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.

Using a Hybrid Model to Forecast the Prevalence of Schistosomiasis in Humans

PubMed Central

Zhou, Lingling; Xia, Jing; Yu, Lijing; Wang, Ying; Shi, Yun; Cai, Shunxiang; Nie, Shaofa

2016-01-01

Background: We previously proposed a hybrid model combining both the autoregressive integrated moving average (ARIMA) and the nonlinear autoregressive neural network (NARNN) models in forecasting schistosomiasis. Our purpose in the current study was to forecast the annual prevalence of human schistosomiasis in Yangxin County, using our ARIMA-NARNN model, thereby further certifying the reliability of our hybrid model. Methods: We used the ARIMA, NARNN and ARIMA-NARNN models to fit and forecast the annual prevalence of schistosomiasis. The modeling time range included was the annual prevalence from 1956 to 2008 while the testing time range included was from 2009 to 2012. The mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were used to measure the model performance. We reconstructed the hybrid model to forecast the annual prevalence from 2013 to 2016. Results: The modeling and testing errors generated by the ARIMA-NARNN model were lower than those obtained from either the single ARIMA or NARNN models. The predicted annual prevalence from 2013 to 2016 demonstrated an initial decreasing trend, followed by an increase. Conclusions: The ARIMA-NARNN model can be well applied to analyze surveillance data for early warning systems for the control and elimination of schistosomiasis. PMID:27023573

Temporal Variability and Statistics of the Strehl Ratio in Adaptive-Optics Images

DTIC Science & Technology

2010-01-01

with the appropriate models and the residuals were extracted. This was done using the ARIMA modelling (Box & Jenkins 1970). ARIMA stands for...It was used here for the opposite goal – to obtain the values of the i.i.d. “noise” and test its distribution. Mixed ARIMA models of order 2 were...often sufficient to ensure non- significant autocorrelation of the residuals. Table 2 lists the stationary sequences with their respective ARIMA models

[Application of ARIMA model on prediction of malaria incidence].

PubMed

Jing, Xia; Hua-Xun, Zhang; Wen, Lin; Su-Jian, Pei; Ling-Cong, Sun; Xiao-Rong, Dong; Mu-Min, Cao; Dong-Ni, Wu; Shunxiang, Cai

2016-01-29

To predict the incidence of local malaria of Hubei Province applying the Autoregressive Integrated Moving Average model (ARIMA). SPSS 13.0 software was applied to construct the ARIMA model based on the monthly local malaria incidence in Hubei Province from 2004 to 2009. The local malaria incidence data of 2010 were used for model validation and evaluation. The model of ARIMA (1, 1, 1) (1, 1, 0) 12 was tested as relatively the best optimal with the AIC of 76.085 and SBC of 84.395. All the actual incidence data were in the range of 95% CI of predicted value of the model. The prediction effect of the model was acceptable. The ARIMA model could effectively fit and predict the incidence of local malaria of Hubei Province.

Application of time series analysis in modelling and forecasting emergency department visits in a medical centre in Southern Taiwan

PubMed Central

Juang, Wang-Chuan; Huang, Sin-Jhih; Huang, Fong-Dee; Cheng, Pei-Wen; Wann, Shue-Ren

2017-01-01

Objective Emergency department (ED) overcrowding is acknowledged as an increasingly important issue worldwide. Hospital managers are increasingly paying attention to ED crowding in order to provide higher quality medical services to patients. One of the crucial elements for a good management strategy is demand forecasting. Our study sought to construct an adequate model and to forecast monthly ED visits. Methods We retrospectively gathered monthly ED visits from January 2009 to December 2016 to carry out a time series autoregressive integrated moving average (ARIMA) analysis. Initial development of the model was based on past ED visits from 2009 to 2016. A best-fit model was further employed to forecast the monthly data of ED visits for the next year (2016). Finally, we evaluated the predicted accuracy of the identified model with the mean absolute percentage error (MAPE). The software packages SAS/ETS V.9.4 and Office Excel 2016 were used for all statistical analyses. Results A series of statistical tests showed that six models, including ARIMA (0, 0, 1), ARIMA (1, 0, 0), ARIMA (1, 0, 1), ARIMA (2, 0, 1), ARIMA (3, 0, 1) and ARIMA (5, 0, 1), were candidate models. The model that gave the minimum Akaike information criterion and Schwartz Bayesian criterion and followed the assumptions of residual independence was selected as the adequate model. Finally, a suitable ARIMA (0, 0, 1) structure, yielding a MAPE of 8.91%, was identified and obtained as Visitt=7111.161+(at+0.37462 at−1). Conclusion The ARIMA (0, 0, 1) model can be considered adequate for predicting future ED visits, and its forecast results can be used to aid decision-making processes. PMID:29196487

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

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

Proceedings of the Annual Precise Time and Time Interval (PTTI) applications and Planning Meeting (20th) Held in Vienna, Virginia on 29 November-1 December 1988

DTIC Science & Technology

1988-12-01

PERFORMANCE IN REAL TIME* Dr. James A. Barnes Austron Boulder, Co. Abstract Kalman filters and ARIMA models provide optimum control and evaluation tech...estimates of the model parameters (e.g., the phi’s and theta’s for an ARIMA model ). These model parameters are often evaluated in a batch mode on a...random walk FM, and linear frequency drift. In ARIMA models , this is equivalent to an ARIMA (0,2,2) with a non-zero average sec- ond difference. Using

Turbulence Time Series Data Hole Filling using Karhunen-Loeve and ARIMA methods

DTIC Science & Technology

2007-01-01

memory is represented by higher values of d. 4.1. ARIMA and EMD We applied an ARIMA (0,d,0) model to predict the behaviour of the final section of the...to a simplified ARIMA (0,d,0) model , which performed better than the linear interpolant but less effectively than the KL algorithm, disregarding edge...ar X iv :p hy si cs /0 70 12 38 v1 22 J an 2 00 7 Turbulence Time Series Data Hole Filling using Karhunen-Loève and ARIMA methods M P J L

Application of time series analysis in modelling and forecasting emergency department visits in a medical centre in Southern Taiwan.

PubMed

Juang, Wang-Chuan; Huang, Sin-Jhih; Huang, Fong-Dee; Cheng, Pei-Wen; Wann, Shue-Ren

2017-12-01

Emergency department (ED) overcrowding is acknowledged as an increasingly important issue worldwide. Hospital managers are increasingly paying attention to ED crowding in order to provide higher quality medical services to patients. One of the crucial elements for a good management strategy is demand forecasting. Our study sought to construct an adequate model and to forecast monthly ED visits. We retrospectively gathered monthly ED visits from January 2009 to December 2016 to carry out a time series autoregressive integrated moving average (ARIMA) analysis. Initial development of the model was based on past ED visits from 2009 to 2016. A best-fit model was further employed to forecast the monthly data of ED visits for the next year (2016). Finally, we evaluated the predicted accuracy of the identified model with the mean absolute percentage error (MAPE). The software packages SAS/ETS V.9.4 and Office Excel 2016 were used for all statistical analyses. A series of statistical tests showed that six models, including ARIMA (0, 0, 1), ARIMA (1, 0, 0), ARIMA (1, 0, 1), ARIMA (2, 0, 1), ARIMA (3, 0, 1) and ARIMA (5, 0, 1), were candidate models. The model that gave the minimum Akaike information criterion and Schwartz Bayesian criterion and followed the assumptions of residual independence was selected as the adequate model. Finally, a suitable ARIMA (0, 0, 1) structure, yielding a MAPE of 8.91%, was identified and obtained as Visit t =7111.161+(a t +0.37462 a t -1). The ARIMA (0, 0, 1) model can be considered adequate for predicting future ED visits, and its forecast results can be used to aid decision-making processes. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

A Computer Program for the Generation of ARIMA Data

ERIC Educational Resources Information Center

Green, Samuel B.; Noles, Keith O.

1977-01-01

The autoregressive integrated moving averages model (ARIMA) has been applied to time series data in psychological and educational research. A program is described that generates ARIMA data of a known order. The program enables researchers to explore statistical properties of ARIMA data and simulate systems producing time dependent observations.…

Time series ARIMA models for daily price of palm oil

NASA Astrophysics Data System (ADS)

Ariff, Noratiqah Mohd; Zamhawari, Nor Hashimah; Bakar, Mohd Aftar Abu

2015-02-01

Palm oil is deemed as one of the most important commodity that forms the economic backbone of Malaysia. Modeling and forecasting the daily price of palm oil is of great interest for Malaysia's economic growth. In this study, time series ARIMA models are used to fit the daily price of palm oil. The Akaike Infromation Criterion (AIC), Akaike Infromation Criterion with a correction for finite sample sizes (AICc) and Bayesian Information Criterion (BIC) are used to compare between different ARIMA models being considered. It is found that ARIMA(1,2,1) model is suitable for daily price of crude palm oil in Malaysia for the year 2010 to 2012.

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

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

Identification of AR(I)MA processes for modelling temporal correlations of GPS observations

NASA Astrophysics Data System (ADS)

Luo, X.; Mayer, M.; Heck, B.

2009-04-01

In many geodetic applications observations of the Global Positioning System (GPS) are routinely processed by means of the least-squares method. However, this algorithm delivers reliable estimates of unknown parameters und realistic accuracy measures only if both the functional and stochastic models are appropriately defined within GPS data processing. One deficiency of the stochastic model used in many GPS software products consists in neglecting temporal correlations of GPS observations. In practice the knowledge of the temporal stochastic behaviour of GPS observations can be improved by analysing time series of residuals resulting from the least-squares evaluation. This paper presents an approach based on the theory of autoregressive (integrated) moving average (AR(I)MA) processes to model temporal correlations of GPS observations using time series of observation residuals. A practicable integration of AR(I)MA models in GPS data processing requires the determination of the order parameters of AR(I)MA processes at first. In case of GPS, the identification of AR(I)MA processes could be affected by various factors impacting GPS positioning results, e.g. baseline length, multipath effects, observation weighting, or weather variations. The influences of these factors on AR(I)MA identification are empirically analysed based on a large amount of representative residual time series resulting from differential GPS post-processing using 1-Hz observation data collected within the permanent SAPOS® (Satellite Positioning Service of the German State Survey) network. Both short and long time series are modelled by means of AR(I)MA processes. The final order parameters are determined based on the whole residual database; the corresponding empirical distribution functions illustrate that multipath and weather variations seem to affect the identification of AR(I)MA processes much more significantly than baseline length and observation weighting. Additionally, the modelling results of temporal correlations using high-order AR(I)MA processes are compared with those by means of first order autoregressive (AR(1)) processes and empirically estimated autocorrelation functions.

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.

Modeling seasonal variation of hip fracture in Montreal, Canada.

PubMed

Modarres, Reza; Ouarda, Taha B M J; Vanasse, Alain; Orzanco, Maria Gabriela; Gosselin, Pierre

2012-04-01

The investigation of the association of the climate variables with hip fracture incidences is important in social health issues. This study examined and modeled the seasonal variation of monthly population based hip fracture rate (HFr) time series. The seasonal ARIMA time series modeling approach is used to model monthly HFr incidences time series of female and male patients of the ages 40-74 and 75+ of Montreal, Québec province, Canada, in the period of 1993-2004. The correlation coefficients between meteorological variables such as temperature, snow depth, rainfall depth and day length and HFr are significant. The nonparametric Mann-Kendall test for trend assessment and the nonparametric Levene's test and Wilcoxon's test for checking the difference of HFr before and after change point are also used. The seasonality in HFr indicated sharp difference between winter and summer time. The trend assessment showed decreasing trends in HFr of female and male groups. The nonparametric test also indicated a significant change of the mean HFr. A seasonal ARIMA model was applied for HFr time series without trend and a time trend ARIMA model (TT-ARIMA) was developed and fitted to HFr time series with a significant trend. The multi criteria evaluation showed the adequacy of SARIMA and TT-ARIMA models for modeling seasonal hip fracture time series with and without significant trend. In the time series analysis of HFr of the Montreal region, the effects of the seasonal variation of climate variables on hip fracture are clear. The Seasonal ARIMA model is useful for modeling HFr time series without trend. However, for time series with significant trend, the TT-ARIMA model should be applied for modeling HFr time series. Copyright © 2011 Elsevier Inc. All rights reserved.

Proceedings of the Third International Workshop on Multistrategy Learning, May 23-25 Harpers Ferry, WV.

DTIC Science & Technology

1996-09-16

approaches are: • Adaptive filtering • Single exponential smoothing (Brown, 1963) * The Box-Jenkins methodology ( ARIMA modeling ) - Linear exponential... ARIMA • Linear exponential smoothing: Holt’s two parameter modeling (Box and Jenkins, 1976). However, there are two approach (Holt et al., 1960) very...crucial disadvantages: The most important point in - Winters’ three parameter method (Winters, 1960) ARIMA modeling is model identification. As shown in

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.

[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).

[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.

Stochastic approaches for time series forecasting of boron: a case study of Western Turkey.

PubMed

Durdu, Omer Faruk

2010-10-01

In the present study, a seasonal and non-seasonal prediction of boron concentrations time series data for the period of 1996-2004 from Büyük Menderes river in western Turkey are addressed by means of linear stochastic models. The methodology presented here is to develop adequate linear stochastic models known as autoregressive integrated moving average (ARIMA) and multiplicative seasonal autoregressive integrated moving average (SARIMA) to predict boron content in the Büyük Menderes catchment. Initially, the Box-Whisker plots and Kendall's tau test are used to identify the trends during the study period. The measurements locations do not show significant overall trend in boron concentrations, though marginal increasing and decreasing trends are observed for certain periods at some locations. ARIMA modeling approach involves the following three steps: model identification, parameter estimation, and diagnostic checking. In the model identification step, considering the autocorrelation function (ACF) and partial autocorrelation function (PACF) results of boron data series, different ARIMA models are identified. The model gives the minimum Akaike information criterion (AIC) is selected as the best-fit model. The parameter estimation step indicates that the estimated model parameters are significantly different from zero. The diagnostic check step is applied to the residuals of the selected ARIMA models and the results indicate that the residuals are independent, normally distributed, and homoscadastic. For the model validation purposes, the predicted results using the best ARIMA models are compared to the observed data. The predicted data show reasonably good agreement with the actual data. The comparison of the mean and variance of 3-year (2002-2004) observed data vs predicted data from the selected best models show that the boron model from ARIMA modeling approaches could be used in a safe manner since the predicted values from these models preserve the basic statistics of observed data in terms of mean. The ARIMA modeling approach is recommended for predicting boron concentration series of a river.

