly accepted method based on Stationary Wavelet Transform (SWT). SWT in conjunction with the Autoregressive model (SWT + MIMO-AR) and SWT in conjunction with an Autoregressive Neural Network (SWT + MIMO-ANN) were evaluated. The empirical results have shown that the best accuracy was achieved by the forecasting model based on the proposed decomposition method MSVD, in comparison with the forecasting models based on SWT. PMID:28261267

Estimation of Value-at-Risk for Energy Commodities via CAViaR Model

NASA Astrophysics Data System (ADS)

Xiliang, Zhao; Xi, Zhu

This paper uses the Conditional Autoregressive Value at Risk model (CAViaR) proposed by Engle and Manganelli (2004) to evaluate the value-at-risk for daily spot prices of Brent crude oil and West Texas Intermediate crude oil covering the period May 21th, 1987 to Novermber 18th, 2008. Then the accuracy of the estimates of CAViaR model, Normal-GARCH, and GED-GARCH was compared. The results show that all the methods do good job for the low confidence level (95%), and GED-GARCH is the best for spot WTI price, Normal-GARCH and Adaptive-CAViaR are the best for spot Brent price. However, for the high confidence level (99%), Normal-GARCH do a good job for spot WTI, GED-GARCH and four kind of CAViaR specifications do well for spot Brent price. Normal-GARCH does badly for spot Brent price. The result seems suggest that CAViaR do well as well as GED-GARCH since CAViaR directly model the quantile autoregression, but it does not outperform GED-GARCH although it does outperform Normal-GARCH.

Hybrid Support Vector Regression and Autoregressive Integrated Moving Average Models Improved by Particle Swarm Optimization for Property Crime Rates Forecasting with Economic Indicators

PubMed Central

Alwee, Razana; Hj Shamsuddin, Siti Mariyam; Sallehuddin, Roselina

2013-01-01

Crimes forecasting is an important area in the field of criminology. Linear models, such as regression and econometric models, are commonly applied in crime forecasting. However, in real crimes data, it is common that the data consists of both linear and nonlinear components. A single model may not be sufficient to identify all the characteristics of the data. The purpose of this study is to introduce a hybrid model that combines support vector regression (SVR) and autoregressive integrated moving average (ARIMA) to be applied in crime rates forecasting. SVR is very robust with small training data and high-dimensional problem. Meanwhile, ARIMA has the ability to model several types of time series. However, the accuracy of the SVR model depends on values of its parameters, while ARIMA is not robust to be applied to small data sets. Therefore, to overcome this problem, particle swarm optimization is used to estimate the parameters of the SVR and ARIMA models. The proposed hybrid model is used to forecast the property crime rates of the United State based on economic indicators. The experimental results show that the proposed hybrid model is able to produce more accurate forecasting results as compared to the individual models. PMID:23766729

A Deep and Autoregressive Approach for Topic Modeling of Multimodal Data.

PubMed

Zheng, Yin; Zhang, Yu-Jin; Larochelle, Hugo

2016-06-01

Topic modeling based on latent Dirichlet allocation (LDA) has been a framework of choice to deal with multimodal data, such as in image annotation tasks. Another popular approach to model the multimodal data is through deep neural networks, such as the deep Boltzmann machine (DBM). Recently, a new type of topic model called the Document Neural Autoregressive Distribution Estimator (DocNADE) was proposed and demonstrated state-of-the-art performance for text document modeling. In this work, we show how to successfully apply and extend this model to multimodal data, such as simultaneous image classification and annotation. First, we propose SupDocNADE, a supervised extension of DocNADE, that increases the discriminative power of the learned hidden topic features and show how to employ it to learn a joint representation from image visual words, annotation words and class label information. We test our model on the LabelMe and UIUC-Sports data sets and show that it compares favorably to other topic models. Second, we propose a deep extension of our model and provide an efficient way of training the deep model. Experimental results show that our deep model outperforms its shallow version and reaches state-of-the-art performance on the Multimedia Information Retrieval (MIR) Flickr data set.

Hybrid support vector regression and autoregressive integrated moving average models improved by particle swarm optimization for property crime rates forecasting with economic indicators.

PubMed

Alwee, Razana; Shamsuddin, Siti Mariyam Hj; Sallehuddin, Roselina

2013-01-01

Crimes forecasting is an important area in the field of criminology. Linear models, such as regression and econometric models, are commonly applied in crime forecasting. However, in real crimes data, it is common that the data consists of both linear and nonlinear components. A single model may not be sufficient to identify all the characteristics of the data. The purpose of this study is to introduce a hybrid model that combines support vector regression (SVR) and autoregressive integrated moving average (ARIMA) to be applied in crime rates forecasting. SVR is very robust with small training data and high-dimensional problem. Meanwhile, ARIMA has the ability to model several types of time series. However, the accuracy of the SVR model depends on values of its parameters, while ARIMA is not robust to be applied to small data sets. Therefore, to overcome this problem, particle swarm optimization is used to estimate the parameters of the SVR and ARIMA models. The proposed hybrid model is used to forecast the property crime rates of the United State based on economic indicators. The experimental results show that the proposed hybrid model is able to produce more accurate forecasting results as compared to the individual models.

Modeling of Engine Parameters for Condition-Based Maintenance of the MTU Series 2000 Diesel Engine

DTIC Science & Technology

2016-09-01

are suitable. To model the behavior of the engine, an autoregressive distributed lag (ARDL) time series model of engine speed and exhaust gas... time series model of engine speed and exhaust gas temperature is derived. The lag length for ARDL is determined by whitening of residuals using the...15 B. REGRESSION ANALYSIS ....................................................................15 1. Time Series Analysis

Non-linear models for the detection of impaired cerebral blood flow autoregulation.

PubMed

Chacón, Max; Jara, José Luis; Miranda, Rodrigo; Katsogridakis, Emmanuel; Panerai, Ronney B

2018-01-01

The ability to discriminate between normal and impaired dynamic cerebral autoregulation (CA), based on measurements of spontaneous fluctuations in arterial blood pressure (BP) and cerebral blood flow (CBF), has considerable clinical relevance. We studied 45 normal subjects at rest and under hypercapnia induced by breathing a mixture of carbon dioxide and air. Non-linear models with BP as input and CBF velocity (CBFV) as output, were implemented with support vector machines (SVM) using separate recordings for learning and validation. Dynamic SVM implementations used either moving average or autoregressive structures. The efficiency of dynamic CA was estimated from the model's derived CBFV response to a step change in BP as an autoregulation index for both linear and non-linear models. Non-linear models with recurrences (autoregressive) showed the best results, with CA indexes of 5.9 ± 1.5 in normocapnia, and 2.5 ± 1.2 for hypercapnia with an area under the receiver-operator curve of 0.955. The high performance achieved by non-linear SVM models to detect deterioration of dynamic CA should encourage further assessment of its applicability to clinical conditions where CA might be impaired.

Forecasting incidence of hemorrhagic fever with renal syndrome in China using ARIMA model

PubMed Central

2011-01-01

Background China is a country that is most seriously affected by hemorrhagic fever with renal syndrome (HFRS) with 90% of HFRS cases reported globally. At present, HFRS is getting worse with increasing cases and natural foci in China. Therefore, there is an urgent need for monitoring and predicting HFRS incidence to make the control of HFRS more effective. In this study, we applied a stochastic autoregressive integrated moving average (ARIMA) model with the objective of monitoring and short-term forecasting HFRS incidence in China. Methods Chinese HFRS data from 1975 to 2008 were used to fit ARIMA model. Akaike Information Criterion (AIC) and Ljung-Box test were used to evaluate the constructed models. Subsequently, the fitted ARIMA model was applied to obtain the fitted HFRS incidence from 1978 to 2008 and contrast with corresponding observed values. To assess the validity of the proposed model, the mean absolute percentage error (MAPE) between the observed and fitted HFRS incidence (1978-2008) was calculated. Finally, the fitted ARIMA model was used to forecast the incidence of HFRS of the years 2009 to 2011. All analyses were performed using SAS9.1 with a significant level of p < 0.05. Results The goodness-of-fit test of the optimum ARIMA (0,3,1) model showed non-significant autocorrelations in the residuals of the model (Ljung-Box Q statistic = 5.95,P = 0.3113). The fitted values made by ARIMA (0,3,1) model for years 1978-2008 closely followed the observed values for the same years, with a mean absolute percentage error (MAPE) of 12.20%. The forecast values from 2009 to 2011 were 0.69, 0.86, and 1.21per 100,000 population, respectively. Conclusion ARIMA models applied to historical HFRS incidence data are an important tool for HFRS surveillance in China. This study shows that accurate forecasting of the HFRS incidence is possible using an ARIMA model. If predicted values from this study are accurate, China can expect a rise in HFRS incidence. PMID:21838933

Condition Monitoring for Helicopter Data. Appendix A

NASA Technical Reports Server (NTRS)

Wen, Fang; Willett, Peter; Deb, Somnath

2000-01-01

In this paper the classical "Westland" set of empirical accelerometer helicopter data is analyzed with the aim of condition monitoring for diagnostic purposes. The goal is to determine features for failure events from these data, via a proprietary signal processing toolbox, and to weigh these according to a variety of classification algorithms. As regards signal processing, it appears that the autoregressive (AR) coefficients from a simple linear model encapsulate a great deal of information in a relatively few measurements; it has also been found that augmentation of these by harmonic and other parameters can improve classification significantly. As regards classification, several techniques have been explored, among these restricted Coulomb energy (RCE) networks, learning vector quantization (LVQ), Gaussian mixture classifiers and decision trees. A problem with these approaches, and in common with many classification paradigms, is that augmentation of the feature dimension can degrade classification ability. Thus, we also introduce the Bayesian data reduction algorithm (BDRA), which imposes a Dirichlet prior on training data and is thus able to quantify probability of error in an exact manner, such that features may be discarded or coarsened appropriately.