Stochastic model to forecast ground-level ozone concentration at urban and rural areas.

PubMed

Dueñas, C; Fernández, M C; Cañete, S; Carretero, J; Liger, E

2005-12-01

Stochastic models that estimate the ground-level ozone concentrations in air at an urban and rural sampling points in South-eastern Spain have been developed. Studies of temporal series of data, spectral analyses of temporal series and ARIMA models have been used. The ARIMA model (1,0,0) x (1,0,1)24 satisfactorily predicts hourly ozone concentrations in the urban area. The ARIMA (2,1,1) x (0,1,1)24 has been developed for the rural area. In both sampling points, predictions of hourly ozone concentrations agree reasonably well with measured values. However, the prediction of hourly ozone concentrations in the rural point appears to be better than that of the urban point. The performance of ARIMA models suggests that this kind of modelling can be suitable for ozone concentrations forecasting.

Forecast Model Analysis for the Morbidity of Tuberculosis in Xinjiang, China

PubMed Central

Zheng, Yan-Ling; Zhang, Li-Ping; Zhang, Xue-Liang; Wang, Kai; Zheng, Yu-Jian

2015-01-01

Tuberculosis is a major global public health problem, which also affects economic and social development. China has the second largest burden of tuberculosis in the world. The tuberculosis morbidity in Xinjiang is much higher than the national situation; therefore, there is an urgent need for monitoring and predicting tuberculosis morbidity so as to make the control of tuberculosis more effective. Recently, the Box-Jenkins approach, specifically the autoregressive integrated moving average (ARIMA) model, is typically applied to predict the morbidity of infectious diseases; it can take into account changing trends, periodic changes, and random disturbances in time series. Autoregressive conditional heteroscedasticity (ARCH) models are the prevalent tools used to deal with time series heteroscedasticity. In this study, based on the data of the tuberculosis morbidity from January 2004 to June 2014 in Xinjiang, we establish the single ARIMA (1, 1, 2) (1, 1, 1)12 model and the combined ARIMA (1, 1, 2) (1, 1, 1)12-ARCH (1) model, which can be used to predict the tuberculosis morbidity successfully in Xinjiang. Comparative analyses show that the combined model is more effective. To the best of our knowledge, this is the first study to establish the ARIMA model and ARIMA-ARCH model for prediction and monitoring the monthly morbidity of tuberculosis in Xinjiang. Based on the results of this study, the ARIMA (1, 1, 2) (1, 1, 1)12-ARCH (1) model is suggested to give tuberculosis surveillance by providing estimates on tuberculosis morbidity trends in Xinjiang, China. PMID:25760345

Semi-Cooperative Learning in Smart Grid Agents

DTIC Science & Technology

2013-12-01

2009 HOEP and its ACF/PACF diagnostic functions. . . 37 2.21 Residuals from multiplicative seasonal ARIMA model fit on 2009 HOEP. . . . . . . . . . 38...2.22 Typical forecast for the next 72 hours based on the ARIMA model of Eq. 2.14. . . . . . . 40 3.1 Consumption capacity of two small villages over...1 (the training series). . 49 3.4 ARIMA model (Eq. 3.1) forecasts based on the full village 1 time series (top subfigure) and the first 24 hours of

Use of Time-Series, ARIMA Designs to Assess Program Efficacy.

ERIC Educational Resources Information Center

Braden, Jeffery P.; And Others

1990-01-01

Illustrates use of time-series designs for determining efficacy of interventions with fictitious data describing drug-abuse prevention program. Discusses problems and procedures associated with time-series data analysis using Auto Regressive Integrated Moving Averages (ARIMA) models. Example illustrates application of ARIMA analysis for…

Streamflow predictions in Alpine Catchments by using artificial neural networks. Application in the Alto Genil Basin (South Spain)

NASA Astrophysics Data System (ADS)

Jimeno-Saez, Patricia; Pegalajar-Cuellar, Manuel; Pulido-Velazquez, David

2017-04-01

This study explores techniques of modeling water inflow series, focusing on techniques of short-term steamflow prediction. An appropriate estimation of streamflow in advance is necessary to anticipate measures to mitigate the impacts and risks related to drought conditions. This study analyzes the prediction of future streamflow of nineteen subbasins in the Alto-Genil basin in Granada (Southeast of Spain). Some of these basin streamflow have an important component of snowmelt due to part of the system is located in Sierra Nevada Mountain Range, the highest mountain of continental Spain. Streamflow prediction models have been calibrated using time series of historical natural streamflows. The available streamflow measurements have been downloaded from several public data sources. These original data have been preprocessed to turn them to the original natural regime, removing the anthropic effects. The missing values in the adopted horizon period to calibrate the prediction models have been estimated by using a Temez hydrological balance model, approaching the snowmelt processes with a hybrid degree day method. In the experimentation, ARIMA models are used as baseline method, and recurrent neural networks ELMAN and nonlinear autoregressive neural network (NAR) to test if the prediction accuracy can be improved. After performing the multiple experiments with these models, non-parametric statistical tests are applied to select the best of these techniques. In the experiments carried out with ARIMA, it is concluded that ARIMA models are not adequate in this case study due to the existence of a nonlinear component that cannot be modeled. Secondly, ELMAN and NAR neural networks with multi-start training is performed with each network structure to deal with the local optimum problem, since in neural network training there is a very strong dependence on the initial weights of the network. The obtained results suggest that both neural networks are efficient for the short term prediction, surpassing the limitations of the ARIMA models and, in general, the experiments showed that NAR networks are the ones with the greatest generalization capability. Therefore, NAR networks are chosen as the starting point for other works, in which we study the streamflow predictions incorporating exogenous variables (as the Snow Cover Area), the sensitivity of the prediction to the initial conditions, multivariate streamflow predictions considering the spatial correlation between the sub-basins streamflow and the synthetic generations to assess droughts statistic. This research has been partially supported by the CGL2013-48424-C2-2-R (MINECO) and the PMAFI/06/14 (UCAM) projects.

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

Crude oil price analysis and forecasting based on variational mode decomposition and independent component analysis

NASA Astrophysics Data System (ADS)

E, Jianwei; Bao, Yanling; Ye, Jimin

2017-10-01

As one of the most vital energy resources in the world, crude oil plays a significant role in international economic market. The fluctuation of crude oil price has attracted academic and commercial attention. There exist many methods in forecasting the trend of crude oil price. However, traditional models failed in predicting accurately. Based on this, a hybrid method will be proposed in this paper, which combines variational mode decomposition (VMD), independent component analysis (ICA) and autoregressive integrated moving average (ARIMA), called VMD-ICA-ARIMA. The purpose of this study is to analyze the influence factors of crude oil price and predict the future crude oil price. Major steps can be concluded as follows: Firstly, applying the VMD model on the original signal (crude oil price), the modes function can be decomposed adaptively. Secondly, independent components are separated by the ICA, and how the independent components affect the crude oil price is analyzed. Finally, forecasting the price of crude oil price by the ARIMA model, the forecasting trend demonstrates that crude oil price declines periodically. Comparing with benchmark ARIMA and EEMD-ICA-ARIMA, VMD-ICA-ARIMA can forecast the crude oil price more accurately.

Disease management with ARIMA model in time series.

PubMed

Sato, Renato Cesar

2013-01-01

The evaluation of infectious and noninfectious disease management can be done through the use of a time series analysis. In this study, we expect to measure the results and prevent intervention effects on the disease. Clinical studies have benefited from the use of these techniques, particularly for the wide applicability of the ARIMA model. This study briefly presents the process of using the ARIMA model. This analytical tool offers a great contribution for researchers and healthcare managers in the evaluation of healthcare interventions in specific populations.

The development rainfall forecasting using kalman filter

NASA Astrophysics Data System (ADS)

Zulfi, Mohammad; Hasan, Moh.; Dwidja Purnomo, Kosala

2018-04-01

Rainfall forecasting is very interesting for agricultural planing. Rainfall information is useful to make decisions about the plan planting certain commodities. In this studies, the rainfall forecasting by ARIMA and Kalman Filter method. Kalman Filter method is used to declare a time series model of which is shown in the form of linear state space to determine the future forecast. This method used a recursive solution to minimize error. The rainfall data in this research clustered by K-means clustering. Implementation of Kalman Filter method is for modelling and forecasting rainfall in each cluster. We used ARIMA (p,d,q) to construct a state space for KalmanFilter model. So, we have four group of the data and one model in each group. In conclusions, Kalman Filter method is better than ARIMA model for rainfall forecasting in each group. It can be showed from error of Kalman Filter method that smaller than error of ARIMA model.

The development of a combined mathematical model to forecast the incidence of hepatitis E in Shanghai, China.

PubMed

Ren, Hong; Li, Jian; Yuan, Zheng-An; Hu, Jia-Yu; Yu, Yan; Lu, Yi-Han

2013-09-08

Sporadic hepatitis E has become an important public health concern in China. Accurate forecasting of the incidence of hepatitis E is needed to better plan future medical needs. Few mathematical models can be used because hepatitis E morbidity data has both linear and nonlinear patterns. We developed a combined mathematical model using an autoregressive integrated moving average model (ARIMA) and a back propagation neural network (BPNN) to forecast the incidence of hepatitis E. The morbidity data of hepatitis E in Shanghai from 2000 to 2012 were retrieved from the China Information System for Disease Control and Prevention. The ARIMA-BPNN combined model was trained with 144 months of morbidity data from January 2000 to December 2011, validated with 12 months of data January 2012 to December 2012, and then employed to forecast hepatitis E incidence January 2013 to December 2013 in Shanghai. Residual analysis, Root Mean Square Error (RMSE), normalized Bayesian Information Criterion (BIC), and stationary R square methods were used to compare the goodness-of-fit among ARIMA models. The Bayesian regularization back-propagation algorithm was used to train the network. The mean error rate (MER) was used to assess the validity of the combined model. A total of 7,489 hepatitis E cases was reported in Shanghai from 2000 to 2012. Goodness-of-fit (stationary R2=0.531, BIC= -4.768, Ljung-Box Q statistics=15.59, P=0.482) and parameter estimates were used to determine the best-fitting model as ARIMA (0,1,1)×(0,1,1)12. Predicted morbidity values in 2012 from best-fitting ARIMA model and actual morbidity data from 2000 to 2011 were used to further construct the combined model. The MER of the ARIMA model and the ARIMA-BPNN combined model were 0.250 and 0.176, respectively. The forecasted incidence of hepatitis E in 2013 was 0.095 to 0.372 per 100,000 population. There was a seasonal variation with a peak during January-March and a nadir during August-October. Time series analysis suggested a seasonal pattern of hepatitis E morbidity in Shanghai, China. An ARIMA-BPNN combined model was used to fit the linear and nonlinear patterns of time series data, and accurately forecast hepatitis E infections.

Forecasting of particulate matter time series using wavelet analysis and wavelet-ARMA/ARIMA model in Taiyuan, China.

PubMed

Zhang, Hong; Zhang, Sheng; Wang, Ping; Qin, Yuzhe; Wang, Huifeng

2017-07-01

Particulate matter with aerodynamic diameter below 10 μm (PM 10 ) forecasting is difficult because of the uncertainties in describing the emission and meteorological fields. This paper proposed a wavelet-ARMA/ARIMA model to forecast the short-term series of the PM 10 concentrations. It was evaluated by experiments using a 10-year data set of daily PM 10 concentrations from 4 stations located in Taiyuan, China. The results indicated the following: (1) PM 10 concentrations of Taiyuan had a decreasing trend during 2005 to 2012 but increased in 2013. PM 10 concentrations had an obvious seasonal fluctuation related to coal-fired heating in winter and early spring. (2) Spatial differences among the four stations showed that the PM 10 concentrations in industrial and heavily trafficked areas were higher than those in residential and suburb areas. (3) Wavelet analysis revealed that the trend variation and the changes of the PM 10 concentration of Taiyuan were complicated. (4) The proposed wavelet-ARIMA model could be efficiently and successfully applied to the PM 10 forecasting field. Compared with the traditional ARMA/ARIMA methods, this wavelet-ARMA/ARIMA method could effectively reduce the forecasting error, improve the prediction accuracy, and realize multiple-time-scale prediction. Wavelet analysis can filter noisy signals and identify the variation trend and the fluctuation of the PM 10 time-series data. Wavelet decomposition and reconstruction reduce the nonstationarity of the PM 10 time-series data, and thus improve the accuracy of the prediction. This paper proposed a wavelet-ARMA/ARIMA model to forecast the PM 10 time series. Compared with the traditional ARMA/ARIMA method, this wavelet-ARMA/ARIMA method could effectively reduce the forecasting error, improve the prediction accuracy, and realize multiple-time-scale prediction. The proposed model could be efficiently and successfully applied to the PM 10 forecasting field.

Application of seasonal auto-regressive integrated moving average model in forecasting the incidence of hand-foot-mouth disease in Wuhan, China.

PubMed

Peng, Ying; Yu, Bin; Wang, Peng; Kong, De-Guang; Chen, Bang-Hua; Yang, Xiao-Bing

2017-12-01

Outbreaks of hand-foot-mouth disease (HFMD) have occurred many times and caused serious health burden in China since 2008. Application of modern information technology to prediction and early response can be helpful for efficient HFMD prevention and control. A seasonal auto-regressive integrated moving average (ARIMA) model for time series analysis was designed in this study. Eighty-four-month (from January 2009 to December 2015) retrospective data obtained from the Chinese Information System for Disease Prevention and Control were subjected to ARIMA modeling. The coefficient of determination (R 2 ), normalized Bayesian Information Criterion (BIC) and Q-test P value were used to evaluate the goodness-of-fit of constructed models. Subsequently, the best-fitted ARIMA model was applied to predict the expected incidence of HFMD from January 2016 to December 2016. The best-fitted seasonal ARIMA model was identified as (1,0,1)(0,1,1) 12 , with the largest coefficient of determination (R 2 =0.743) and lowest normalized BIC (BIC=3.645) value. The residuals of the model also showed non-significant autocorrelations (P Box-Ljung (Q) =0.299). The predictions by the optimum ARIMA model adequately captured the pattern in the data and exhibited two peaks of activity over the forecast interval, including a major peak during April to June, and again a light peak for September to November. The ARIMA model proposed in this study can forecast HFMD incidence trend effectively, which could provide useful support for future HFMD prevention and control in the study area. Besides, further observations should be added continually into the modeling data set, and parameters of the models should be adjusted accordingly.

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.

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).

Time series analysis of contaminant transport in the subsurface: applications to conservative tracer and engineered nanomaterials.

PubMed

Bai, Chunmei; Li, Yusong

2014-08-01

Accurately predicting the transport of contaminants in the field is subject to multiple sources of uncertainty due to the variability of geological settings, the complexity of field measurements, and the scarcity of data. Such uncertainties can be amplified when modeling some emerging contaminants, such as engineered nanomaterials, when a fundamental understanding of their fate and transport is lacking. Typical field work includes collecting concentration at a certain location for an extended period of time, or measuring the movement of plume for an extended period time, which would result in a time series of observation data. This work presents an effort to evaluate the possibility of applying time series analysis, particularly, autoregressive integrated moving average (ARIMA) models, to forecast contaminant transport and distribution in the subsurface environment. ARIMA modeling was first assessed in terms of its capability to forecast tracer transport at two field sites, which had different levels of heterogeneity. After that, this study evaluated the applicability of ARIMA modeling to predict the transport of engineered nanomaterials at field sites, including field measured data of nanoscale zero valent iron and (nZVI) and numerically generated data for the transport of nano-fullerene aggregates (nC60). This proof-of-concept effort demonstrates the possibility of applying ARIMA to predict the contaminant transport in the subsurface environment. Like many other statistical models, ARIMA modeling is only descriptive and not explanatory. The limitation and the challenge associated with applying ARIMA modeling to contaminant transport in the subsurface are also discussed. Copyright © 2014 Elsevier B.V. All rights reserved.

Time series analysis of contaminant transport in the subsurface: Applications to conservative tracer and engineered nanomaterials

NASA Astrophysics Data System (ADS)

Bai, Chunmei; Li, Yusong

2014-08-01

Accurately predicting the transport of contaminants in the field is subject to multiple sources of uncertainty due to the variability of geological settings, the complexity of field measurements, and the scarcity of data. Such uncertainties can be amplified when modeling some emerging contaminants, such as engineered nanomaterials, when a fundamental understanding of their fate and transport is lacking. Typical field work includes collecting concentration at a certain location for an extended period of time, or measuring the movement of plume for an extended period time, which would result in a time series of observation data. This work presents an effort to evaluate the possibility of applying time series analysis, particularly, autoregressive integrated moving average (ARIMA) models, to forecast contaminant transport and distribution in the subsurface environment. ARIMA modeling was first assessed in terms of its capability to forecast tracer transport at two field sites, which had different levels of heterogeneity. After that, this study evaluated the applicability of ARIMA modeling to predict the transport of engineered nanomaterials at field sites, including field measured data of nanoscale zero valent iron and (nZVI) and numerically generated data for the transport of nano-fullerene aggregates (nC60). This proof-of-concept effort demonstrates the possibility of applying ARIMA to predict the contaminant transport in the subsurface environment. Like many other statistical models, ARIMA modeling is only descriptive and not explanatory. The limitation and the challenge associated with applying ARIMA modeling to contaminant transport in the subsurface are also discussed.

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%.

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.

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)…

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

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…

[Study on the epidemiological characteristics and incidence trend of scarlet fever in Shanghai, 2005-2012].

PubMed

Ren, Hong; Wang, Ye; Chen, Ming-liang; Yuan, Zheng-an; Li, Yan-ting; Huang, Pu; Hu, Jia-yu

2013-07-01

To systemically analyze the epidemiological characteristics, molecular markers of circulating group A Streptococcus (GAS) isolates and the incidence trend of scarlet fever in Shanghai from 2005 to 2012 as well as to explore the practice of GAS isolates surveillance program and the combined mathematical model in the early warning of scarlet fever. The morbidity series of scarlet fever were retrieved to analyze and fit the combined mathematical model which comprised an autoregressive integrated moving average (ARIMA) model and a neural network. GAS isolates surveillances programs were implemented on community healthy population, using the emm typing and superantigens detecting method in Shanghai during the epidemic period of scarlet fever in 2008, 2010 and 2012. The standardized prevalence of GAS isolates was estimated with the demographic data. From 2005 to 2012, there were a total of 9410 scarlet fever cases reported in Shanghai including local registered residents and immigrant population, showing that the distribution of patients as sporadic. The morbidity kept rising with seasonal and periodical variations and the peak was in 2011. The average morbidity was 6.012 per 100 000 persons. Morbidity in the the suburban was significantly higher than that in the urban areas. Children at 4 to 8 years old were easy to be involved. The mean error rate of single ARIMA model,ARIMA-GRNN and back propagation artificial neural network combined model were 0.268, 0.432 and 0.131 respectively. The predicted incidence of scarlet fever in 2013 would keep fluctuating within a narrow range from 0.446 to 3.467 per 100 000 persons. A total number of 4409 throat swab samples were collected through the GAS isolates surveillance programs in 2008, 2010 and 2012. The standardized prevalence of GAS isolates in each year were 0.000%, 0.000% and 1.092%. 18 GAS isolates were identified and 15 isolates (83.33%)belonged to emm 12.0. The morbidity of scarlet fever would continue to maintain an upward trend in Shanghai and the techniques used in GAS isolates surveillance program and in the combined mathematical model could be applied for the early warning system on scarlet fever.

Forecasting of primary energy consumption data in the United States: A comparison between ARIMA and Holter-Winters models

NASA Astrophysics Data System (ADS)

Rahman, A.; Ahmar, A. S.

2017-09-01

This research has a purpose to compare ARIMA Model and Holt-Winters Model based on MAE, RSS, MSE, and RMS criteria in predicting Primary Energy Consumption Total data in the US. The data from this research ranges from January 1973 to December 2016. This data will be processed by using R Software. Based on the results of data analysis that has been done, it is found that the model of Holt-Winters Additive type (MSE: 258350.1) is the most appropriate model in predicting Primary Energy Consumption Total data in the US. This model is more appropriate when compared with Holt-Winters Multiplicative type (MSE: 262260,4) and ARIMA Seasonal model (MSE: 723502,2).

Smoothing Strategies Combined with ARIMA and Neural Networks to Improve the Forecasting of Traffic Accidents

PubMed Central

Rodríguez, Nibaldo

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%. PMID:25243200

Monthly streamflow forecasting with auto-regressive integrated moving average

NASA Astrophysics Data System (ADS)

Nasir, Najah; Samsudin, Ruhaidah; Shabri, Ani

2017-09-01

Forecasting of streamflow is one of the many ways that can contribute to better decision making for water resource management. The auto-regressive integrated moving average (ARIMA) model was selected in this research for monthly streamflow forecasting with enhancement made by pre-processing the data using singular spectrum analysis (SSA). This study also proposed an extension of the SSA technique to include a step where clustering was performed on the eigenvector pairs before reconstruction of the time series. The monthly streamflow data of Sungai Muda at Jeniang, Sungai Muda at Jambatan Syed Omar and Sungai Ketil at Kuala Pegang was gathered from the Department of Irrigation and Drainage Malaysia. A ratio of 9:1 was used to divide the data into training and testing sets. The ARIMA, SSA-ARIMA and Clustered SSA-ARIMA models were all developed in R software. Results from the proposed model are then compared to a conventional auto-regressive integrated moving average model using the root-mean-square error and mean absolute error values. It was found that the proposed model can outperform the conventional model.

[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.

Correlation analysis of air pollutant index levels and dengue cases across five different zones in Selangor, Malaysia.

PubMed

Thiruchelvam, Loshini; Dass, Sarat C; Zaki, Rafdzah; Yahya, Abqariyah; Asirvadam, Vijanth S

2018-05-07

This study investigated the potential relationship between dengue cases and air quality - as measured by the Air Pollution Index (API) for five zones in the state of Selangor, Malaysia. Dengue case patterns can be learned using prediction models based on feedback (lagged terms). However, the question whether air quality affects dengue cases is still not thoroughly investigated based on such feedback models. This work developed dengue prediction models using the autoregressive integrated moving average (ARIMA) and ARIMA with an exogeneous variable (ARIMAX) time series methodologies with API as the exogeneous variable. The Box Jenkins approach based on maximum likelihood was used for analysis as it gives effective model estimates and prediction. Three stages of model comparison were carried out for each zone: first with ARIMA models without API, then ARIMAX models with API data from the API station for that zone and finally, ARIMAX models with API data from the zone and spatially neighbouring zones. Bayesian Information Criterion (BIC) gives goodness-of-fit versus parsimony comparisons between all elicited models. Our study found that ARIMA models, with the lowest BIC value, outperformed the rest in all five zones. The BIC values for the zone of Kuala Selangor were -800.66, -796.22, and -790.5229, respectively, for ARIMA only, ARIMAX with single API component and ARIMAX with API components from its zone and spatially neighbouring zones. Therefore, we concluded that API levels, either temporally for each zone or spatio- temporally based on neighbouring zones, do not have a significant effect on dengue cases.

Hybrid model for forecasting time series with trend, seasonal and salendar variation patterns

NASA Astrophysics Data System (ADS)

Suhartono; Rahayu, S. P.; Prastyo, D. D.; Wijayanti, D. G. P.; Juliyanto

2017-09-01

Most of the monthly time series data in economics and business in Indonesia and other Moslem countries not only contain trend and seasonal, but also affected by two types of calendar variation effects, i.e. the effect of the number of working days or trading and holiday effects. The purpose of this research is to develop a hybrid model or a combination of several forecasting models to predict time series that contain trend, seasonal and calendar variation patterns. This hybrid model is a combination of classical models (namely time series regression and ARIMA model) and/or modern methods (artificial intelligence method, i.e. Artificial Neural Networks). A simulation study was used to show that the proposed procedure for building the hybrid model could work well for forecasting time series with trend, seasonal and calendar variation patterns. Furthermore, the proposed hybrid model is applied for forecasting real data, i.e. monthly data about inflow and outflow of currency at Bank Indonesia. The results show that the hybrid model tend to provide more accurate forecasts than individual forecasting models. Moreover, this result is also in line with the third results of the M3 competition, i.e. the hybrid model on average provides a more accurate forecast than the individual model.

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.

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.

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

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.

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

Time Series Analysis for Forecasting Hospital Census: Application to the Neonatal Intensive Care Unit

PubMed Central

Hoover, Stephen; Jackson, Eric V.; Paul, David; Locke, Robert

2016-01-01

Summary Background Accurate prediction of future patient census in hospital units is essential for patient safety, health outcomes, and resource planning. Forecasting census in the Neonatal Intensive Care Unit (NICU) is particularly challenging due to limited ability to control the census and clinical trajectories. The fixed average census approach, using average census from previous year, is a forecasting alternative used in clinical practice, but has limitations due to census variations. Objective Our objectives are to: (i) analyze the daily NICU census at a single health care facility and develop census forecasting models, (ii) explore models with and without patient data characteristics obtained at the time of admission, and (iii) evaluate accuracy of the models compared with the fixed average census approach. Methods We used five years of retrospective daily NICU census data for model development (January 2008 – December 2012, N=1827 observations) and one year of data for validation (January – December 2013, N=365 observations). Best-fitting models of ARIMA and linear regression were applied to various 7-day prediction periods and compared using error statistics. Results The census showed a slightly increasing linear trend. Best fitting models included a non-seasonal model, ARIMA(1,0,0), seasonal ARIMA models, ARIMA(1,0,0)x(1,1,2)7 and ARIMA(2,1,4)x(1,1,2)14, as well as a seasonal linear regression model. Proposed forecasting models resulted on average in 36.49% improvement in forecasting accuracy compared with the fixed average census approach. Conclusions Time series models provide higher prediction accuracy under different census conditions compared with the fixed average census approach. Presented methodology is easily applicable in clinical practice, can be generalized to other care settings, support short- and long-term census forecasting, and inform staff resource planning. PMID:27437040

Time Series Analysis for Forecasting Hospital Census: Application to the Neonatal Intensive Care Unit.

PubMed

Capan, Muge; Hoover, Stephen; Jackson, Eric V; Paul, David; Locke, Robert

2016-01-01

Accurate prediction of future patient census in hospital units is essential for patient safety, health outcomes, and resource planning. Forecasting census in the Neonatal Intensive Care Unit (NICU) is particularly challenging due to limited ability to control the census and clinical trajectories. The fixed average census approach, using average census from previous year, is a forecasting alternative used in clinical practice, but has limitations due to census variations. Our objectives are to: (i) analyze the daily NICU census at a single health care facility and develop census forecasting models, (ii) explore models with and without patient data characteristics obtained at the time of admission, and (iii) evaluate accuracy of the models compared with the fixed average census approach. We used five years of retrospective daily NICU census data for model development (January 2008 - December 2012, N=1827 observations) and one year of data for validation (January - December 2013, N=365 observations). Best-fitting models of ARIMA and linear regression were applied to various 7-day prediction periods and compared using error statistics. The census showed a slightly increasing linear trend. Best fitting models included a non-seasonal model, ARIMA(1,0,0), seasonal ARIMA models, ARIMA(1,0,0)x(1,1,2)7 and ARIMA(2,1,4)x(1,1,2)14, as well as a seasonal linear regression model. Proposed forecasting models resulted on average in 36.49% improvement in forecasting accuracy compared with the fixed average census approach. Time series models provide higher prediction accuracy under different census conditions compared with the fixed average census approach. Presented methodology is easily applicable in clinical practice, can be generalized to other care settings, support short- and long-term census forecasting, and inform staff resource planning.