Deterministic decomposition and seasonal ARIMA time series models applied to airport noise forecasting

NASA Astrophysics Data System (ADS)

Guarnaccia, Claudio; Quartieri, Joseph; Tepedino, Carmine

2017-06-01

One of the most hazardous physical polluting agents, considering their effects on human health, is acoustical noise. Airports are a strong source of acoustical noise, due to the airplanes turbines, to the aero-dynamical noise of transits, to the acceleration or the breaking during the take-off and landing phases of aircrafts, to the road traffic around the airport, etc.. The monitoring and the prediction of the acoustical level emitted by airports can be very useful to assess the impact on human health and activities. In the airports noise scenario, thanks to flights scheduling, the predominant sources may have a periodic behaviour. Thus, a Time Series Analysis approach can be adopted, considering that a general trend and a seasonal behaviour can be highlighted and used to build a predictive model. In this paper, two different approaches are adopted, thus two predictive models are constructed and tested. The first model is based on deterministic decomposition and is built composing the trend, that is the long term behaviour, the seasonality, that is the periodic component, and the random variations. The second model is based on seasonal autoregressive moving average, and it belongs to the stochastic class of models. The two different models are fitted on an acoustical level dataset collected close to the Nice (France) international airport. Results will be encouraging and will show good prediction performances of both the adopted strategies. A residual analysis is performed, in order to quantify the forecasting error features.

Challenges of Electronic Medical Surveillance Systems

DTIC Science & Technology

2004-06-01

More sophisticated approaches, such as regression models and classical autoregressive moving average ( ARIMA ) models that make estimates based on...with those predicted by a mathematical model . The primary benefit of ARIMA models is their ability to correct for local trends in the data so that...works well, for example, during a particularly severe flu season, where prolonged periods of high visit rates are adjusted to by the ARIMA model , thus

Time Series Modelling of Syphilis Incidence in China from 2005 to 2012

PubMed Central

Zhang, Xingyu; Zhang, Tao; Pei, Jiao; Liu, Yuanyuan; Li, Xiaosong; Medrano-Gracia, Pau

2016-01-01

Background The infection rate of syphilis in China has increased dramatically in recent decades, becoming a serious public health concern. Early prediction of syphilis is therefore of great importance for heath planning and management. Methods In this paper, we analyzed surveillance time series data for primary, secondary, tertiary, congenital and latent syphilis in mainland China from 2005 to 2012. Seasonality and long-term trend were explored with decomposition methods. Autoregressive integrated moving average (ARIMA) was used to fit a univariate time series model of syphilis incidence. A separate multi-variable time series for each syphilis type was also tested using an autoregressive integrated moving average model with exogenous variables (ARIMAX). Results The syphilis incidence rates have increased three-fold from 2005 to 2012. All syphilis time series showed strong seasonality and increasing long-term trend. Both ARIMA and ARIMAX models fitted and estimated syphilis incidence well. All univariate time series showed highest goodness-of-fit results with the ARIMA(0,0,1)×(0,1,1) model. Conclusion Time series analysis was an effective tool for modelling the historical and future incidence of syphilis in China. The ARIMAX model showed superior performance than the ARIMA model for the modelling of syphilis incidence. Time series correlations existed between the models for primary, secondary, tertiary, congenital and latent syphilis. PMID:26901682

Time Series Modelling of Syphilis Incidence in China from 2005 to 2012.

PubMed

Zhang, Xingyu; Zhang, Tao; Pei, Jiao; Liu, Yuanyuan; Li, Xiaosong; Medrano-Gracia, Pau

2016-01-01

The infection rate of syphilis in China has increased dramatically in recent decades, becoming a serious public health concern. Early prediction of syphilis is therefore of great importance for heath planning and management. In this paper, we analyzed surveillance time series data for primary, secondary, tertiary, congenital and latent syphilis in mainland China from 2005 to 2012. Seasonality and long-term trend were explored with decomposition methods. Autoregressive integrated moving average (ARIMA) was used to fit a univariate time series model of syphilis incidence. A separate multi-variable time series for each syphilis type was also tested using an autoregressive integrated moving average model with exogenous variables (ARIMAX). The syphilis incidence rates have increased three-fold from 2005 to 2012. All syphilis time series showed strong seasonality and increasing long-term trend. Both ARIMA and ARIMAX models fitted and estimated syphilis incidence well. All univariate time series showed highest goodness-of-fit results with the ARIMA(0,0,1)×(0,1,1) model. Time series analysis was an effective tool for modelling the historical and future incidence of syphilis in China. The ARIMAX model showed superior performance than the ARIMA model for the modelling of syphilis incidence. Time series correlations existed between the models for primary, secondary, tertiary, congenital and latent syphilis.

Application and evaluation of forecasting methods for municipal solid waste generation in an Eastern-European city.

PubMed

Rimaityte, Ingrida; Ruzgas, Tomas; Denafas, Gintaras; Racys, Viktoras; Martuzevicius, Dainius

2012-01-01

Forecasting of generation of municipal solid waste (MSW) in developing countries is often a challenging task due to the lack of data and selection of suitable forecasting method. This article aimed to select and evaluate several methods for MSW forecasting in a medium-scaled Eastern European city (Kaunas, Lithuania) with rapidly developing economics, with respect to affluence-related and seasonal impacts. The MSW generation was forecast with respect to the economic activity of the city (regression modelling) and using time series analysis. The modelling based on social-economic indicators (regression implemented in LCA-IWM model) showed particular sensitivity (deviation from actual data in the range from 2.2 to 20.6%) to external factors, such as the synergetic effects of affluence parameters or changes in MSW collection system. For the time series analysis, the combination of autoregressive integrated moving average (ARIMA) and seasonal exponential smoothing (SES) techniques were found to be the most accurate (mean absolute percentage error equalled to 6.5). Time series analysis method was very valuable for forecasting the weekly variation of waste generation data (r (2) > 0.87), but the forecast yearly increase should be verified against the data obtained by regression modelling. The methods and findings of this study may assist the experts, decision-makers and scientists performing forecasts of MSW generation, especially in developing countries.

Forecasting Hourly Water Demands With Seasonal Autoregressive Models for Real-Time Application

NASA Astrophysics Data System (ADS)

Chen, Jinduan; Boccelli, Dominic L.

2018-02-01

Consumer water demands are not typically measured at temporal or spatial scales adequate to support real-time decision making, and recent approaches for estimating unobserved demands using observed hydraulic measurements are generally not capable of forecasting demands and uncertainty information. While time series modeling has shown promise for representing total system demands, these models have generally not been evaluated at spatial scales appropriate for representative real-time modeling. This study investigates the use of a double-seasonal time series model to capture daily and weekly autocorrelations to both total system demands and regional aggregated demands at a scale that would capture demand variability across a distribution system. Emphasis was placed on the ability to forecast demands and quantify uncertainties with results compared to traditional time series pattern-based demand models as well as nonseasonal and single-seasonal time series models. Additional research included the implementation of an adaptive-parameter estimation scheme to update the time series model when unobserved changes occurred in the system. For two case studies, results showed that (1) for the smaller-scale aggregated water demands, the log-transformed time series model resulted in improved forecasts, (2) the double-seasonal model outperformed other models in terms of forecasting errors, and (3) the adaptive adjustment of parameters during forecasting improved the accuracy of the generated prediction intervals. These results illustrate the capabilities of time series modeling to forecast both water demands and uncertainty estimates at spatial scales commensurate for real-time modeling applications and provide a foundation for developing a real-time integrated demand-hydraulic model.

Multi-Step Time Series Forecasting with an Ensemble of Varied Length Mixture Models.

PubMed

Ouyang, Yicun; Yin, Hujun

2018-05-01

Many real-world problems require modeling and forecasting of time series, such as weather temperature, electricity demand, stock prices and foreign exchange (FX) rates. Often, the tasks involve predicting over a long-term period, e.g. several weeks or months. Most existing time series models are inheritably for one-step prediction, that is, predicting one time point ahead. Multi-step or long-term prediction is difficult and challenging due to the lack of information and uncertainty or error accumulation. The main existing approaches, iterative and independent, either use one-step model recursively or treat the multi-step task as an independent model. They generally perform poorly in practical applications. In this paper, as an extension of the self-organizing mixture autoregressive (AR) model, the varied length mixture (VLM) models are proposed to model and forecast time series over multi-steps. The key idea is to preserve the dependencies between the time points within the prediction horizon. Training data are segmented to various lengths corresponding to various forecasting horizons, and the VLM models are trained in a self-organizing fashion on these segments to capture these dependencies in its component AR models of various predicting horizons. The VLM models form a probabilistic mixture of these varied length models. A combination of short and long VLM models and an ensemble of them are proposed to further enhance the prediction performance. The effectiveness of the proposed methods and their marked improvements over the existing methods are demonstrated through a number of experiments on synthetic data, real-world FX rates and weather temperatures.

Modeling Nonstationary Emotion Dynamics in Dyads using a Time-Varying Vector-Autoregressive Model.

PubMed

Bringmann, Laura F; Ferrer, Emilio; Hamaker, Ellen L; Borsboom, Denny; Tuerlinckx, Francis

2018-01-01

Emotion dynamics are likely to arise in an interpersonal context. Standard methods to study emotions in interpersonal interaction are limited because stationarity is assumed. This means that the dynamics, for example, time-lagged relations, are invariant across time periods. However, this is generally an unrealistic assumption. Whether caused by an external (e.g., divorce) or an internal (e.g., rumination) event, emotion dynamics are prone to change. The semi-parametric time-varying vector-autoregressive (TV-VAR) model is based on well-studied generalized additive models, implemented in the software R. The TV-VAR can explicitly model changes in temporal dependency without pre-existing knowledge about the nature of change. A simulation study is presented, showing that the TV-VAR model is superior to the standard time-invariant VAR model when the dynamics change over time. The TV-VAR model is applied to empirical data on daily feelings of positive affect (PA) from a single couple. Our analyses indicate reliable changes in the male's emotion dynamics over time, but not in the female's-which were not predicted by her own affect or that of her partner. This application illustrates the usefulness of using a TV-VAR model to detect changes in the dynamics in a system.

Non-linear auto-regressive models for cross-frequency coupling in neural time series

PubMed Central

Tallot, Lucille; Grabot, Laetitia; Doyère, Valérie; Grenier, Yves; Gramfort, Alexandre

2017-01-01

We address the issue of reliably detecting and quantifying cross-frequency coupling (CFC) in neural time series. Based on non-linear auto-regressive models, the proposed method provides a generative and parametric model of the time-varying spectral content of the signals. As this method models the entire spectrum simultaneously, it avoids the pitfalls related to incorrect filtering or the use of the Hilbert transform on wide-band signals. As the model is probabilistic, it also provides a score of the model “goodness of fit” via the likelihood, enabling easy and legitimate model selection and parameter comparison; this data-driven feature is unique to our model-based approach. Using three datasets obtained with invasive neurophysiological recordings in humans and rodents, we demonstrate that these models are able to replicate previous results obtained with other metrics, but also reveal new insights such as the influence of the amplitude of the slow oscillation. Using simulations, we demonstrate that our parametric method can reveal neural couplings with shorter signals than non-parametric methods. We also show how the likelihood can be used to find optimal filtering parameters, suggesting new properties on the spectrum of the driving signal, but also to estimate the optimal delay between the coupled signals, enabling a directionality estimation in the coupling. PMID:29227989

Mutual information estimation for irregularly sampled time series

NASA Astrophysics Data System (ADS)

Rehfeld, K.; Marwan, N.; Heitzig, J.; Kurths, J.

2012-04-01

For the automated, objective and joint analysis of time series, similarity measures are crucial. Used in the analysis of climate records, they allow for a complimentary, unbiased view onto sparse datasets. The irregular sampling of many of these time series, however, makes it necessary to either perform signal reconstruction (e.g. interpolation) or to develop and use adapted measures. Standard linear interpolation comes with an inevitable loss of information and bias effects. We have recently developed a Gaussian kernel-based correlation algorithm with which the interpolation error can be substantially lowered, but this would not work should the functional relationship in a bivariate setting be non-linear. We therefore propose an algorithm to estimate lagged auto and cross mutual information from irregularly sampled time series. We have extended the standard and adaptive binning histogram estimators and use Gaussian distributed weights in the estimation of the (joint) probabilities. To test our method we have simulated linear and nonlinear auto-regressive processes with Gamma-distributed inter-sampling intervals. We have then performed a sensitivity analysis for the estimation of actual coupling length, the lag of coupling and the decorrelation time in the synthetic time series and contrast our results to the performance of a signal reconstruction scheme. Finally we applied our estimator to speleothem records. We compare the estimated memory (or decorrelation time) to that from a least-squares estimator based on fitting an auto-regressive process of order 1. The calculated (cross) mutual information results are compared for the different estimators (standard or adaptive binning) and contrasted with results from signal reconstruction. We find that the kernel-based estimator has a significantly lower root mean square error and less systematic sampling bias than the interpolation-based method. It is possible that these encouraging results could be further improved by using non-histogram mutual information estimators, like k-Nearest Neighbor or Kernel-Density estimators, but for short (<1000 points) and irregularly sampled datasets the proposed algorithm is already a great improvement.

Bayesian inference of uncertainties in precipitation-streamflow modeling in a snow affected catchment

NASA Astrophysics Data System (ADS)

Koskela, J. J.; Croke, B. W. F.; Koivusalo, H.; Jakeman, A. J.; Kokkonen, T.

2012-11-01

Bayesian inference is used to study the effect of precipitation and model structural uncertainty on estimates of model parameters and confidence limits of predictive variables in a conceptual rainfall-runoff model in the snow-fed Rudbäck catchment (142 ha) in southern Finland. The IHACRES model is coupled with a simple degree day model to account for snow accumulation and melt. The posterior probability distribution of the model parameters is sampled by using the Differential Evolution Adaptive Metropolis (DREAM(ZS)) algorithm and the generalized likelihood function. Precipitation uncertainty is taken into account by introducing additional latent variables that were used as multipliers for individual storm events. Results suggest that occasional snow water equivalent (SWE) observations together with daily streamflow observations do not contain enough information to simultaneously identify model parameters, precipitation uncertainty and model structural uncertainty in the Rudbäck catchment. The addition of an autoregressive component to account for model structure error and latent variables having uniform priors to account for input uncertainty lead to dubious posterior distributions of model parameters. Thus our hypothesis that informative priors for latent variables could be replaced by additional SWE data could not be confirmed. The model was found to work adequately in 1-day-ahead simulation mode, but the results were poor in the simulation batch mode. This was caused by the interaction of parameters that were used to describe different sources of uncertainty. The findings may have lessons for other cases where parameterizations are similarly high in relation to available prior information.

Multi-model data fusion to improve an early warning system for hypo-/hyperglycemic events.

PubMed

Botwey, Ransford Henry; Daskalaki, Elena; Diem, Peter; Mougiakakou, Stavroula G

2014-01-01

Correct predictions of future blood glucose levels in individuals with Type 1 Diabetes (T1D) can be used to provide early warning of upcoming hypo-/hyperglycemic events and thus to improve the patient's safety. To increase prediction accuracy and efficiency, various approaches have been proposed which combine multiple predictors to produce superior results compared to single predictors. Three methods for model fusion are presented and comparatively assessed. Data from 23 T1D subjects under sensor-augmented pump (SAP) therapy were used in two adaptive data-driven models (an autoregressive model with output correction - cARX, and a recurrent neural network - RNN). Data fusion techniques based on i) Dempster-Shafer Evidential Theory (DST), ii) Genetic Algorithms (GA), and iii) Genetic Programming (GP) were used to merge the complimentary performances of the prediction models. The fused output is used in a warning algorithm to issue alarms of upcoming hypo-/hyperglycemic events. The fusion schemes showed improved performance with lower root mean square errors, lower time lags, and higher correlation. In the warning algorithm, median daily false alarms (DFA) of 0.25%, and 100% correct alarms (CA) were obtained for both event types. The detection times (DT) before occurrence of events were 13.0 and 12.1 min respectively for hypo-/hyperglycemic events. Compared to the cARX and RNN models, and a linear fusion of the two, the proposed fusion schemes represents a significant improvement.

Impact of meteorological factors on the incidence of bacillary dysentery in Beijing, China: A time series analysis (1970-2012).

PubMed

Yan, Long; Wang, Hong; Zhang, Xuan; Li, Ming-Yue; He, Juan

2017-01-01

Influence of meteorological variables on the transmission of bacillary dysentery (BD) is under investigated topic and effective forecasting models as public health tool are lacking. This paper aimed to quantify the relationship between meteorological variables and BD cases in Beijing and to establish an effective forecasting model. A time series analysis was conducted in the Beijing area based upon monthly data on weather variables (i.e. temperature, rainfall, relative humidity, vapor pressure, and wind speed) and on the number of BD cases during the period 1970-2012. Autoregressive integrated moving average models with explanatory variables (ARIMAX) were built based on the data from 1970 to 2004. Prediction of monthly BD cases from 2005 to 2012 was made using the established models. The prediction accuracy was evaluated by the mean square error (MSE). Firstly, temperature with 2-month and 7-month lags and rainfall with 12-month lag were found positively correlated with the number of BD cases in Beijing. Secondly, ARIMAX model with covariates of temperature with 7-month lag (β = 0.021, 95% confidence interval(CI): 0.004-0.038) and rainfall with 12-month lag (β = 0.023, 95% CI: 0.009-0.037) displayed the highest prediction accuracy. The ARIMAX model developed in this study showed an accurate goodness of fit and precise prediction accuracy in the short term, which would be beneficial for government departments to take early public health measures to prevent and control possible BD popularity.