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.

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.

Effect of climate variables on cocoa black pod incidence in Sabah using ARIMAX model

NASA Astrophysics Data System (ADS)

Ling Sheng Chang, Albert; Ramba, Haya; Mohd. Jaaffar, Ahmad Kamil; Kim Phin, Chong; Chong Mun, Ho

2016-06-01

Cocoa black pod disease is one of the major diseases affecting the cocoa production in Malaysia and also around the world. Studies have shown that the climate variables have influenced the cocoa black pod disease incidence and it is important to quantify the black pod disease variation due to the effect of climate variables. Application of time series analysis especially auto-regressive moving average (ARIMA) model has been widely used in economics study and can be used to quantify the effect of climate variables on black pod incidence to forecast the right time to control the incidence. However, ARIMA model does not capture some turning points in cocoa black pod incidence. In order to improve forecasting performance, other explanatory variables such as climate variables should be included into ARIMA model as ARIMAX model. Therefore, this paper is to study the effect of climate variables on the cocoa black pod disease incidence using ARIMAX model. The findings of the study showed ARIMAX model using MA(1) and relative humidity at lag 7 days, RHt - 7 gave better R square value compared to ARIMA model using MA(1) which could be used to forecast the black pod incidence to assist the farmers determine timely application of fungicide spraying and culture practices to control the black pod incidence.

Multistep-Ahead Air Passengers Traffic Prediction with Hybrid ARIMA-SVMs Models

PubMed Central

Ming, Wei; Xiong, Tao

2014-01-01

The hybrid ARIMA-SVMs prediction models have been established recently, which take advantage of the unique strength of ARIMA and SVMs models in linear and nonlinear modeling, respectively. Built upon this hybrid ARIMA-SVMs models alike, this study goes further to extend them into the case of multistep-ahead prediction for air passengers traffic with the two most commonly used multistep-ahead prediction strategies, that is, iterated strategy and direct strategy. Additionally, the effectiveness of data preprocessing approaches, such as deseasonalization and detrending, is investigated and proofed along with the two strategies. Real data sets including four selected airlines' monthly series were collected to justify the effectiveness of the proposed approach. Empirical results demonstrate that the direct strategy performs better than iterative one in long term prediction case while iterative one performs better in the case of short term prediction. Furthermore, both deseasonalization and detrending can significantly improve the prediction accuracy for both strategies, indicating the necessity of data preprocessing. As such, this study contributes as a full reference to the planners from air transportation industries on how to tackle multistep-ahead prediction tasks in the implementation of either prediction strategy. PMID:24723814

Generating temporal model using climate variables for the prediction of dengue cases in Subang Jaya, Malaysia

PubMed Central

Dom, Nazri Che; Hassan, A Abu; Latif, Z Abd; Ismail, Rodziah

2013-01-01

Objective To develop a forecasting model for the incidence of dengue cases in Subang Jaya using time series analysis. Methods The model was performed using the Autoregressive Integrated Moving Average (ARIMA) based on data collected from 2005 to 2010. The fitted model was then used to predict dengue incidence for the year 2010 by extrapolating dengue patterns using three different approaches (i.e. 52, 13 and 4 weeks ahead). Finally cross correlation between dengue incidence and climate variable was computed over a range of lags in order to identify significant variables to be included as external regressor. Results The result of this study revealed that the ARIMA (2,0,0) (0,0,1)52 model developed, closely described the trends of dengue incidence and confirmed the existence of dengue fever cases in Subang Jaya for the year 2005 to 2010. The prediction per period of 4 weeks ahead for ARIMA (2,0,0)(0,0,1)52 was found to be best fit and consistent with the observed dengue incidence based on the training data from 2005 to 2010 (Root Mean Square Error=0.61). The predictive power of ARIMA (2,0,0) (0,0,1)52 is enhanced by the inclusion of climate variables as external regressor to forecast the dengue cases for the year 2010. Conclusions The ARIMA model with weekly variation is a useful tool for disease control and prevention program as it is able to effectively predict the number of dengue cases in Malaysia.

Analysis of Whole-Sky Imager Data to Determine the Validity of PCFLOS models

DTIC Science & Technology

1992-12-01

included in the data sample. 2-5 3.1. Data arrangement for a r x c contingency table ....................... 3-2 3.2. ARIMA models estimated for each...satellites. This model uses the multidimen- sional Boehm Sawtooth Wave Model to establish climatic probabilities through repetitive simula- tions of...analysis techniques to develop an ARIMAe model for each direction at the Columbia and Kirtland sites. Then, the models can be compared and analyzed to

Behavioral Correlates of System Operational Readiness (SOR): Summary of Workshop Proceedings.

DTIC Science & Technology

1983-10-01

1978) time series ARIMA models Use ARIMA models for (Box & Jenkins, 1976) interrupted time series Stage 7. Interpretation 7.1 Formatting and re...This report describes a 2-day conference called to explore the methodology required to develop a behavioral model of system operational readiness (SOR...Participants discussed (4) the behavioral variables that should be included in the model , (2) the system level measures that should be included, (3

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.

Estimating Pressure Reactivity Using Noninvasive Doppler-Based Systolic Flow Index.

PubMed

Zeiler, Frederick A; Smielewski, Peter; Donnelly, Joseph; Czosnyka, Marek; Menon, David K; Ercole, Ari

2018-04-05

The study objective was to derive models that estimate the pressure reactivity index (PRx) using the noninvasive transcranial Doppler (TCD) based systolic flow index (Sx_a) and mean flow index (Mx_a), both based on mean arterial pressure, in traumatic brain injury (TBI). Using a retrospective database of 347 patients with TBI with intracranial pressure and TCD time series recordings, we derived PRx, Sx_a, and Mx_a. We first derived the autocorrelative structure of PRx based on: (A) autoregressive integrative moving average (ARIMA) modeling in representative patients, and (B) within sequential linear mixed effects (LME) models with various embedded ARIMA error structures for PRx for the entire population. Finally, we performed sequential LME models with embedded PRx ARIMA modeling to find the best model for estimating PRx using Sx_a and Mx_a. Model adequacy was assessed via normally distributed residual density. Model superiority was assessed via Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), log likelihood (LL), and analysis of variance testing between models. The most appropriate ARIMA structure for PRx in this population was (2,0,2). This was applied in sequential LME modeling. Two models were superior (employing random effects in the independent variables and intercept): (A) PRx ∼ Sx_a, and (B) PRx ∼ Sx_a + Mx_a. Correlation between observed and estimated PRx with these two models was: (A) 0.794 (p < 0.0001, 95% confidence interval (CI) = 0.788-0.799), and (B) 0.814 (p < 0.0001, 95% CI = 0.809-0.819), with acceptable agreement on Bland-Altman analysis. Through using linear mixed effects modeling and accounting for the ARIMA structure of PRx, one can estimate PRx using noninvasive TCD-based indices. We have described our first attempts at such modeling and PRx estimation, establishing the strong link between two aspects of cerebral autoregulation: measures of cerebral blood flow and those of pulsatile cerebral blood volume. Further work is required to validate.

Perceptions and Efficacy of Flight Operational Quality Assurance (FOQA) Programs Among Small-scale Operators

DTIC Science & Technology

2012-01-01

regressive Integrated Moving Average ( ARIMA ) model for the data, eliminating the need to identify an appropriate model through trial and error alone...06 .11 13.67 16 .62 16 .14 .11 8.06 16 .95 * Based on the asymptotic chi-square approximation. 8 In general, ARIMA models address three...performance standards and measurement processes and a prevailing climate of organizational trust were important factors. Unfortunately, uneven

Near Real-Time Event Detection & Prediction Using Intelligent Software Agents

DTIC Science & Technology

2006-03-01

value was 0.06743. Multiple autoregressive integrated moving average ( ARIMA ) models were then build to see if the raw data, differenced data, or...slight improvement. The best adjusted r^2 value was found to be 0.1814. Successful results were not expected from linear or ARIMA -based modelling ...appear, 2005. [63] Mora-Lopez, L., Mora, J., Morales-Bueno, R., et al. Modelling time series of climatic parameters with probabilistic finite

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.

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.

Short-term forecasting of urban rail transit ridership based on ARIMA and wavelet decomposition

NASA Astrophysics Data System (ADS)

Wang, Xuemei; Zhang, Ning; Chen, Ying; Zhang, Yunlong

2018-05-01

Due to different functions and land use types, there are significant differences in ridership patterns among different urban rail transit stations. Considering the characteristics of different ridership and coping with the uncertainty, periodical and stochastic natures of short-term passenger flow, and this paper proposes a novel hybrid methodology that combines the autoregressive integrated moving average (ARIMA) model and wavelet decomposition, which has strong strengths in signal processing, to short-term ridership forecasting. The seasonal ARIMA is used to represent the relatively stable and regular ridership patterns while the wavelet decomposition is used to capture the stochastic or sometimes drastic changing characteristics of ridership patterns. The inclusion of wavelet decomposition and reconstruction provides the hybrid model with a unique strength in capturing sudden change in ridership patterns associated with certain rail stations. The case study is carried out by analyzing real ridership data of Metro Line 1 in Nanjing, China. The experimental results indicate that the hybrid method is superior to the individual ARIMA model for all ridership patterns, but particularly advantageous in predicting ridership at stations often associated with sudden pattern changes due to special events.

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 New Navigation Satellite Clock Bias Prediction Method Based on Modified Clock-bias Quadratic Polynomial Model

NASA Astrophysics Data System (ADS)

Wang, Y. P.; Lu, Z. P.; Sun, D. S.; Wang, N.

2016-01-01

In order to better express the characteristics of satellite clock bias (SCB) and improve SCB prediction precision, this paper proposed a new SCB prediction model which can take physical characteristics of space-borne atomic clock, the cyclic variation, and random part of SCB into consideration. First, the new model employs a quadratic polynomial model with periodic items to fit and extract the trend term and cyclic term of SCB; then based on the characteristics of fitting residuals, a time series ARIMA ~(Auto-Regressive Integrated Moving Average) model is used to model the residuals; eventually, the results from the two models are combined to obtain final SCB prediction values. At last, this paper uses precise SCB data from IGS (International GNSS Service) to conduct prediction tests, and the results show that the proposed model is effective and has better prediction performance compared with the quadratic polynomial model, grey model, and ARIMA model. In addition, the new method can also overcome the insufficiency of the ARIMA model in model recognition and order determination.

[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.

The Use of Computer-Assisted Identification of ARIMA Time-Series.

ERIC Educational Resources Information Center

Brown, Roger L.

This study was conducted to determine the effects of using various levels of tutorial statistical software for the tentative identification of nonseasonal ARIMA models, a statistical technique proposed by Box and Jenkins for the interpretation of time-series data. The Box-Jenkins approach is an iterative process encompassing several stages of…

Effects of Forecasts on the Revisions of Concurrent Seasonally Adjusted Data Using the X-11 Seasonal Adjustment Procedure.

ERIC Educational Resources Information Center

Bobbitt, Larry; Otto, Mark

Three Autoregressive Integrated Moving Averages (ARIMA) forecast procedures for Census Bureau X-11 concurrent seasonal adjustment were empirically tested. Forty time series from three Census Bureau economic divisions (business, construction, and industry) were analyzed. Forecasts were obtained from fitted seasonal ARIMA models augmented with…

Analysis of Container Yard Capacity In North TPK Using ARIMA Method

NASA Astrophysics Data System (ADS)

Sirajuddin; Cut Gebrina Hisbach, M.; Ekawati, Ratna; Ade Irman, SM

2018-03-01

North container terminal known as North TPK is container terminal located in Indonesia Port Corporation area serving domestic container loading and unloading. It has 1006 ground slots with a total capacity of 5,544 TEUs and the maximum throughput of containers is 539,616 TEUs / year. Container throughput in the North TPK is increasing year by year. In 2011-2012, the North TPK container throughput is 165,080 TEUs / year and in 2015-2016 has reached 213,147 TEUs / year. To avoid congestion, and prevent possible losses in the future, this paper will analyze the flow of containers and the level of Yard Occupation Ratio in the North TPK at Tanjung Priok Port. The method used is the Autoregressive Integrated Moving Average (ARIMA) Model. ARIMA is a model that completely ignores independent variables in making forecasting. ARIMA results show that in 2016-2017 the total throughput of containers reached 234,006 TEUs / year with field effectiveness of 43.4% and in 2017-2018 the total throughput of containers reached 249,417 TEUs / year with field effectiveness 46.2%.

Proceedings of the Conference on the Design of Experiments in Army Research Development and Testing (32nd)

DTIC Science & Technology

1987-06-01

number of series among the 63 which were identified as a particular ARIMA form and were "best" modeled by a particular technique. Figure 1 illustrates a...th time from xe’s. The integrbted autoregressive - moving average model , denoted by ARIMA (p,d,q) is a result of combining d-th differencing process...Experiments, (4) Data Analysis and Modeling , (5) Theory and Probablistic Inference, (6) Fuzzy Statistics, (7) Forecasting and Prediction, (8) Small Sample

[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.

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.