Granger causality revisited

PubMed Central

Friston, Karl J.; Bastos, André M.; Oswal, Ashwini; van Wijk, Bernadette; Richter, Craig; Litvak, Vladimir

2014-01-01

This technical paper offers a critical re-evaluation of (spectral) Granger causality measures in the analysis of biological timeseries. Using realistic (neural mass) models of coupled neuronal dynamics, we evaluate the robustness of parametric and nonparametric Granger causality. Starting from a broad class of generative (state-space) models of neuronal dynamics, we show how their Volterra kernels prescribe the second-order statistics of their response to random fluctuations; characterised in terms of cross-spectral density, cross-covariance, autoregressive coefficients and directed transfer functions. These quantities in turn specify Granger causality — providing a direct (analytic) link between the parameters of a generative model and the expected Granger causality. We use this link to show that Granger causality measures based upon autoregressive models can become unreliable when the underlying dynamics is dominated by slow (unstable) modes — as quantified by the principal Lyapunov exponent. However, nonparametric measures based on causal spectral factors are robust to dynamical instability. We then demonstrate how both parametric and nonparametric spectral causality measures can become unreliable in the presence of measurement noise. Finally, we show that this problem can be finessed by deriving spectral causality measures from Volterra kernels, estimated using dynamic causal modelling. PMID:25003817

Non-linear models for the detection of impaired cerebral blood flow autoregulation

PubMed Central

Miranda, Rodrigo; Katsogridakis, Emmanuel

2018-01-01

The ability to discriminate between normal and impaired dynamic cerebral autoregulation (CA), based on measurements of spontaneous fluctuations in arterial blood pressure (BP) and cerebral blood flow (CBF), has considerable clinical relevance. We studied 45 normal subjects at rest and under hypercapnia induced by breathing a mixture of carbon dioxide and air. Non-linear models with BP as input and CBF velocity (CBFV) as output, were implemented with support vector machines (SVM) using separate recordings for learning and validation. Dynamic SVM implementations used either moving average or autoregressive structures. The efficiency of dynamic CA was estimated from the model’s derived CBFV response to a step change in BP as an autoregulation index for both linear and non-linear models. Non-linear models with recurrences (autoregressive) showed the best results, with CA indexes of 5.9 ± 1.5 in normocapnia, and 2.5 ± 1.2 for hypercapnia with an area under the receiver-operator curve of 0.955. The high performance achieved by non-linear SVM models to detect deterioration of dynamic CA should encourage further assessment of its applicability to clinical conditions where CA might be impaired. PMID:29381724

Assessment and prediction of air quality using fuzzy logic and autoregressive models

NASA Astrophysics Data System (ADS)

Carbajal-Hernández, José Juan; Sánchez-Fernández, Luis P.; Carrasco-Ochoa, Jesús A.; Martínez-Trinidad, José Fco.

2012-12-01

In recent years, artificial intelligence methods have been used for the treatment of environmental problems. This work, presents two models for assessment and prediction of air quality. First, we develop a new computational model for air quality assessment in order to evaluate toxic compounds that can harm sensitive people in urban areas, affecting their normal activities. In this model we propose to use a Sigma operator to statistically asses air quality parameters using their historical data information and determining their negative impact in air quality based on toxicity limits, frequency average and deviations of toxicological tests. We also introduce a fuzzy inference system to perform parameter classification using a reasoning process and integrating them in an air quality index describing the pollution levels in five stages: excellent, good, regular, bad and danger, respectively. The second model proposed in this work predicts air quality concentrations using an autoregressive model, providing a predicted air quality index based on the fuzzy inference system previously developed. Using data from Mexico City Atmospheric Monitoring System, we perform a comparison among air quality indices developed for environmental agencies and similar models. Our results show that our models are an appropriate tool for assessing site pollution and for providing guidance to improve contingency actions in urban areas.

GSTARI model of BPR assets in West Java, Central Java, and East Java

NASA Astrophysics Data System (ADS)

Susanti, Susi; Sulistijowati Handajani, Sri; Indriati, Diari

2018-05-01

Bank Perkreditan Rakyat (BPR) is a financial institution in Indonesia dealing with Micro, Small, and Medium Enterprises (MSMEs). Though limited to MSMEs, the development of the BPR industry continues to increase. West Java, Central Java, and East Java have high BPR asset development are suspected to be interconnected because of their economic activities as a neighboring provincies. BPR assets are nonstationary time series data that follow the uptrend pattern. Therefore, the suitable model with the data is generalized space time autoregressive integrated (GSTARI) which considers the spatial and time interrelationships. GSTARI model used spatial order 1 and the autoregressive order is obtained of optimal lag which has the smallest value of Akaike information criterion corrected. The correlation test results showed that the location used in this study had a close relationship. Based on the results of model identification, the best model obtained is GSTAR(31)-I(1). The parameter estimation used the ordinary least squares with the selection of significant variables used the stepwise method and the normalization cross correlation weighting. The residual model fulfilled the assumption of white noise and normal multivariate, so the model was appropriate. The average RMSE and MAPE values of the model were 498.75 and 2.48%.

Identification of the prediction model for dengue incidence in Can Tho city, a Mekong Delta area in Vietnam.

PubMed

Phung, Dung; Huang, Cunrui; Rutherford, Shannon; Chu, Cordia; Wang, Xiaoming; Nguyen, Minh; Nguyen, Nga Huy; Manh, Cuong Do

2015-01-01

The Mekong Delta is highly vulnerable to climate change and a dengue endemic area in Vietnam. This study aims to examine the association between climate factors and dengue incidence and to identify the best climate prediction model for dengue incidence in Can Tho city, the Mekong Delta area in Vietnam. We used three different regression models comprising: standard multiple regression model (SMR), seasonal autoregressive integrated moving average model (SARIMA), and Poisson distributed lag model (PDLM) to examine the association between climate factors and dengue incidence over the period 2003-2010. We validated the models by forecasting dengue cases for the period of January-December, 2011 using the mean absolute percentage error (MAPE). Receiver operating characteristics curves were used to analyze the sensitivity of the forecast of a dengue outbreak. The results indicate that temperature and relative humidity are significantly associated with changes in dengue incidence consistently across the model methods used, but not cumulative rainfall. The Poisson distributed lag model (PDLM) performs the best prediction of dengue incidence for a 6, 9, and 12-month period and diagnosis of an outbreak however the SARIMA model performs a better prediction of dengue incidence for a 3-month period. The simple or standard multiple regression performed highly imprecise prediction of dengue incidence. We recommend a follow-up study to validate the model on a larger scale in the Mekong Delta region and to analyze the possibility of incorporating a climate-based dengue early warning method into the national dengue surveillance system. Copyright © 2014 Elsevier B.V. All rights reserved.

Comparison of bias-corrected covariance estimators for MMRM analysis in longitudinal data with dropouts.

PubMed

Gosho, Masahiko; Hirakawa, Akihiro; Noma, Hisashi; Maruo, Kazushi; Sato, Yasunori

2017-10-01

In longitudinal clinical trials, some subjects will drop out before completing the trial, so their measurements towards the end of the trial are not obtained. Mixed-effects models for repeated measures (MMRM) analysis with "unstructured" (UN) covariance structure are increasingly common as a primary analysis for group comparisons in these trials. Furthermore, model-based covariance estimators have been routinely used for testing the group difference and estimating confidence intervals of the difference in the MMRM analysis using the UN covariance. However, using the MMRM analysis with the UN covariance could lead to convergence problems for numerical optimization, especially in trials with a small-sample size. Although the so-called sandwich covariance estimator is robust to misspecification of the covariance structure, its performance deteriorates in settings with small-sample size. We investigated the performance of the sandwich covariance estimator and covariance estimators adjusted for small-sample bias proposed by Kauermann and Carroll ( J Am Stat Assoc 2001; 96: 1387-1396) and Mancl and DeRouen ( Biometrics 2001; 57: 126-134) fitting simpler covariance structures through a simulation study. In terms of the type 1 error rate and coverage probability of confidence intervals, Mancl and DeRouen's covariance estimator with compound symmetry, first-order autoregressive (AR(1)), heterogeneous AR(1), and antedependence structures performed better than the original sandwich estimator and Kauermann and Carroll's estimator with these structures in the scenarios where the variance increased across visits. The performance based on Mancl and DeRouen's estimator with these structures was nearly equivalent to that based on the Kenward-Roger method for adjusting the standard errors and degrees of freedom with the UN structure. The model-based covariance estimator with the UN structure under unadjustment of the degrees of freedom, which is frequently used in applications, resulted in substantial inflation of the type 1 error rate. We recommend the use of Mancl and DeRouen's estimator in MMRM analysis if the number of subjects completing is ( n + 5) or less, where n is the number of planned visits. Otherwise, the use of Kenward and Roger's method with UN structure should be the best way.

Forecasting intentional wildfires using temporal and spatiotemporal autocorrelations

Treesearch

Jeffrey P. Prestemon; María L. Chas-Amil; Julia M. Touza; Scott L. Goodrick

2012-01-01

We report daily time series models containing both temporal and spatiotemporal lags, which are applied to forecasting intentional wildfires in Galicia, Spain. Models are estimated independently for each of the 19 forest districts in Galicia using a 1999–2003 training dataset and evaluated out-of-sample with a 2004–06 dataset. Poisson autoregressive models of order P –...

Spatial and temporal changes in the structure of groundwater nitrate concentration time series (1935 1999) as demonstrated by autoregressive modelling

NASA Astrophysics Data System (ADS)

Jones, A. L.; Smart, P. L.

2005-08-01

Autoregressive modelling is used to investigate the internal structure of long-term (1935-1999) records of nitrate concentration for five karst springs in the Mendip Hills. There is a significant short term (1-2 months) positive autocorrelation at three of the five springs due to the availability of sufficient nitrate within the soil store to maintain concentrations in winter recharge for several months. The absence of short term (1-2 months) positive autocorrelation in the other two springs is due to the marked contrast in land use between the limestone and swallet parts of the catchment, rapid concentrated recharge from the latter causing short term switching in the dominant water source at the spring and thus fluctuating nitrate concentrations. Significant negative autocorrelation is evident at lags varying from 4 to 7 months through to 14-22 months for individual springs, with positive autocorrelation at 19-20 months at one site. This variable timing is explained by moderation of the exhaustion effect in the soil by groundwater storage, which gives longer residence times in large catchments and those with a dominance of diffuse flow. The lags derived from autoregressive modelling may therefore provide an indication of average groundwater residence times. Significant differences in the structure of the autocorrelation function for successive 10-year periods are evident at Cheddar Spring, and are explained by the effect the ploughing up of grasslands during the Second World War and increased fertiliser usage on available nitrogen in the soil store. This effect is moderated by the influence of summer temperatures on rates of mineralization, and of both summer and winter rainfall on the timing and magnitude of nitrate leaching. The pattern of nitrate leaching also appears to have been perturbed by the 1976 drought.

Autoregressive Processes in Homogenization of GNSS Tropospheric Data

NASA Astrophysics Data System (ADS)

Klos, A.; Bogusz, J.; Teferle, F. N.; Bock, O.; Pottiaux, E.; Van Malderen, R.

2016-12-01

Offsets due to changes in hardware equipment or any other artificial event are all a subject of a task of homogenization of tropospheric data estimated within a processing of Global Navigation Satellite System (GNSS) observables. This task is aimed at identifying exact epochs of offsets and estimate their magnitudes since they may artificially under- or over-estimate trend and its uncertainty delivered from tropospheric data and used in climate studies. In this research, we analysed a common data set of differences of Integrated Water Vapour (IWV) from GPS and ERA-Interim (1995-2010) provided for a homogenization group working within ES1206 COST Action GNSS4SWEC. We analysed daily IWV records of GPS and ERA-Interim in terms of trend, seasonal terms and noise model with Maximum Likelihood Estimation in Hector software. We found that this data has a character of autoregressive process (AR). Basing on this analysis, we performed Monte Carlo simulations of 25 years long data with two different noise types: white as well as combination of white and autoregressive and also added few strictly defined offsets. This synthetic data set of exactly the same character as IWV from GPS and ERA-Interim was then subjected to a task of manual and automatic/statistical homogenization. We made blind tests and detected possible epochs of offsets manually. We found that simulated offsets were easily detected in series with white noise, no influence of seasonal signal was noticed. The autoregressive series were much more problematic when offsets had to be determined. We found few epochs, for which no offset was simulated. This was mainly due to strong autocorrelation of data, which brings an artificial trend within. Due to regime-like behaviour of AR it is difficult for statistical methods to properly detect epochs of offsets, which was previously reported by climatologists.

Predicting the incidence of hand, foot and mouth disease in Sichuan province, China using the ARIMA model.

PubMed

Liu, L; Luan, R S; Yin, F; Zhu, X P; Lü, Q

2016-01-01

Hand, foot and mouth disease (HFMD) is an infectious disease caused by enteroviruses, which usually occurs in children aged <5 years. In China, the HFMD situation is worsening, with increasing number of cases nationwide. Therefore, monitoring and predicting HFMD incidence are urgently needed to make control measures more effective. In this study, we applied an autoregressive integrated moving average (ARIMA) model to forecast HFMD incidence in Sichuan province, China. HFMD infection data from January 2010 to June 2014 were used to fit the ARIMA model. The coefficient of determination (R 2), normalized Bayesian Information Criterion (BIC) and mean absolute percentage of error (MAPE) were used to evaluate the goodness-of-fit of the constructed models. The fitted ARIMA model was applied to forecast the incidence of HMFD from April to June 2014. The goodness-of-fit test generated the optimum general multiplicative seasonal ARIMA (1,0,1) × (0,1,0)12 model (R 2 = 0·692, MAPE = 15·982, BIC = 5·265), which also showed non-significant autocorrelations in the residuals of the model (P = 0·893). The forecast incidence values of the ARIMA (1,0,1) × (0,1,0)12 model from July to December 2014 were 4103-9987, which were proximate forecasts. The ARIMA model could be applied to forecast HMFD incidence trend and provide support for HMFD prevention and control. Further observations should be carried out continually into the time sequence, and the parameters of the models could be adjusted because HMFD incidence will not be absolutely stationary in the future.

Profiling physicochemical and planktonic features from discretely/continuously sampled surface water.

PubMed

Oita, Azusa; Tsuboi, Yuuri; Date, Yasuhiro; Oshima, Takahiro; Sakata, Kenji; Yokoyama, Akiko; Moriya, Shigeharu; Kikuchi, Jun

2018-04-24

There is an increasing need for assessing aquatic ecosystems that are globally endangered. Since aquatic ecosystems are complex, integrated consideration of multiple factors utilizing omics technologies can help us better understand aquatic ecosystems. An integrated strategy linking three analytical (machine learning, factor mapping, and forecast-error-variance decomposition) approaches for extracting the features of surface water from datasets comprising ions, metabolites, and microorganisms is proposed herein. The three developed approaches can be employed for diverse datasets of sample sizes and experimentally analyzed factors. The three approaches are applied to explore the features of bay water surrounding Odaiba, Tokyo, Japan, as a case study. Firstly, the machine learning approach separated 681 surface water samples within Japan into three clusters, categorizing Odaiba water into seawater with relatively low inorganic ions, including Mg, Ba, and B. Secondly, the factor mapping approach illustrated Odaiba water samples from the summer as rich in multiple amino acids and some other metabolites and poor in inorganic ions relative to other seasons based on their seasonal dynamics. Finally, forecast-error-variance decomposition using vector autoregressive models indicated that a type of microalgae (Raphidophyceae) grows in close correlation with alanine, succinic acid, and valine on filters and with isobutyric acid and 4-hydroxybenzoic acid in filtrate, Ba, and average wind speed. Our integrated strategy can be used to examine many biological, chemical, and environmental physical factors to analyze surface water. Copyright © 2018. Published by Elsevier B.V.

Smoothing strategies combined with ARIMA and neural networks to improve the forecasting of traffic accidents.

PubMed

Barba, Lida; Rodríguez, Nibaldo; Montt, Cecilia

2014-01-01

Two smoothing strategies combined with autoregressive integrated moving average (ARIMA) and autoregressive neural networks (ANNs) models to improve the forecasting of time series are presented. The strategy of forecasting is implemented using two stages. In the first stage the time series is smoothed using either, 3-point moving average smoothing, or singular value Decomposition of the Hankel matrix (HSVD). In the second stage, an ARIMA model and two ANNs for one-step-ahead time series forecasting are used. The coefficients of the first ANN are estimated through the particle swarm optimization (PSO) learning algorithm, while the coefficients of the second ANN are estimated with the resilient backpropagation (RPROP) learning algorithm. The proposed models are evaluated using a weekly time series of traffic accidents of Valparaíso, Chilean region, from 2003 to 2012. The best result is given by the combination HSVD-ARIMA, with a MAPE of 0:26%, followed by MA-ARIMA with a MAPE of 1:12%; the worst result is given by the MA-ANN based on PSO with a MAPE of 15:51%.

Linear and nonlinear trending and prediction for AVHRR time series data

NASA Technical Reports Server (NTRS)

Smid, J.; Volf, P.; Slama, M.; Palus, M.

1995-01-01

The variability of AVHRR calibration coefficient in time was analyzed using algorithms of linear and non-linear time series analysis. Specifically we have used the spline trend modeling, autoregressive process analysis, incremental neural network learning algorithm and redundancy functional testing. The analysis performed on available AVHRR data sets revealed that (1) the calibration data have nonlinear dependencies, (2) the calibration data depend strongly on the target temperature, (3) both calibration coefficients and the temperature time series can be modeled, in the first approximation, as autonomous dynamical systems, (4) the high frequency residuals of the analyzed data sets can be best modeled as an autoregressive process of the 10th degree. We have dealt with a nonlinear identification problem and the problem of noise filtering (data smoothing). The system identification and filtering are significant problems for AVHRR data sets. The algorithms outlined in this study can be used for the future EOS missions. Prediction and smoothing algorithms for time series of calibration data provide a functional characterization of the data. Those algorithms can be particularly useful when calibration data are incomplete or sparse.

The comparison study among several data transformations in autoregressive modeling

NASA Astrophysics Data System (ADS)

Setiyowati, Susi; Waluyo, Ramdhani Try

2015-12-01

In finance, the adjusted close of stocks are used to observe the performance of a company. The extreme prices, which may increase or decrease drastically, are often become particular concerned since it can impact to bankruptcy. As preventing action, the investors have to observe the future (forecasting) stock prices comprehensively. For that purpose, time series analysis could be one of statistical methods that can be implemented, for both stationary and non-stationary processes. Since the variability process of stocks prices tend to large and also most of time the extreme values are always exist, then it is necessary to do data transformation so that the time series models, i.e. autoregressive model, could be applied appropriately. One of popular data transformation in finance is return model, in addition to ratio of logarithm and some others Tukey ladder transformation. In this paper these transformations are applied to AR stationary models and non-stationary ARCH and GARCH models through some simulations with varying parameters. As results, this work present the suggestion table that shows transformations behavior for some condition of parameters and models. It is confirmed that the better transformation is obtained, depends on type of data distributions. In other hands, the parameter conditions term give significant influence either.

Optimizing Estimates of Instantaneous Heart Rate from Pulse Wave Signals with the Synchrosqueezing Transform.