Rate of Oviposition by Culex Quinquefasciatus in San Antonio, Texas, During Three Years

DTIC Science & Technology

1988-09-01

autoregression and zero orders of integration and moving average ( ARIMA (l,O,O)). This model was chosen initially because rainfall ap- peared to...have no trend requiring integration and no obvious requirement for a moving aver- age component (i.e., no regular periodicity). This ARIMA model was...Say in both the northern and southern hem- ispheres exposes this species to a variety of climatic challenges to its survival. It is able to adjust

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.

Daily air quality index forecasting with hybrid models: A case in China.

PubMed

Zhu, Suling; Lian, Xiuyuan; Liu, Haixia; Hu, Jianming; Wang, Yuanyuan; Che, Jinxing

2017-12-01

Air quality is closely related to quality of life. Air pollution forecasting plays a vital role in air pollution warnings and controlling. However, it is difficult to attain accurate forecasts for air pollution indexes because the original data are non-stationary and chaotic. The existing forecasting methods, such as multiple linear models, autoregressive integrated moving average (ARIMA) and support vector regression (SVR), cannot fully capture the information from series of pollution indexes. Therefore, new effective techniques need to be proposed to forecast air pollution indexes. The main purpose of this research is to develop effective forecasting models for regional air quality indexes (AQI) to address the problems above and enhance forecasting accuracy. Therefore, two hybrid models (EMD-SVR-Hybrid and EMD-IMFs-Hybrid) are proposed to forecast AQI data. The main steps of the EMD-SVR-Hybrid model are as follows: the data preprocessing technique EMD (empirical mode decomposition) is utilized to sift the original AQI data to obtain one group of smoother IMFs (intrinsic mode functions) and a noise series, where the IMFs contain the important information (level, fluctuations and others) from the original AQI series. LS-SVR is applied to forecast the sum of the IMFs, and then, S-ARIMA (seasonal ARIMA) is employed to forecast the residual sequence of LS-SVR. In addition, EMD-IMFs-Hybrid first separately forecasts the IMFs via statistical models and sums the forecasting results of the IMFs as EMD-IMFs. Then, S-ARIMA is employed to forecast the residuals of EMD-IMFs. To certify the proposed hybrid model, AQI data from June 2014 to August 2015 collected from Xingtai in China are utilized as a test case to investigate the empirical research. In terms of some of the forecasting assessment measures, the AQI forecasting results of Xingtai show that the two proposed hybrid models are superior to ARIMA, SVR, GRNN, EMD-GRNN, Wavelet-GRNN and Wavelet-SVR. Therefore, the proposed hybrid models can be used as effective and simple tools for air pollution forecasting and warning as well as for management. Copyright © 2017 Elsevier Ltd. All rights reserved.

A Regional Analysis of Non-Methane Hydrocarbons And Meteorology of The Rural Southeast United States

DTIC Science & Technology

1996-01-01

Zt is an ARIMA time series. This is a typical regression model , except that it allows for autocorrelation in the error term Z. In this work, an ARMA...data=folder; var residual; run; II Statistical output of 1992 regression model on 1993 ozone data ARIMA Procedure Maximum Likelihood Estimation Approx...at each of the sites, and to show the effect of synoptic meteorology on high ozone by examining NOAA daily weather maps and climatic data

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.

Forecasting of monthly inflow and outflow currency using time series regression and ARIMAX: The Idul Fitri effect

NASA Astrophysics Data System (ADS)

Ahmad, Imam Safawi; Setiawan, Suhartono, Masun, Nunun Hilyatul

2015-12-01

Currency plays an important role in economic transactions of Indonesian society. In order to guarantee the availability of currency, Bank Indonesia needs to develop demand and supply planning of currency. The purpose of this study is to get model and predict inflow and outflow of currency in KPW BI Region IV (East Java) with ARIMA method, time series regression and ARIMAX. The data of monthly inflow and outflow is used of currency in KPW BI Surabaya, Malang, Kediri and Jember.The observation period starting from January 2003 to December 2014. Based on the smallest values of out-sample RMSE and SMAPE, ARIMA is the best model to predict the outflow of currency in KPW BI Surabaya and ARIMAX for KPW BI Malang, Kediri and Jember. The best forecasting model for inflow of currency in KPW BI Surabaya, Malang, Kediri and Jember chronologically as follows are calendar variation model, transfer function, ARIMA, and time series regression. These results indicates that the more complex models may not necessarily produce a more accurate forecast as the result of M3-Competition.

Development of the statistical ARIMA model: an application for predicting the upcoming of MJO index

NASA Astrophysics Data System (ADS)

Hermawan, Eddy; Nurani Ruchjana, Budi; Setiawan Abdullah, Atje; Gede Nyoman Mindra Jaya, I.; Berliana Sipayung, Sinta; Rustiana, Shailla

2017-10-01

This study is mainly concerned in development one of the most important equatorial atmospheric phenomena that we call as the Madden Julian Oscillation (MJO) which having strong impacts to the extreme rainfall anomalies over the Indonesian Maritime Continent (IMC). In this study, we focused to the big floods over Jakarta and surrounded area that suspecting caused by the impacts of MJO. We concentrated to develop the MJO index using the statistical model that we call as Box-Jenkis (ARIMA) ini 1996, 2002, and 2007, respectively. They are the RMM (Real Multivariate MJO) index as represented by RMM1 and RMM2, respectively. There are some steps to develop that model, starting from identification of data, estimated, determined model, before finally we applied that model for investigation some big floods that occurred at Jakarta in 1996, 2002, and 2007 respectively. We found the best of estimated model for the RMM1 and RMM2 prediction is ARIMA (2,1,2). Detailed steps how that model can be extracted and applying to predict the rainfall anomalies over Jakarta for 3 to 6 months later is discussed at this paper.

Evaluation of the effects of climate and man intervention on ground waters and their dependent ecosystems using time series analysis

NASA Astrophysics Data System (ADS)

Gemitzi, Alexandra; Stefanopoulos, Kyriakos

2011-06-01

SummaryGroundwaters and their dependent ecosystems are affected both by the meteorological conditions as well as from human interventions, mainly in the form of groundwater abstractions for irrigation needs. This work aims at investigating the quantitative effects of meteorological conditions and man intervention on groundwater resources and their dependent ecosystems. Various seasonal Auto-Regressive Integrated Moving Average (ARIMA) models with external predictor variables were used in order to model the influence of meteorological conditions and man intervention on the groundwater level time series. Initially, a seasonal ARIMA model that simulates the abstraction time series using as external predictor variable temperature ( T) was prepared. Thereafter, seasonal ARIMA models were developed in order to simulate groundwater level time series in 8 monitoring locations, using the appropriate predictor variables determined for each individual case. The spatial component was introduced through the use of Geographical Information Systems (GIS). Application of the proposed methodology took place in the Neon Sidirochorion alluvial aquifer (Northern Greece), for which a 7-year long time series (i.e., 2003-2010) of piezometric and groundwater abstraction data exists. According to the developed ARIMA models, three distinct groups of groundwater level time series exist; the first one proves to be dependent only on the meteorological parameters, the second group demonstrates a mixed dependence both on meteorological conditions and on human intervention, whereas the third group shows a clear influence from man intervention. Moreover, there is evidence that groundwater abstraction has affected an important protected ecosystem.

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.

Comparison of estimators for rolling samples using Forest Inventory and Analysis data

Treesearch

Devin S. Johnson; Michael S. Williams; Raymond L. Czaplewski

2003-01-01

The performance of three classes of weighted average estimators is studied for an annual inventory design similar to the Forest Inventory and Analysis program of the United States. The first class is based on an ARIMA(0,1,1) time series model. The equal weight, simple moving average is a member of this class. The second class is based on an ARIMA(0,2,2) time series...

Study of Various Types of Resonances within the Phonon Damping Model

NASA Astrophysics Data System (ADS)

Dang, Nguyen Dinh

2001-10-01

The main successes of the Phonon Damping Model (PDM)(N. Dinh Dang and A. Arima, Phys. Rev. Lett. 80), 4145 (1998); Nucl. Phys. A 636, 427 (1998); N. Dinh Dang, K. Tanabe, and A. Arima, Phys. Rev. C 58, 3374 (1998). are presented in the description of: 1) the giant dipole resonance (GDR) in highly excited nuclei, 2) the double giant dipole resonance (DGDR) and multiple phonon resonances, 3) the Gamow-Teller resonance (GTR), and 4) the damping of pygmy dipole resonance (PDR) in neutron-rich nuclei. The analyses of results of numerical calculations are discussed in comparison with the experimental systematics on i) the width and the shape of the GDR at finite temperature ^1,(N. Dinh Dang et al., Phys. Rev. C 61), 027302 (2000). and angular momentum(N. Dinh Dang, Nucl. Phys. A 687), 261c (2001). for tin isotopes , ii) the electromagnetic cross sections of DGDR for ^136Xe and ^208Pb on a lead target at relativistic energies(N. Dinh Dang, V. Kim Au, and A. Arima, Phys. Rev. Lett. 85), 1827 (2000)., iii) the strength function of GTR(N. Dinh Dang, T. Suzuki, and A. Arima, Preprint RIKEN-AF-NF 377 (2000), submitted.), and iv) the PDR in oxygen and calcium isotopes(N. Dinh Dang et al., Phys. Rev. C 63), 044302 (2001)..

Application of the Kishimoto-Tamura boson expansion theory to a single-j shell model

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

Li, C.T.; Pedrocchi, V.G.; Tamura, T.

1985-11-01

The boson expansion theory of Kishimoto and Tamura is applied to a single-j shell model. It is shown that this theory is quite accurate, giving results that agree very closely with those of the exact fermion calculations. The fast convergence of the boson expansion is also demonstrated. A critical discussion is then made of an earlier paper by Arima, in which he stated that the Kishimoto-Tamura theory gives rise to very poor numerical results. The source of the trouble encountered by Arima is unmasked.

Ensemble averaging and stacking of ARIMA and GSTAR model for rainfall forecasting

NASA Astrophysics Data System (ADS)

Anggraeni, D.; Kurnia, I. F.; Hadi, A. F.

2018-04-01

Unpredictable rainfall changes can affect human activities, such as in agriculture, aviation, shipping which depend on weather forecasts. Therefore, we need forecasting tools with high accuracy in predicting the rainfall in the future. This research focus on local forcasting of the rainfall at Jember in 2005 until 2016, from 77 rainfall stations. The rainfall here was not only related to the occurrence of the previous of its stations, but also related to others, it’s called the spatial effect. The aim of this research is to apply the GSTAR model, to determine whether there are some correlations of spatial effect between one to another stations. The GSTAR model is an expansion of the space-time model that combines the time-related effects, the locations (stations) in a time series effects, and also the location it self. The GSTAR model will also be compared to the ARIMA model that completely ignores the independent variables. The forcested value of the ARIMA and of the GSTAR models then being combined using the ensemble forecasting technique. The averaging and stacking method of ensemble forecasting method here provide us the best model with higher acuracy model that has the smaller RMSE (Root Mean Square Error) value. Finally, with the best model we can offer a better local rainfall forecasting in Jember for the future.

Nationwide survey of Arima syndrome: revised diagnostic criteria from epidemiological analysis.

PubMed

Itoh, Masayuki; Iwasaki, Yuji; Ohno, Kohsaku; Inoue, Takehiko; Hayashi, Masaharu; Ito, Shuichi; Matsuzaka, Tetsuo; Ide, Shuhei; Arima, Masataka

2014-05-01

We have never known any epidemiological study of Arima syndrome since it was first described in 1971. To investigate the number of Arima syndrome patients and clarify the clinical differences between Arima syndrome and Joubert syndrome, we performed the first nationwide survey of Arima syndrome, and herein report its results. Furthermore, we revised the diagnostic criteria for Arima syndrome. As a primary survey, we sent out self-administered questionnaires to most of the Japanese hospitals with a pediatric clinic, and facilities for persons with severe motor and intellectual disabilities, inquiring as to the number of patients having symptoms of Arima syndrome, including severe psychomotor delay, agenesis or hypoplasia of cerebellar vermis, renal dysfunction, visual dysfunction and with or without ptosis-like appearance. Next, as the second survey, we sent out detailed clinical questionnaires to the institutes having patients with two or more typical symptoms. The response rate of the primary survey was 72.7% of hospitals with pediatric clinic, 63.5% of national hospitals and 66.7% of municipal and private facilities. The number of patients with 5 typical symptoms was 13 and that with 2-4 symptoms was 32. The response rate of the secondary survey was 52% (23 patients). After reviewing clinical features of 23 patients, we identified 7 Arima syndrome patients and 16 Joubert syndrome patients. Progressive renal dysfunction was noticed in all Arima syndrome patients, but in 33% of those with Joubert syndrome. It is sometimes difficult to distinguish Arima syndrome from Joubert syndrome. Some clinicians described a patient with Joubert syndrome and its complications of visual dysfunction and renal dysfunction, whose current diagnosis was Arima syndrome. Thus, the diagnosis of the two syndromes may be confused. Here, we revised the diagnostic criteria for Arima syndrome. Copyright © 2013 The Japanese Society of Child Neurology. Published by Elsevier B.V. All rights reserved.

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.

Time Series Analysis and Forecasting of Wastewater Inflow into Bandar Tun Razak Sewage Treatment Plant in Selangor, Malaysia

NASA Astrophysics Data System (ADS)

Abunama, Taher; Othman, Faridah

2017-06-01

Analysing the fluctuations of wastewater inflow rates in sewage treatment plants (STPs) is essential to guarantee a sufficient treatment of wastewater before discharging it to the environment. The main objectives of this study are to statistically analyze and forecast the wastewater inflow rates into the Bandar Tun Razak STP in Kuala Lumpur, Malaysia. A time series analysis of three years’ weekly influent data (156weeks) has been conducted using the Auto-Regressive Integrated Moving Average (ARIMA) model. Various combinations of ARIMA orders (p, d, q) have been tried to select the most fitted model, which was utilized to forecast the wastewater inflow rates. The linear regression analysis was applied to testify the correlation between the observed and predicted influents. ARIMA (3, 1, 3) model was selected with the highest significance R-square and lowest normalized Bayesian Information Criterion (BIC) value, and accordingly the wastewater inflow rates were forecasted to additional 52weeks. The linear regression analysis between the observed and predicted values of the wastewater inflow rates showed a positive linear correlation with a coefficient of 0.831.