PubMed

Wu, Hau-Tieng; Lewis, Gregory F; Davila, Maria I; Daubechies, Ingrid; Porges, Stephen W

2016-10-17

With recent advances in sensor and computer technologies, the ability to monitor peripheral pulse activity is no longer limited to the laboratory and clinic. Now inexpensive sensors, which interface with smartphones or other computer-based devices, are expanding into the consumer market. When appropriate algorithms are applied, these new technologies enable ambulatory monitoring of dynamic physiological responses outside the clinic in a variety of applications including monitoring fatigue, health, workload, fitness, and rehabilitation. Several of these applications rely upon measures derived from peripheral pulse waves measured via contact or non-contact photoplethysmography (PPG). As technologies move from contact to non-contact PPG, there are new challenges. The technology necessary to estimate average heart rate over a few seconds from a noncontact PPG is available. However, a technology to precisely measure instantaneous heat rate (IHR) from non-contact sensors, on a beat-to-beat basis, is more challenging. The objective of this paper is to develop an algorithm with the ability to accurately monitor IHR from peripheral pulse waves, which provides an opportunity to measure the neural regulation of the heart from the beat-to-beat heart rate pattern (i.e., heart rate variability). The adaptive harmonic model is applied to model the contact or non-contact PPG signals, and a new methodology, the Synchrosqueezing Transform (SST), is applied to extract IHR. The body sway rhythm inherited in the non-contact PPG signal is modeled and handled by the notion of wave-shape function. The SST optimizes the extraction of IHR from the PPG signals and the technique functions well even during periods of poor signal to noise. We contrast the contact and non-contact indices of PPG derived heart rate with a criterion electrocardiogram (ECG). ECG and PPG signals were monitored in 21 healthy subjects performing tasks with different physical demands. The root mean square error of IHR estimated by SST is significantly better than commonly applied methods such as autoregressive (AR) method. In the walking situation, while AR method fails, SST still provides a reasonably good result. The SST processed PPG data provided an accurate estimate of the ECG derived IHR and consistently performed better than commonly applied methods such as autoregressive method.

Forecasting Natural Gas Prices Using Wavelets, Time Series, and Artificial Neural Networks

PubMed Central

2015-01-01

Following the unconventional gas revolution, the forecasting of natural gas prices has become increasingly important because the association of these prices with those of crude oil has weakened. With this as motivation, we propose some modified hybrid models in which various combinations of the wavelet approximation, detail components, autoregressive integrated moving average, generalized autoregressive conditional heteroskedasticity, and artificial neural network models are employed to predict natural gas prices. We also emphasize the boundary problem in wavelet decomposition, and compare results that consider the boundary problem case with those that do not. The empirical results show that our suggested approach can handle the boundary problem, such that it facilitates the extraction of the appropriate forecasting results. The performance of the wavelet-hybrid approach was superior in all cases, whereas the application of detail components in the forecasting was only able to yield a small improvement in forecasting performance. Therefore, forecasting with only an approximation component would be acceptable, in consideration of forecasting efficiency. PMID:26539722

Increase in suicides the months after the death of Robin Williams in the US

PubMed Central

Santaella-Tenorio, Julian; Keyes, Katherine M.

2018-01-01

Investigating suicides following the death of Robin Williams, a beloved actor and comedian, on August 11th, 2014, we used time-series analysis to estimate the expected number of suicides during the months following Williams’ death. Monthly suicide count data in the US (1999–2015) were from the Centers for Disease Control and Prevention Wide-ranging ONline Data for Epidemiologic Research (CDC WONDER). Expected suicides were calculated using a seasonal autoregressive integrated moving averages model to account for both the seasonal patterns and autoregression. Time-series models indicated that we would expect 16,849 suicides from August to December 2014; however, we observed 18,690 suicides in that period, suggesting an excess of 1,841 cases (9.85% increase). Although excess suicides were observed across gender and age groups, males and persons aged 30–44 had the greatest increase in excess suicide events. This study documents associations between Robin Williams’ death and suicide deaths in the population thereafter. PMID:29415016

Forecasting Natural Gas Prices Using Wavelets, Time Series, and Artificial Neural Networks.

PubMed

Jin, Junghwan; Kim, Jinsoo

2015-01-01

Following the unconventional gas revolution, the forecasting of natural gas prices has become increasingly important because the association of these prices with those of crude oil has weakened. With this as motivation, we propose some modified hybrid models in which various combinations of the wavelet approximation, detail components, autoregressive integrated moving average, generalized autoregressive conditional heteroskedasticity, and artificial neural network models are employed to predict natural gas prices. We also emphasize the boundary problem in wavelet decomposition, and compare results that consider the boundary problem case with those that do not. The empirical results show that our suggested approach can handle the boundary problem, such that it facilitates the extraction of the appropriate forecasting results. The performance of the wavelet-hybrid approach was superior in all cases, whereas the application of detail components in the forecasting was only able to yield a small improvement in forecasting performance. Therefore, forecasting with only an approximation component would be acceptable, in consideration of forecasting efficiency.

Model-Based Referenceless Quality Metric of 3D Synthesized Images Using Local Image Description.

PubMed

Gu, Ke; Jakhetiya, Vinit; Qiao, Jun-Fei; Li, Xiaoli; Lin, Weisi; Thalmann, Daniel

2017-07-28

New challenges have been brought out along with the emerging of 3D-related technologies such as virtual reality (VR), augmented reality (AR), and mixed reality (MR). Free viewpoint video (FVV), due to its applications in remote surveillance, remote education, etc, based on the flexible selection of direction and viewpoint, has been perceived as the development direction of next-generation video technologies and has drawn a wide range of researchers' attention. Since FVV images are synthesized via a depth image-based rendering (DIBR) procedure in the "blind" environment (without reference images), a reliable real-time blind quality evaluation and monitoring system is urgently required. But existing assessment metrics do not render human judgments faithfully mainly because geometric distortions are generated by DIBR. To this end, this paper proposes a novel referenceless quality metric of DIBR-synthesized images using the autoregression (AR)-based local image description. It was found that, after the AR prediction, the reconstructed error between a DIBR-synthesized image and its AR-predicted image can accurately capture the geometry distortion. The visual saliency is then leveraged to modify the proposed blind quality metric to a sizable margin. Experiments validate the superiority of our no-reference quality method as compared with prevailing full-, reduced- and no-reference models.

Stochastic Parametrization for the Impact of Neglected Variability Patterns

NASA Astrophysics Data System (ADS)

Kaiser, Olga; Hien, Steffen; Achatz, Ulrich; Horenko, Illia

2017-04-01

An efficient description of the gravity wave variability and the related spontaneous emission processes requires an empirical stochastic closure for the impact of neglected variability patterns (subgridscales or SGS). In particular, we focus on the analysis of the IGW emission within a tangent linear model which requires a stochastic SGS parameterization for taking the self interaction of the ageostrophic flow components into account. For this purpose, we identify the best SGS model in terms of exactness and simplicity by deploying a wide range of different data-driven model classes, including standard stationary regression models, autoregression and artificial neuronal networks models - as well as the family of nonstationary models like FEM-BV-VARX model class (Finite Element based vector autoregressive time series analysis with bounded variation of the model parameters). The models are used to investigate the main characteristics of the underlying dynamics and to explore the significant spatial and temporal neighbourhood dependencies. The best SGS model in terms of exactness and simplicity is obtained for the nonstationary FEM-BV-VARX setting, determining only direct spatial and temporal neighbourhood as significant - and allowing to drastically reduce the number of informations that are required for the optimal SGS. Additionally, the models are characterized by sets of vector- and matrix-valued parameters that must be inferred from big data sets provided by simulations - making it a task that can not be solved without deploying high-performance computing facilities (HPC).

Adaptive spline autoregression threshold method in forecasting Mitsubishi car sales volume at PT Srikandi Diamond Motors

NASA Astrophysics Data System (ADS)

Susanti, D.; Hartini, E.; Permana, A.

2017-01-01

Sale and purchase of the growing competition between companies in Indonesian, make every company should have a proper planning in order to win the competition with other companies. One of the things that can be done to design the plan is to make car sales forecast for the next few periods, it’s required that the amount of inventory of cars that will be sold in proportion to the number of cars needed. While to get the correct forecasting, on of the methods that can be used is the method of Adaptive Spline Threshold Autoregression (ASTAR). Therefore, this time the discussion will focus on the use of Adaptive Spline Threshold Autoregression (ASTAR) method in forecasting the volume of car sales in PT.Srikandi Diamond Motors using time series data.In the discussion of this research, forecasting using the method of forecasting value Adaptive Spline Threshold Autoregression (ASTAR) produce approximately correct.

Decoupling emissions of greenhouse gas, urbanization, energy and income: analysis from the economy of China.

PubMed

Wang, Tianqiong; Riti, Joshua Sunday; Shu, Yang

2018-05-08

The adoption and ratification of relevant policies, particularly the household enrolment system metamorphosis in China, led to rising urbanization growth. As the leading developing economy, China has experienced a drastic and rapid increase in the rate of urbanization, energy use, economic growth and greenhouse gas (GHG) pollution for the past 30 years. The knowledge of the dynamic interrelationships among these trends has a plethora of implications ranging from demographic, energy, and environmental and sustainable development policies. This study analyzes the role of urbanization in decoupling GHG emissions, energy, and income in China while considering the critical contribution of energy use. As a contribution to the extant body of literature, the present research introduces a new phenomenon called "the environmental urbanization Kuznets curve" (EUKC), which shows that at the early stage of urbanization, the environment degrades however, after a threshold point the technique effects surface and environmental degradation reduces with rise in urbanization. Applying the autoregressive distributed lag model and the vector error correction model, the paper finds the presence of inverted U-shaped curve between urbanization and GHG emission of CO 2 , while the same hypothesis cannot be found between income and GHG emission of CO 2 . Energy use in all the models contributes to GHG emission of CO 2 . In decoupling greenhouse gas emissions, urbanization, energy, and income, articulated and well-implemented energy and urbanization policies should be considered.