Time series analysis of reference crop evapotranspiration using soft computing techniques for Ganjam District, Odisha, India

NASA Astrophysics Data System (ADS)

Patra, S. R.

2017-12-01

Evapotranspiration (ET0) influences water resources and it is considered as a vital process in aridic hydrologic frameworks. It is one of the most important measure in finding the drought condition. Therefore, time series forecasting of evapotranspiration is very important in order to help the decision makers and water system mangers build up proper systems to sustain and manage water resources. Time series considers that -history repeats itself, hence by analysing the past values, better choices, or forecasts, can be carried out for the future. Ten years of ET0 data was used as a part of this study to make sure a satisfactory forecast of monthly values. In this study, three models: (ARIMA) mathematical model, artificial neural network model, support vector machine model are presented. These three models are used for forecasting monthly reference crop evapotranspiration based on ten years of past historical records (1991-2001) of measured evaporation at Ganjam region, Odisha, India without considering the climate data. The developed models will allow water resource managers to predict up to 12 months, making these predictions very useful to optimize the resources needed for effective water resources management. In this study multistep-ahead prediction is performed which is more complex and troublesome than onestep ahead. Our investigation proposed that nonlinear relationships may exist among the monthly indices, so that the ARIMA model might not be able to effectively extract the full relationship hidden in the historical data. Support vector machines are potentially helpful time series forecasting strategies on account of their strong nonlinear mapping capability and resistance to complexity in forecasting data. SVMs have great learning capability in time series modelling compared to ANN. For instance, the SVMs execute the structural risk minimization principle, which allows in better generalization as compared to neural networks that use the empirical risk minimization principle. The reliability of these computational models was analysed in light of simulation results and it was found out that SVM model produces better results among the three. The future research should be routed to extend the validation data set and to check the validity of our results on different areas with hybrid intelligence techniques.

Application Study of Comprehensive Forecasting Model Based on Entropy Weighting Method on Trend of PM2.5 Concentration in Guangzhou, China

PubMed Central

Liu, Dong-jun; Li, Li

2015-01-01

For the issue of haze-fog, PM2.5 is the main influence factor of haze-fog pollution in China. The trend of PM2.5 concentration was analyzed from a qualitative point of view based on mathematical models and simulation in this study. The comprehensive forecasting model (CFM) was developed based on the combination forecasting ideas. Autoregressive Integrated Moving Average Model (ARIMA), Artificial Neural Networks (ANNs) model and Exponential Smoothing Method (ESM) were used to predict the time series data of PM2.5 concentration. The results of the comprehensive forecasting model were obtained by combining the results of three methods based on the weights from the Entropy Weighting Method. The trend of PM2.5 concentration in Guangzhou China was quantitatively forecasted based on the comprehensive forecasting model. The results were compared with those of three single models, and PM2.5 concentration values in the next ten days were predicted. The comprehensive forecasting model balanced the deviation of each single prediction method, and had better applicability. It broadens a new prediction method for the air quality forecasting field. PMID:26110332

Application Study of Comprehensive Forecasting Model Based on Entropy Weighting Method on Trend of PM2.5 Concentration in Guangzhou, China.

PubMed

Liu, Dong-jun; Li, Li

2015-06-23

For the issue of haze-fog, PM2.5 is the main influence factor of haze-fog pollution in China. The trend of PM2.5 concentration was analyzed from a qualitative point of view based on mathematical models and simulation in this study. The comprehensive forecasting model (CFM) was developed based on the combination forecasting ideas. Autoregressive Integrated Moving Average Model (ARIMA), Artificial Neural Networks (ANNs) model and Exponential Smoothing Method (ESM) were used to predict the time series data of PM2.5 concentration. The results of the comprehensive forecasting model were obtained by combining the results of three methods based on the weights from the Entropy Weighting Method. The trend of PM2.5 concentration in Guangzhou China was quantitatively forecasted based on the comprehensive forecasting model. The results were compared with those of three single models, and PM2.5 concentration values in the next ten days were predicted. The comprehensive forecasting model balanced the deviation of each single prediction method, and had better applicability. It broadens a new prediction method for the air quality forecasting field.

Epidemiological Features and Forecast Model Analysis for the Morbidity of Influenza in Ningbo, China, 2006-2014.

PubMed

Wang, Chunli; Li, Yongdong; Feng, Wei; Liu, Kui; Zhang, Shu; Hu, Fengjiao; Jiao, Suli; Lao, Xuying; Ni, Hongxia; Xu, Guozhang

2017-05-25

This study aimed to identify circulating influenza virus strains and vulnerable population groups and investigate the distribution and seasonality of influenza viruses in Ningbo, China. Then, an autoregressive integrated moving average (ARIMA) model for prediction was established. Influenza surveillance data for 2006-2014 were obtained for cases of influenza-like illness (ILI) ( n = 129,528) from the municipal Centers for Disease Control and virus surveillance systems of Ningbo, China. The ARIMA model was proposed to predict the expected morbidity cases from January 2015 to December 2015. Of the 13,294 specimens, influenza virus was detected in 1148 (8.64%) samples, including 951 (82.84%) influenza type A and 197 (17.16%) influenza type B viruses; the influenza virus isolation rate was strongly correlated with the rate of ILI during the overall study period ( r = 0.20, p < 0.05). The ARIMA (1, 1, 1) (1, 1, 0) 12 model could be used to predict the ILI incidence in Ningbo. The seasonal pattern of influenza activity in Ningbo tended to peak during the rainy season and winter. Given those results, the model we established could effectively predict the trend of influenza-related morbidity, providing a methodological basis for future influenza monitoring and control strategies in the study area.

A comparison of classical and intelligent methods to detect potential thermal anomalies before the 11 August 2012 Varzeghan, Iran, earthquake (Mw = 6.4)

NASA Astrophysics Data System (ADS)

Akhoondzadeh, M.

2013-04-01

In this paper, a number of classical and intelligent methods, including interquartile, autoregressive integrated moving average (ARIMA), artificial neural network (ANN) and support vector machine (SVM), have been proposed to quantify potential thermal anomalies around the time of the 11 August 2012 Varzeghan, Iran, earthquake (Mw = 6.4). The duration of the data set, which is comprised of Aqua-MODIS land surface temperature (LST) night-time snapshot images, is 62 days. In order to quantify variations of LST data obtained from satellite images, the air temperature (AT) data derived from the meteorological station close to the earthquake epicenter has been taken into account. For the models examined here, results indicate the following: (i) ARIMA models, which are the most widely used in the time series community for short-term forecasting, are quickly and easily implemented, and can efficiently act through linear solutions. (ii) A multilayer perceptron (MLP) feed-forward neural network can be a suitable non-parametric method to detect the anomalous changes of a non-linear time series such as variations of LST. (iii) Since SVMs are often used due to their many advantages for classification and regression tasks, it can be shown that, if the difference between the predicted value using the SVM method and the observed value exceeds the pre-defined threshold value, then the observed value could be regarded as an anomaly. (iv) ANN and SVM methods could be powerful tools in modeling complex phenomena such as earthquake precursor time series where we may not know what the underlying data generating process is. There is good agreement in the results obtained from the different methods for quantifying potential anomalies in a given LST time series. This paper indicates that the detection of the potential thermal anomalies derive credibility from the overall efficiencies and potentialities of the four integrated methods.

Time series analysis of temporal trends in the pertussis incidence in Mainland China from 2005 to 2016.

PubMed

Zeng, Qianglin; Li, Dandan; Huang, Gui; Xia, Jin; Wang, Xiaoming; Zhang, Yamei; Tang, Wanping; Zhou, Hui

2016-08-31

Short-term forecast of pertussis incidence is helpful for advanced warning and planning resource needs for future epidemics. By utilizing the Auto-Regressive Integrated Moving Average (ARIMA) model and Exponential Smoothing (ETS) model as alterative models with R software, this paper analyzed data from Chinese Center for Disease Control and Prevention (China CDC) between January 2005 and June 2016. The ARIMA (0,1,0)(1,1,1)12 model (AICc = 1342.2 BIC = 1350.3) was selected as the best performing ARIMA model and the ETS (M,N,M) model (AICc = 1678.6, BIC = 1715.4) was selected as the best performing ETS model, and the ETS (M,N,M) model with the minimum RMSE was finally selected for in-sample-simulation and out-of-sample forecasting. Descriptive statistics showed that the reported number of pertussis cases by China CDC increased by 66.20% from 2005 (4058 cases) to 2015 (6744 cases). According to Hodrick-Prescott filter, there was an apparent cyclicity and seasonality in the pertussis reports. In out of sample forecasting, the model forecasted a relatively high incidence cases in 2016, which predicates an increasing risk of ongoing pertussis resurgence in the near future. In this regard, the ETS model would be a useful tool in simulating and forecasting the incidence of pertussis, and helping decision makers to take efficient decisions based on the advanced warning of disease incidence.

Middle and long-term prediction of UT1-UTC based on combination of Gray Model and Autoregressive Integrated Moving Average

NASA Astrophysics Data System (ADS)

Jia, Song; Xu, Tian-he; Sun, Zhang-zhen; Li, Jia-jing

2017-02-01

UT1-UTC is an important part of the Earth Orientation Parameters (EOP). The high-precision predictions of UT1-UTC play a key role in practical applications of deep space exploration, spacecraft tracking and satellite navigation and positioning. In this paper, a new prediction method with combination of Gray Model (GM(1, 1)) and Autoregressive Integrated Moving Average (ARIMA) is developed. The main idea is as following. Firstly, the UT1-UTC data are preprocessed by removing the leap second and Earth's zonal harmonic tidal to get UT1R-TAI data. Periodic terms are estimated and removed by the least square to get UT2R-TAI. Then the linear terms of UT2R-TAI data are modeled by the GM(1, 1), and the residual terms are modeled by the ARIMA. Finally, the UT2R-TAI prediction can be performed based on the combined model of GM(1, 1) and ARIMA, and the UT1-UTC predictions are obtained by adding the corresponding periodic terms, leap second correction and the Earth's zonal harmonic tidal correction. The results show that the proposed model can be used to predict UT1-UTC effectively with higher middle and long-term (from 32 to 360 days) accuracy than those of LS + AR, LS + MAR and WLS + MAR.

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.

Forecasting Natural Rubber Price In Malaysia Using Arima

NASA Astrophysics Data System (ADS)

Zahari, Fatin Z.; Khalid, Kamil; Roslan, Rozaini; Sufahani, Suliadi; Mohamad, Mahathir; Saifullah Rusiman, Mohd; Ali, Maselan

2018-04-01

This paper contains introduction, materials and methods, results and discussions, conclusions and references. Based on the title mentioned, high volatility of the price of natural rubber nowadays will give the significant risk to the producers, traders, consumers, and others parties involved in the production of natural rubber. To help them in making decisions, forecasting is needed to predict the price of natural rubber. The main objective of the research is to forecast the upcoming price of natural rubber by using the reliable statistical method. The data are gathered from Malaysia Rubber Board which the data are from January 2000 until December 2015. In this research, average monthly price of Standard Malaysia Rubber 20 (SMR20) will be forecast by using Box-Jenkins approach. Time series plot is used to determine the pattern of the data. The data have trend pattern which indicates the data is non-stationary data and the data need to be transformed. By using the Box-Jenkins method, the best fit model for the time series data is ARIMA (1, 1, 0) which this model satisfy all the criteria needed. Hence, ARIMA (1, 1, 0) is the best fitted model and the model will be used to forecast the average monthly price of Standard Malaysia Rubber 20 (SMR20) for twelve months ahead.

Time series analysis of malaria in Afghanistan: using ARIMA models to predict future trends in incidence.

PubMed

Anwar, Mohammad Y; Lewnard, Joseph A; Parikh, Sunil; Pitzer, Virginia E

2016-11-22

Malaria remains endemic in Afghanistan. National control and prevention strategies would be greatly enhanced through a better ability to forecast future trends in disease incidence. It is, therefore, of interest to develop a predictive tool for malaria patterns based on the current passive and affordable surveillance system in this resource-limited region. This study employs data from Ministry of Public Health monthly reports from January 2005 to September 2015. Malaria incidence in Afghanistan was forecasted using autoregressive integrated moving average (ARIMA) models in order to build a predictive tool for malaria surveillance. Environmental and climate data were incorporated to assess whether they improve predictive power of models. Two models were identified, each appropriate for different time horizons. For near-term forecasts, malaria incidence can be predicted based on the number of cases in the four previous months and 12 months prior (Model 1); for longer-term prediction, malaria incidence can be predicted using the rates 1 and 12 months prior (Model 2). Next, climate and environmental variables were incorporated to assess whether the predictive power of proposed models could be improved. Enhanced vegetation index was found to have increased the predictive accuracy of longer-term forecasts. Results indicate ARIMA models can be applied to forecast malaria patterns in Afghanistan, complementing current surveillance systems. The models provide a means to better understand malaria dynamics in a resource-limited context with minimal data input, yielding forecasts that can be used for public health planning at the national level.

ARIMA representation for daily solar irradiance and surface air temperature time series

NASA Astrophysics Data System (ADS)

Kärner, Olavi

2009-06-01

Autoregressive integrated moving average (ARIMA) models are used to compare long-range temporal variability of the total solar irradiance (TSI) at the top of the atmosphere (TOA) and surface air temperature series. The comparison shows that one and the same type of the model is applicable to represent the TSI and air temperature series. In terms of the model type surface air temperature imitates closely that for the TSI. This may mean that currently no other forcing to the climate system is capable to change the random walk type variability established by the varying activity of the rotating Sun. The result should inspire more detailed examination of the dependence of various climate series on short-range fluctuations of TSI.

Performance of time-series methods in forecasting the demand for red blood cell transfusion.

PubMed

Pereira, Arturo

2004-05-01

Planning the future blood collection efforts must be based on adequate forecasts of transfusion demand. In this study, univariate time-series methods were investigated for their performance in forecasting the monthly demand for RBCs at one tertiary-care, university hospital. Three time-series methods were investigated: autoregressive integrated moving average (ARIMA), the Holt-Winters family of exponential smoothing models, and one neural-network-based method. The time series consisted of the monthly demand for RBCs from January 1988 to December 2002 and was divided into two segments: the older one was used to fit or train the models, and the younger to test for the accuracy of predictions. Performance was compared across forecasting methods by calculating goodness-of-fit statistics, the percentage of months in which forecast-based supply would have met the RBC demand (coverage rate), and the outdate rate. The RBC transfusion series was best fitted by a seasonal ARIMA(0,1,1)(0,1,1)(12) model. Over 1-year time horizons, forecasts generated by ARIMA or exponential smoothing laid within the +/- 10 percent interval of the real RBC demand in 79 percent of months (62% in the case of neural networks). The coverage rate for the three methods was 89, 91, and 86 percent, respectively. Over 2-year time horizons, exponential smoothing largely outperformed the other methods. Predictions by exponential smoothing laid within the +/- 10 percent interval of real values in 75 percent of the 24 forecasted months, and the coverage rate was 87 percent. Over 1-year time horizons, predictions of RBC demand generated by ARIMA or exponential smoothing are accurate enough to be of help in the planning of blood collection efforts. For longer time horizons, exponential smoothing outperforms the other forecasting methods.

Analysis of time series for postal shipments in Regional VII East Java Indonesia

NASA Astrophysics Data System (ADS)

Kusrini, DE; Ulama, B. S. S.; Aridinanti, L.

2018-03-01

The change of number delivery goods through PT. Pos Regional VII East Java Indonesia indicates that the trend of increasing and decreasing the delivery of documents and non-documents in PT. Pos Regional VII East Java Indonesia is strongly influenced by conditions outside of PT. Pos Regional VII East Java Indonesia so that the prediction the number of document and non-documents requires a model that can accommodate it. Based on the time series plot monthly data fluctuations occur from 2013-2016 then the model is done using ARIMA or seasonal ARIMA and selected the best model based on the smallest AIC value. The results of data analysis about the number of shipments on each product sent through the Sub-Regional Postal Office VII East Java indicates that there are 5 post offices of 26 post offices entering the territory. The largest number of shipments is available on the PPB (Paket Pos Biasa is regular package shipment/non-document ) and SKH (Surat Kilat Khusus is Special Express Mail/document) products. The time series model generated is largely a Random walk model meaning that the number of shipment in the future is influenced by random effects that are difficult to predict. Some are AR and MA models, except for Express shipment products with Malang post office destination which has seasonal ARIMA model on lag 6 and 12. This means that the number of items in the following month is affected by the number of items in the previous 6 months.

Development of temporal modelling for forecasting and prediction of malaria infections using time-series and ARIMAX analyses: a case study in endemic districts of Bhutan.

PubMed

Wangdi, Kinley; Singhasivanon, Pratap; Silawan, Tassanee; Lawpoolsri, Saranath; White, Nicholas J; Kaewkungwal, Jaranit

2010-09-03

Malaria still remains a public health problem in some districts of Bhutan despite marked reduction of cases in last few years. To strengthen the country's prevention and control measures, this study was carried out to develop forecasting and prediction models of malaria incidence in the endemic districts of Bhutan using time series and ARIMAX. This study was carried out retrospectively using the monthly reported malaria cases from the health centres to Vector-borne Disease Control Programme (VDCP) and the meteorological data from Meteorological Unit, Department of Energy, Ministry of Economic Affairs. Time series analysis was performed on monthly malaria cases, from 1994 to 2008, in seven malaria endemic districts. The time series models derived from a multiplicative seasonal autoregressive integrated moving average (ARIMA) was deployed to identify the best model using data from 1994 to 2006. The best-fit model was selected for each individual district and for the overall endemic area was developed and the monthly cases from January to December 2009 and 2010 were forecasted. In developing the prediction model, the monthly reported malaria cases and the meteorological factors from 1996 to 2008 of the seven districts were analysed. The method of ARIMAX modelling was employed to determine predictors of malaria of the subsequent month. It was found that the ARIMA (p, d, q) (P, D, Q)s model (p and P representing the auto regressive and seasonal autoregressive; d and D representing the non-seasonal differences and seasonal differencing; and q and Q the moving average parameters and seasonal moving average parameters, respectively and s representing the length of the seasonal period) for the overall endemic districts was (2,1,1)(0,1,1)12; the modelling data from each district revealed two most common ARIMA models including (2,1,1)(0,1,1)12 and (1,1,1)(0,1,1)12. The forecasted monthly malaria cases from January to December 2009 and 2010 varied from 15 to 82 cases in 2009 and 67 to 149 cases in 2010, where population in 2009 was 285,375 and the expected population of 2010 to be 289,085. The ARIMAX model of monthly cases and climatic factors showed considerable variations among the different districts. In general, the mean maximum temperature lagged at one month was a strong positive predictor of an increased malaria cases for four districts. The monthly number of cases of the previous month was also a significant predictor in one district, whereas no variable could predict malaria cases for two districts. The ARIMA models of time-series analysis were useful in forecasting the number of cases in the endemic areas of Bhutan. There was no consistency in the predictors of malaria cases when using ARIMAX model with selected lag times and climatic predictors. The ARIMA forecasting models could be employed for planning and managing malaria prevention and control programme in Bhutan.

Prediction of biological sensors appearance with ARIMA models as a tool for Integrated Pest Management protocols.

PubMed

Fernández-González, María; Ramos-Valcárcel, David; Aira, María Jesús; Rodríguez-Rajo, Francisco Javier

2016-01-01

Powdery mildew caused by Uncinula necator and Downy mildew produced by Plasmopara viticola are the most common diseases in the North-West Spain vineyards. Knowledge of airborne spore concentrations could be a useful tool in the Integrated Pest Management protocols in order to reduce the number of pesticide treatments, applied only when there is a real risk of infection. The study was carried out in a vineyard of the D. O. Ribeiro, in the North-West Spain, during the grapevine active period 2004-2012. A Hirts-type volumetric spore-trap was used for the aerobiological monitoring. During the study period the annual total U. necator spores amount ranged from the 578 spores registered in 2007 to the 4,145 spores sampled during 2008. The highest annual total P. viticola spores quantity was observed in 2010 (1,548 spores) and the lowest in 2005 (210 spores). In order to forecast the concentration of fungal spores, ARIMA models were elaborated. The most accurate models were an ARIMA (3.1.3) for U. necator and (1.0.3) for P. viticola. The possibility to forecast the spore presence 72 hours in advance open an important horizon for optimizing the organization of the harvest processes in the vineyard.

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.

Performance evaluation of ionospheric time delay forecasting models using GPS observations at a low-latitude station

NASA Astrophysics Data System (ADS)

Sivavaraprasad, G.; Venkata Ratnam, D.

2017-07-01

Ionospheric delay is one of the major atmospheric effects on the performance of satellite-based radio navigation systems. It limits the accuracy and availability of Global Positioning System (GPS) measurements, related to critical societal and safety applications. The temporal and spatial gradients of ionospheric total electron content (TEC) are driven by several unknown priori geophysical conditions and solar-terrestrial phenomena. Thereby, the prediction of ionospheric delay is challenging especially over Indian sub-continent. Therefore, an appropriate short/long-term ionospheric delay forecasting model is necessary. Hence, the intent of this paper is to forecast ionospheric delays by considering day to day, monthly and seasonal ionospheric TEC variations. GPS-TEC data (January 2013-December 2013) is extracted from a multi frequency GPS receiver established at K L University, Vaddeswaram, Guntur station (geographic: 16.37°N, 80.37°E; geomagnetic: 7.44°N, 153.75°E), India. An evaluation, in terms of forecasting capabilities, of three ionospheric time delay models - an Auto Regressive Moving Average (ARMA) model, Auto Regressive Integrated Moving Average (ARIMA) model, and a Holt-Winter's model is presented. The performances of these models are evaluated through error measurement analysis during both geomagnetic quiet and disturbed days. It is found that, ARMA model is effectively forecasting the ionospheric delay with an accuracy of 82-94%, which is 10% more superior to ARIMA and Holt-Winter's models. Moreover, the modeled VTEC derived from International Reference Ionosphere, IRI (IRI-2012) model and new global TEC model, Neustrelitz TEC Model (NTCM-GL) have compared with forecasted VTEC values of ARMA, ARIMA and Holt-Winter's models during geomagnetic quiet days. The forecast results are indicating that ARMA model would be useful to set up an early warning system for ionospheric disturbances at low latitude regions.

A novel hybrid ensemble learning paradigm for tourism forecasting

NASA Astrophysics Data System (ADS)

Shabri, Ani

2015-02-01

In this paper, a hybrid forecasting model based on Empirical Mode Decomposition (EMD) and Group Method of Data Handling (GMDH) is proposed to forecast tourism demand. This methodology first decomposes the original visitor arrival series into several Intrinsic Model Function (IMFs) components and one residual component by EMD technique. Then, IMFs components and the residual components is forecasted respectively using GMDH model whose input variables are selected by using Partial Autocorrelation Function (PACF). The final forecasted result for tourism series is produced by aggregating all the forecasted results. For evaluating the performance of the proposed EMD-GMDH methodologies, the monthly data of tourist arrivals from Singapore to Malaysia are used as an illustrative example. Empirical results show that the proposed EMD-GMDH model outperforms the EMD-ARIMA as well as the GMDH and ARIMA (Autoregressive Integrated Moving Average) models without time series decomposition.

Modeling Philippine Stock Exchange Composite Index Using Time Series Analysis

NASA Astrophysics Data System (ADS)

Gayo, W. S.; Urrutia, J. D.; Temple, J. M. F.; Sandoval, J. R. D.; Sanglay, J. E. A.

2015-06-01

This study was conducted to develop a time series model of the Philippine Stock Exchange Composite Index and its volatility using the finite mixture of ARIMA model with conditional variance equations such as ARCH, GARCH, EG ARCH, TARCH and PARCH models. Also, the study aimed to find out the reason behind the behaviorof PSEi, that is, which of the economic variables - Consumer Price Index, crude oil price, foreign exchange rate, gold price, interest rate, money supply, price-earnings ratio, Producers’ Price Index and terms of trade - can be used in projecting future values of PSEi and this was examined using Granger Causality Test. The findings showed that the best time series model for Philippine Stock Exchange Composite index is ARIMA(1,1,5) - ARCH(1). Also, Consumer Price Index, crude oil price and foreign exchange rate are factors concluded to Granger cause Philippine Stock Exchange Composite Index.

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.

Hospital daily outpatient visits forecasting using a combinatorial model based on ARIMA and SES models.

PubMed

Luo, Li; Luo, Le; Zhang, Xinli; He, Xiaoli

2017-07-10

Accurate forecasting of hospital outpatient visits is beneficial for the reasonable planning and allocation of healthcare resource to meet the medical demands. In terms of the multiple attributes of daily outpatient visits, such as randomness, cyclicity and trend, time series methods, ARIMA, can be a good choice for outpatient visits forecasting. On the other hand, the hospital outpatient visits are also affected by the doctors' scheduling and the effects are not pure random. Thinking about the impure specialty, this paper presents a new forecasting model that takes cyclicity and the day of the week effect into consideration. We formulate a seasonal ARIMA (SARIMA) model on a daily time series and then a single exponential smoothing (SES) model on the day of the week time series, and finally establish a combinatorial model by modifying them. The models are applied to 1 year of daily visits data of urban outpatients in two internal medicine departments of a large hospital in Chengdu, for forecasting the daily outpatient visits about 1 week ahead. The proposed model is applied to forecast the cross-sectional data for 7 consecutive days of daily outpatient visits over an 8-weeks period based on 43 weeks of observation data during 1 year. The results show that the two single traditional models and the combinatorial model are simplicity of implementation and low computational intensiveness, whilst being appropriate for short-term forecast horizons. Furthermore, the combinatorial model can capture the comprehensive features of the time series data better. Combinatorial model can achieve better prediction performance than the single model, with lower residuals variance and small mean of residual errors which needs to be optimized deeply on the next research step.

Assessment of trend and seasonality in road accident data: an Iranian case study.

PubMed

Razzaghi, Alireza; Bahrampour, Abbas; Baneshi, Mohammad Reza; Zolala, Farzaneh

2013-06-01

Road traffic accidents and their related deaths have become a major concern, particularly in developing countries. Iran has adopted a series of policies and interventions to control the high number of accidents occurring over the past few years. In this study we used a time series model to understand the trend of accidents, and ascertain the viability of applying ARIMA models on data from Taybad city. This study is a cross-sectional study. We used data from accidents occurring in Taybad between 2007 and 2011. We obtained the data from the Ministry of Health (MOH) and used the time series method with a time lag of one month. After plotting the trend, non-stationary data in mean and variance were removed using Box-Cox transformation and a differencing method respectively. The ACF and PACF plots were used to control the stationary situation. The traffic accidents in our study had an increasing trend over the five years of study. Based on ACF and PACF plots gained after applying Box-Cox transformation and differencing, data did not fit to a time series model. Therefore, neither ARIMA model nor seasonality were observed. Traffic accidents in Taybad have an upward trend. In addition, we expected either the AR model, MA model or ARIMA model to have a seasonal trend, yet this was not observed in this analysis. Several reasons may have contributed to this situation, such as uncertainty of the quality of data, weather changes, and behavioural factors that are not taken into account by time series analysis.