Applying Bayesian hierarchical models to examine motorcycle crashes at signalized intersections.

PubMed

Haque, Md Mazharul; Chin, Hoong Chor; Huang, Helai

2010-01-01

Motorcycles are overrepresented in road traffic crashes and particularly vulnerable at signalized intersections. The objective of this study is to identify causal factors affecting the motorcycle crashes at both four-legged and T signalized intersections. Treating the data in time-series cross-section panels, this study explores different Hierarchical Poisson models and found that the model allowing autoregressive lag-1 dependence specification in the error term is the most suitable. Results show that the number of lanes at the four-legged signalized intersections significantly increases motorcycle crashes largely because of the higher exposure resulting from higher motorcycle accumulation at the stop line. Furthermore, the presence of a wide median and an uncontrolled left-turn lane at major roadways of four-legged intersections exacerbate this potential hazard. For T signalized intersections, the presence of exclusive right-turn lane at both major and minor roadways and an uncontrolled left-turn lane at major roadways increases motorcycle crashes. Motorcycle crashes increase on high-speed roadways because they are more vulnerable and less likely to react in time during conflicts. The presence of red light cameras reduces motorcycle crashes significantly for both four-legged and T intersections. With the red light camera, motorcycles are less exposed to conflicts because it is observed that they are more disciplined in queuing at the stop line and less likely to jump start at the start of green.

A modified NARMAX model-based self-tuner with fault tolerance for unknown nonlinear stochastic hybrid systems with an input-output direct feed-through term.

PubMed

Tsai, Jason S-H; Hsu, Wen-Teng; Lin, Long-Guei; Guo, Shu-Mei; Tann, Joseph W

2014-01-01

A modified nonlinear autoregressive moving average with exogenous inputs (NARMAX) model-based state-space self-tuner with fault tolerance is proposed in this paper for the unknown nonlinear stochastic hybrid system with a direct transmission matrix from input to output. Through the off-line observer/Kalman filter identification method, one has a good initial guess of modified NARMAX model to reduce the on-line system identification process time. Then, based on the modified NARMAX-based system identification, a corresponding adaptive digital control scheme is presented for the unknown continuous-time nonlinear system, with an input-output direct transmission term, which also has measurement and system noises and inaccessible system states. Besides, an effective state space self-turner with fault tolerance scheme is presented for the unknown multivariable stochastic system. A quantitative criterion is suggested by comparing the innovation process error estimated by the Kalman filter estimation algorithm, so that a weighting matrix resetting technique by adjusting and resetting the covariance matrices of parameter estimate obtained by the Kalman filter estimation algorithm is utilized to achieve the parameter estimation for faulty system recovery. Consequently, the proposed method can effectively cope with partially abrupt and/or gradual system faults and input failures by the fault detection. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

A robust damage-detection technique with environmental variability combining time-series models with principal components

NASA Astrophysics Data System (ADS)

Lakshmi, K.; Rama Mohan Rao, A.

2014-10-01

In this paper, a novel output-only damage-detection technique based on time-series models for structural health monitoring in the presence of environmental variability and measurement noise is presented. The large amount of data obtained in the form of time-history response is transformed using principal component analysis, in order to reduce the data size and thereby improve the computational efficiency of the proposed algorithm. The time instant of damage is obtained by fitting the acceleration time-history data from the structure using autoregressive (AR) and AR with exogenous inputs time-series prediction models. The probability density functions (PDFs) of damage features obtained from the variances of prediction errors corresponding to references and healthy current data are found to be shifting from each other due to the presence of various uncertainties such as environmental variability and measurement noise. Control limits using novelty index are obtained using the distances of the peaks of the PDF curves in healthy condition and used later for determining the current condition of the structure. Numerical simulation studies have been carried out using a simply supported beam and also validated using an experimental benchmark data corresponding to a three-storey-framed bookshelf structure proposed by Los Alamos National Laboratory. Studies carried out in this paper clearly indicate the efficiency of the proposed algorithm for damage detection in the presence of measurement noise and environmental variability.

Dynamic GSCA (Generalized Structured Component Analysis) with Applications to the Analysis of Effective Connectivity in Functional Neuroimaging Data

ERIC Educational Resources Information Center

Jung, Kwanghee; Takane, Yoshio; Hwang, Heungsun; Woodward, Todd S.

2012-01-01

We propose a new method of structural equation modeling (SEM) for longitudinal and time series data, named Dynamic GSCA (Generalized Structured Component Analysis). The proposed method extends the original GSCA by incorporating a multivariate autoregressive model to account for the dynamic nature of data taken over time. Dynamic GSCA also…

Fault detection using a two-model test for changes in the parameters of an autoregressive time series

NASA Technical Reports Server (NTRS)

Scholtz, P.; Smyth, P.

1992-01-01

This article describes an investigation of a statistical hypothesis testing method for detecting changes in the characteristics of an observed time series. The work is motivated by the need for practical automated methods for on-line monitoring of Deep Space Network (DSN) equipment to detect failures and changes in behavior. In particular, on-line monitoring of the motor current in a DSN 34-m beam waveguide (BWG) antenna is used as an example. The algorithm is based on a measure of the information theoretic distance between two autoregressive models: one estimated with data from a dynamic reference window and one estimated with data from a sliding reference window. The Hinkley cumulative sum stopping rule is utilized to detect a change in the mean of this distance measure, corresponding to the detection of a change in the underlying process. The basic theory behind this two-model test is presented, and the problem of practical implementation is addressed, examining windowing methods, model estimation, and detection parameter assignment. Results from the five fault-transition simulations are presented to show the possible limitations of the detection method, and suggestions for future implementation are given.

Autoregressive model in the Lp norm space for EEG analysis.

PubMed

Li, Peiyang; Wang, Xurui; Li, Fali; Zhang, Rui; Ma, Teng; Peng, Yueheng; Lei, Xu; Tian, Yin; Guo, Daqing; Liu, Tiejun; Yao, Dezhong; Xu, Peng

2015-01-30

The autoregressive (AR) model is widely used in electroencephalogram (EEG) analyses such as waveform fitting, spectrum estimation, and system identification. In real applications, EEGs are inevitably contaminated with unexpected outlier artifacts, and this must be overcome. However, most of the current AR models are based on the L2 norm structure, which exaggerates the outlier effect due to the square property of the L2 norm. In this paper, a novel AR object function is constructed in the Lp (p≤1) norm space with the aim to compress the outlier effects on EEG analysis, and a fast iteration procedure is developed to solve this new AR model. The quantitative evaluation using simulated EEGs with outliers proves that the proposed Lp (p≤1) AR can estimate the AR parameters more robustly than the Yule-Walker, Burg and LS methods, under various simulated outlier conditions. The actual application to the resting EEG recording with ocular artifacts also demonstrates that Lp (p≤1) AR can effectively address the outliers and recover a resting EEG power spectrum that is more consistent with its physiological basis. Copyright © 2014 Elsevier B.V. All rights reserved.

Neural net forecasting for geomagnetic activity

NASA Technical Reports Server (NTRS)

Hernandez, J. V.; Tajima, T.; Horton, W.

1993-01-01

We use neural nets to construct nonlinear models to forecast the AL index given solar wind and interplanetary magnetic field (IMF) data. We follow two approaches: (1) the state space reconstruction approach, which is a nonlinear generalization of autoregressive-moving average models (ARMA) and (2) the nonlinear filter approach, which reduces to a moving average model (MA) in the linear limit. The database used here is that of Bargatze et al. (1985).

Fast Algorithms for Mining Co-evolving Time Series

DTIC Science & Technology

2011-09-01

Keogh et al., 2001, 2004] and (b) forecasting, like an autoregressive integrated moving average model ( ARIMA ) and related meth- ods [Box et al., 1994...computing hardware? We develop models to mine time series with missing values, to extract compact representation from time sequences, to segment the...sequences, and to do forecasting. For large scale data, we propose algorithms for learning time series models , in particular, including Linear Dynamical

Three Dimensional Object Recognition Using a Complex Autoregressive Model

DTIC Science & Technology

1993-12-01

3.4.2 Template Matching Algorithm ...................... 3-16 3.4.3 K-Nearest-Neighbor ( KNN ) Techniques ................. 3-25 3.4.4 Hidden Markov Model...Neighbor ( KNN ) Test Results ...................... 4-13 4.2.1 Single-Look 1-NN Testing .......................... 4-14 4.2.2 Multiple-Look 1-NN Testing...4-15 4.2.3 Discussion of KNN Test Results ...................... 4-15 4.3 Hidden Markov Model (HMM) Test Results

On the measurement of stability in over-time data.

PubMed

Kenny, D A; Campbell, D T

1989-06-01

In this article, autoregressive models and growth curve models are compared. Autoregressive models are useful because they allow for random change, permit scores to increase or decrease, and do not require strong assumptions about the level of measurement. Three previously presented designs for estimating stability are described: (a) time-series, (b) simplex, and (c) two-wave, one-factor methods. A two-wave, multiple-factor model also is presented, in which the variables are assumed to be caused by a set of latent variables. The factor structure does not change over time and so the synchronous relationships are temporally invariant. The factors do not cause each other and have the same stability. The parameters of the model are the factor loading structure, each variable's reliability, and the stability of the factors. We apply the model to two data sets. For eight cognitive skill variables measured at four times, the 2-year stability is estimated to be .92 and the 6-year stability is .83. For nine personality variables, the 3-year stability is .68. We speculate that for many variables there are two components: one component that changes very slowly (the trait component) and another that changes very rapidly (the state component); thus each variable is a mixture of trait and state. Circumstantial evidence supporting this view is presented.

A stochastic post-processing method for solar irradiance forecasts derived from NWPs models

NASA Astrophysics Data System (ADS)

Lara-Fanego, V.; Pozo-Vazquez, D.; Ruiz-Arias, J. A.; Santos-Alamillos, F. J.; Tovar-Pescador, J.

2010-09-01

Solar irradiance forecast is an important area of research for the future of the solar-based renewable energy systems. Numerical Weather Prediction models (NWPs) have proved to be a valuable tool for solar irradiance forecasting with lead time up to a few days. Nevertheless, these models show low skill in forecasting the solar irradiance under cloudy conditions. Additionally, climatic (averaged over seasons) aerosol loading are usually considered in these models, leading to considerable errors for the Direct Normal Irradiance (DNI) forecasts during high aerosols load conditions. In this work we propose a post-processing method for the Global Irradiance (GHI) and DNI forecasts derived from NWPs. Particularly, the methods is based on the use of Autoregressive Moving Average with External Explanatory Variables (ARMAX) stochastic models. These models are applied to the residuals of the NWPs forecasts and uses as external variables the measured cloud fraction and aerosol loading of the day previous to the forecast. The method is evaluated for a set one-moth length three-days-ahead forecast of the GHI and DNI, obtained based on the WRF mesoscale atmospheric model, for several locations in Andalusia (Southern Spain). The Cloud fraction is derived from MSG satellite estimates and the aerosol loading from the MODIS platform estimates. Both sources of information are readily available at the time of the forecast. Results showed a considerable improvement of the forecasting skill of the WRF model using the proposed post-processing method. Particularly, relative improvement (in terms of the RMSE) for the DNI during summer is about 20%. A similar value is obtained for the GHI during the winter.

An algebraic method for constructing stable and consistent autoregressive filters

DOE Office of Scientific and Technical Information (OSTI.GOV)

Harlim, John, E-mail: jharlim@psu.edu; Department of Meteorology, the Pennsylvania State University, University Park, PA 16802; Hong, Hoon, E-mail: hong@ncsu.edu

2015-02-15

In this paper, we introduce an algebraic method to construct stable and consistent univariate autoregressive (AR) models of low order for filtering and predicting nonlinear turbulent signals with memory depth. By stable, we refer to the classical stability condition for the AR model. By consistent, we refer to the classical consistency constraints of Adams–Bashforth methods of order-two. One attractive feature of this algebraic method is that the model parameters can be obtained without directly knowing any training data set as opposed to many standard, regression-based parameterization methods. It takes only long-time average statistics as inputs. The proposed method provides amore » discretization time step interval which guarantees the existence of stable and consistent AR model and simultaneously produces the parameters for the AR models. In our numerical examples with two chaotic time series with different characteristics of decaying time scales, we find that the proposed AR models produce significantly more accurate short-term predictive skill and comparable filtering skill relative to the linear regression-based AR models. These encouraging results are robust across wide ranges of discretization times, observation times, and observation noise variances. Finally, we also find that the proposed model produces an improved short-time prediction relative to the linear regression-based AR-models in forecasting a data set that characterizes the variability of the Madden–Julian Oscillation, a dominant tropical atmospheric wave pattern.« less

Modeling volatility using state space models.

PubMed

Timmer, J; Weigend, A S

1997-08-01

In time series problems, noise can be divided into two categories: dynamic noise which drives the process, and observational noise which is added in the measurement process, but does not influence future values of the system. In this framework, we show that empirical volatilities (the squared relative returns of prices) exhibit a significant amount of observational noise. To model and predict their time evolution adequately, we estimate state space models that explicitly include observational noise. We obtain relaxation times for shocks in the logarithm of volatility ranging from three weeks (for foreign exchange) to three to five months (for stock indices). In most cases, a two-dimensional hidden state is required to yield residuals that are consistent with white noise. We compare these results with ordinary autoregressive models (without a hidden state) and find that autoregressive models underestimate the relaxation times by about two orders of magnitude since they do not distinguish between observational and dynamic noise. This new interpretation of the dynamics of volatility in terms of relaxators in a state space model carries over to stochastic volatility models and to GARCH models, and is useful for several problems in finance, including risk management and the pricing of derivative securities. Data sets used: Olsen & Associates high frequency DEM/USD foreign exchange rates (8 years). Nikkei 225 index (40 years). Dow Jones Industrial Average (25 years).

Forecasting zoonotic cutaneous leishmaniasis using meteorological factors in eastern Fars province, Iran: a SARIMA analysis.

PubMed

Tohidinik, Hamid Reza; Mohebali, Mehdi; Mansournia, Mohammad Ali; Niakan Kalhori, Sharareh R; Ali-Akbarpour, Mohsen; Yazdani, Kamran

2018-05-22

To predict the occurrence of zoonotic cutaneous leishmaniasis (ZCL) and evaluate the effect of climatic variables on disease incidence in the east of Fars province, Iran using the Seasonal Autoregressive Integrated Moving Average (SARIMA) model. The Box-Jenkins approach was applied to fit the SARIMA model for ZCL incidence from 2004 to 2015. Then the model was used to predict the number of ZCL cases for the year 2016. Finally, we assessed the relation of meteorological variables (rainfall, rainy days, temperature, hours of sunshine and relative humidity) with ZCL incidence. SARIMA(2,0,0) (2,1,0)12 was the preferred model for predicting ZCL incidence in the east of Fars province (validation Root Mean Square Error, RMSE = 0.27). It showed that ZCL incidence in a given month can be estimated by the number of cases occurring 1 and 2 months, as well as 12 and 24 months earlier. The predictive power of SARIMA models was improved by the inclusion of rainfall at a lag of 2 months (β = -0.02), rainy days at a lag of 2 months (β = -0.09) and relative humidity at a lag of 8 months (β = 0.13) as external regressors (P-values < 0.05). The latter was the best climatic variable for predicting ZCL cases (validation RMSE = 0.26). Time series models can be useful tools to predict the trend of ZCL in Fars province, Iran; thus, they can be used in the planning of public health programmes. Introducing meteorological variables into the models may improve their precision. © 2018 John Wiley & Sons Ltd.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cavanaugh, J.E.; McQuarrie, A.D.; Shumway, R.H.

Conventional methods for discriminating between earthquakes and explosions at regional distances have concentrated on extracting specific features such as amplitude and spectral ratios from the waveforms of the P and S phases. We consider here an optimum nonparametric classification procedure derived from the classical approach to discriminating between two Gaussian processes with unequal spectra. Two robust variations based on the minimum discrimination information statistic and Renyi's entropy are also considered. We compare the optimum classification procedure with various amplitude and spectral ratio discriminants and show that its performance is superior when applied to a small population of 8 land-based earthquakesmore » and 8 mining explosions recorded in Scandinavia. Several parametric characterizations of the notion of complexity based on modeling earthquakes and explosions as autoregressive or modulated autoregressive processes are also proposed and their performance compared with the nonparametric and feature extraction approaches.« less

Long short-term memory neural network for air pollutant concentration predictions: Method development and evaluation.

PubMed

Li, Xiang; Peng, Ling; Yao, Xiaojing; Cui, Shaolong; Hu, Yuan; You, Chengzeng; Chi, Tianhe

2017-12-01

Air pollutant concentration forecasting is an effective method of protecting public health by providing an early warning against harmful air pollutants. However, existing methods of air pollutant concentration prediction fail to effectively model long-term dependencies, and most neglect spatial correlations. In this paper, a novel long short-term memory neural network extended (LSTME) model that inherently considers spatiotemporal correlations is proposed for air pollutant concentration prediction. Long short-term memory (LSTM) layers were used to automatically extract inherent useful features from historical air pollutant data, and auxiliary data, including meteorological data and time stamp data, were merged into the proposed model to enhance the performance. Hourly PM 2.5 (particulate matter with an aerodynamic diameter less than or equal to 2.5 μm) concentration data collected at 12 air quality monitoring stations in Beijing City from Jan/01/2014 to May/28/2016 were used to validate the effectiveness of the proposed LSTME model. Experiments were performed using the spatiotemporal deep learning (STDL) model, the time delay neural network (TDNN) model, the autoregressive moving average (ARMA) model, the support vector regression (SVR) model, and the traditional LSTM NN model, and a comparison of the results demonstrated that the LSTME model is superior to the other statistics-based models. Additionally, the use of auxiliary data improved model performance. For the one-hour prediction tasks, the proposed model performed well and exhibited a mean absolute percentage error (MAPE) of 11.93%. In addition, we conducted multiscale predictions over different time spans and achieved satisfactory performance, even for 13-24 h prediction tasks (MAPE = 31.47%). Copyright © 2017 Elsevier Ltd. All rights reserved.

Reciprocal Influences between Parents' Marital Problems and Adolescent Internalizing and Externalizing Behavior

ERIC Educational Resources Information Center

Cui, Ming; Donnellan, M. Brent; Conger, Rand D.

2007-01-01

The present study examines reciprocal associations between marital functioning and adolescent maladjustment using cross-lagged autoregressive models. The research involved 451 early adolescents and their families and used a prospective, longitudinal research design with multi-informant methods. Results indicate that parental conflicts over child…