Forecasting models for flow and total dissolved solids in Karoun river-Iran

NASA Astrophysics Data System (ADS)

Salmani, Mohammad Hassan; Salmani Jajaei, Efat

2016-04-01

Water quality is one of the most important factors contributing to a healthy life. From the water quality management point of view, TDS (total dissolved solids) is the most important factor and many water developing plans have been implemented in recognition of this factor. However, these plans have not been perfect and very successful in overcoming the poor water quality problem, so there are a good volume of related studies in the literature. We study TDS and the water flow of the Karoun river in southwest Iran. We collected the necessary time series data from the Harmaleh station located in the river. We present two Univariate Seasonal Autoregressive Integrated Movement Average (ARIMA) models to forecast TDS and water flow in this river. Then, we build up a Transfer Function (TF) model to formulate the TDS as a function of water flow volume. A performance comparison between the Seasonal ARIMA and the TF models are presented.

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

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.

Development of temporal modelling for forecasting and prediction of malaria infections using time-series and ARIMAX analyses: A case study in endemic districts of Bhutan

PubMed Central

2010-01-01

Background Malaria still remains a public health problem in some districts of Bhutan despite marked reduction of cases in last few years. To strengthen the country's prevention and control measures, this study was carried out to develop forecasting and prediction models of malaria incidence in the endemic districts of Bhutan using time series and ARIMAX. Methods This study was carried out retrospectively using the monthly reported malaria cases from the health centres to Vector-borne Disease Control Programme (VDCP) and the meteorological data from Meteorological Unit, Department of Energy, Ministry of Economic Affairs. Time series analysis was performed on monthly malaria cases, from 1994 to 2008, in seven malaria endemic districts. The time series models derived from a multiplicative seasonal autoregressive integrated moving average (ARIMA) was deployed to identify the best model using data from 1994 to 2006. The best-fit model was selected for each individual district and for the overall endemic area was developed and the monthly cases from January to December 2009 and 2010 were forecasted. In developing the prediction model, the monthly reported malaria cases and the meteorological factors from 1996 to 2008 of the seven districts were analysed. The method of ARIMAX modelling was employed to determine predictors of malaria of the subsequent month. Results It was found that the ARIMA (p, d, q) (P, D, Q)s model (p and P representing the auto regressive and seasonal autoregressive; d and D representing the non-seasonal differences and seasonal differencing; and q and Q the moving average parameters and seasonal moving average parameters, respectively and s representing the length of the seasonal period) for the overall endemic districts was (2,1,1)(0,1,1)12; the modelling data from each district revealed two most common ARIMA models including (2,1,1)(0,1,1)12 and (1,1,1)(0,1,1)12. The forecasted monthly malaria cases from January to December 2009 and 2010 varied from 15 to 82 cases in 2009 and 67 to 149 cases in 2010, where population in 2009 was 285,375 and the expected population of 2010 to be 289,085. The ARIMAX model of monthly cases and climatic factors showed considerable variations among the different districts. In general, the mean maximum temperature lagged at one month was a strong positive predictor of an increased malaria cases for four districts. The monthly number of cases of the previous month was also a significant predictor in one district, whereas no variable could predict malaria cases for two districts. Conclusions The ARIMA models of time-series analysis were useful in forecasting the number of cases in the endemic areas of Bhutan. There was no consistency in the predictors of malaria cases when using ARIMAX model with selected lag times and climatic predictors. The ARIMA forecasting models could be employed for planning and managing malaria prevention and control programme in Bhutan. PMID:20813066

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.

Potential barge transportation for inbound corn and grain

DOT National Transportation Integrated Search

1997-12-31

This research develops a model for estimating future barge and rail rates for decision making. The Box-Jenkins and the Regression Analysis with ARIMA errors forecasting methods were used to develop appropriate models for determining future rates. A s...

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.

What is Military Psychology? Symposium Proceedings.

DTIC Science & Technology

1980-07-01

AQ-AU90 b13 NAVAL POSTGRADUATE SCHOOL MONTEREY CA F/6 5/10 WHAT 1S MILITARY PSYCHOLOGY? SYMPOSIUM PROCEEDINGS.(U) lJUL 80 J K ARIMA , k R MACKIE, E A...PSYCHOLOGY? Symposium Proceedings. by - - - - . . . o Jameis K. Arima (Ed.) r.i~ ,~ ~ i~.. , P. / Approved for public release; distribution unlimited...prepared by: Jams K. Arima , Associate Professor Department of Administrative Sciences Reviewed by: Released by: ,(C. R. Jones Chairman W. M. Tolles

Seasonal trend analysis and ARIMA modeling of relative humidity and wind speed time series around Yamula Dam

NASA Astrophysics Data System (ADS)

Eymen, Abdurrahman; Köylü, Ümran

2018-02-01

Local climate change is determined by analysis of long-term recorded meteorological data. In the statistical analysis of the meteorological data, the Mann-Kendall rank test, which is one of the non-parametrical tests, has been used; on the other hand, for determining the power of the trend, Theil-Sen method has been used on the data obtained from 16 meteorological stations. The stations cover the provinces of Kayseri, Sivas, Yozgat, and Nevşehir in the Central Anatolia region of Turkey. Changes in land-use affect local climate. Dams are structures that cause major changes on the land. Yamula Dam is located 25 km northwest of Kayseri. The dam has huge water body which is approximately 85 km2. The mentioned tests have been used for detecting the presence of any positive or negative trend in meteorological data. The meteorological data in relation to the seasonal average, maximum, and minimum values of the relative humidity and seasonal average wind speed have been organized as time series and the tests have been conducted accordingly. As a result of these tests, the following have been identified: increase was observed in minimum relative humidity values in the spring, summer, and autumn seasons. As for the seasonal average wind speed, decrease was detected for nine stations in all seasons, whereas increase was observed in four stations. After the trend analysis, pre-dam mean relative humidity time series were modeled with Autoregressive Integrated Moving Averages (ARIMA) model which is statistical modeling tool. Post-dam relative humidity values were predicted by ARIMA models.

Stochastic Frontier Approach and Data Envelopment Analysis to Total Factor Productivity and Efficiency Measurement of Bangladeshi Rice

PubMed Central

Hossain, Md. Kamrul; Kamil, Anton Abdulbasah; Baten, Md. Azizul; Mustafa, Adli

2012-01-01

The objective of this paper is to apply the Translog Stochastic Frontier production model (SFA) and Data Envelopment Analysis (DEA) to estimate efficiencies over time and the Total Factor Productivity (TFP) growth rate for Bangladeshi rice crops (Aus, Aman and Boro) throughout the most recent data available comprising the period 1989–2008. Results indicate that technical efficiency was observed as higher for Boro among the three types of rice, but the overall technical efficiency of rice production was found around 50%. Although positive changes exist in TFP for the sample analyzed, the average growth rate of TFP for rice production was estimated at almost the same levels for both Translog SFA with half normal distribution and DEA. Estimated TFP from SFA is forecasted with ARIMA (2, 0, 0) model. ARIMA (1, 0, 0) model is used to forecast TFP of Aman from DEA estimation. PMID:23077500

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

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.

Forecasting dengue hemorrhagic fever cases using ARIMA model: a case study in Asahan district

NASA Astrophysics Data System (ADS)

Siregar, Fazidah A.; Makmur, Tri; Saprin, S.

2018-01-01

Time series analysis had been increasingly used to forecast the number of dengue hemorrhagic fever in many studies. Since no vaccine exist and poor public health infrastructure, predicting the occurrence of dengue hemorrhagic fever (DHF) is crucial. This study was conducted to determine trend and forecasting the occurrence of DHF in Asahan district, North Sumatera Province. Monthly reported dengue cases for the years 2012-2016 were obtained from the district health offices. A time series analysis was conducted by Autoregressive integrated moving average (ARIMA) modeling to forecast the occurrence of DHF. The results demonstrated that the reported DHF cases showed a seasonal variation. The SARIMA (1,0,0)(0,1,1)12 model was the best model and adequate for the data. The SARIMA model for DHF is necessary and could applied to predict the incidence of DHF in Asahan district and assist with design public health maesures to prevent and control the diseases.

A comparison of the stochastic and machine learning approaches in hydrologic time series forecasting

NASA Astrophysics Data System (ADS)

Kim, T.; Joo, K.; Seo, J.; Heo, J. H.

2016-12-01

Hydrologic time series forecasting is an essential task in water resources management and it becomes more difficult due to the complexity of runoff process. Traditional stochastic models such as ARIMA family has been used as a standard approach in time series modeling and forecasting of hydrological variables. Due to the nonlinearity in hydrologic time series data, machine learning approaches has been studied with the advantage of discovering relevant features in a nonlinear relation among variables. This study aims to compare the predictability between the traditional stochastic model and the machine learning approach. Seasonal ARIMA model was used as the traditional time series model, and Random Forest model which consists of decision tree and ensemble method using multiple predictor approach was applied as the machine learning approach. In the application, monthly inflow data from 1986 to 2015 of Chungju dam in South Korea were used for modeling and forecasting. In order to evaluate the performances of the used models, one step ahead and multi-step ahead forecasting was applied. Root mean squared error and mean absolute error of two models were compared.

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…

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

An open Markov chain scheme model for a credit consumption portfolio fed by ARIMA and SARMA processes

NASA Astrophysics Data System (ADS)

Esquível, Manuel L.; Fernandes, José Moniz; Guerreiro, Gracinda R.

2016-06-01

We introduce a schematic formalism for the time evolution of a random population entering some set of classes and such that each member of the population evolves among these classes according to a scheme based on a Markov chain model. We consider that the flow of incoming members is modeled by a time series and we detail the time series structure of the elements in each of the classes. We present a practical application to data from a credit portfolio of a Cape Verdian bank; after modeling the entering population in two different ways - namely as an ARIMA process and as a deterministic sigmoid type trend plus a SARMA process for the residues - we simulate the behavior of the population and compare the results. We get that the second method is more accurate in describing the behavior of the populations when compared to the observed values in a direct simulation of the Markov chain.

Regional stochastic generation of streamflows using an ARIMA (1,0,1) process and disaggregation

USGS Publications Warehouse

Armbruster, Jeffrey T.

1979-01-01

An ARIMA (1,0,1) model was calibrated and used to generate long annual flow sequences at three sites in the Juniata River basin, Pennsylvania. The model preserves the mean, variance, and cross correlations of the observed station data. In addition, it has a desirable blend of both high and low frequency characteristics and therefore is capable of preserving the Hurst coefficient, h. The generated annual flows are disaggregated into monthly sequences using a modification of the Valencia-Schaake model. The low-flow frequency and flow duration characteristics of the generated monthly flows, with length equal to the historical data, compare favorably with the historical data. Once the models were verified, 100-year sequences were generated and analyzed for their low flow characteristics. One-, three- and six- month low-flow frequencies at recurrence intervals greater than 10 years are generally found to be lower than flow computed from the historical flows. A method is proposed for synthesizing flows at ungaged sites. (Kosco-USGS)

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

Time series regression and ARIMAX for forecasting currency flow at Bank Indonesia in Sulawesi region

NASA Astrophysics Data System (ADS)

Suharsono, Agus; Suhartono, Masyitha, Aulia; Anuravega, Arum

2015-12-01

The purpose of the study is to forecast the outflow and inflow of currency at Indonesian Central Bank or Bank Indonesia (BI) in Sulawesi Region. The currency outflow and inflow data tend to have a trend pattern which is influenced by calendar variation effects. Therefore, this research focuses to apply some forecasting methods that could handle calendar variation effects, i.e. Time Series Regression (TSR) and ARIMAX models, and compare the forecast accuracy with ARIMA model. The best model is selected based on the lowest of Root Mean Squares Errors (RMSE) at out-sample dataset. The results show that ARIMA is the best model for forecasting the currency outflow and inflow at South Sulawesi. Whereas, the best model for forecasting the currency outflow at Central Sulawesi and Southeast Sulawesi, and for forecasting the currency inflow at South Sulawesi and North Sulawesi is TSR. Additionally, ARIMAX is the best model for forecasting the currency outflow at North Sulawesi. Hence, the results show that more complex models do not neccessary yield more accurate forecast than the simpler one.

Reconstruction of missing daily streamflow data using dynamic regression models

NASA Astrophysics Data System (ADS)

Tencaliec, Patricia; Favre, Anne-Catherine; Prieur, Clémentine; Mathevet, Thibault

2015-12-01

River discharge is one of the most important quantities in hydrology. It provides fundamental records for water resources management and climate change monitoring. Even very short data-gaps in this information can cause extremely different analysis outputs. Therefore, reconstructing missing data of incomplete data sets is an important step regarding the performance of the environmental models, engineering, and research applications, thus it presents a great challenge. The objective of this paper is to introduce an effective technique for reconstructing missing daily discharge data when one has access to only daily streamflow data. The proposed procedure uses a combination of regression and autoregressive integrated moving average models (ARIMA) called dynamic regression model. This model uses the linear relationship between neighbor and correlated stations and then adjusts the residual term by fitting an ARIMA structure. Application of the model to eight daily streamflow data for the Durance river watershed showed that the model yields reliable estimates for the missing data in the time series. Simulation studies were also conducted to evaluate the performance of the procedure.

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

Using forecast modelling to evaluate treatment effects in single-group interrupted time series analysis.

PubMed

Linden, Ariel

2018-05-11

Interrupted time series analysis (ITSA) is an evaluation methodology in which a single treatment unit's outcome is studied serially over time and the intervention is expected to "interrupt" the level and/or trend of that outcome. ITSA is commonly evaluated using methods which may produce biased results if model assumptions are violated. In this paper, treatment effects are alternatively assessed by using forecasting methods to closely fit the preintervention observations and then forecast the post-intervention trend. A treatment effect may be inferred if the actual post-intervention observations diverge from the forecasts by some specified amount. The forecasting approach is demonstrated using the effect of California's Proposition 99 for reducing cigarette sales. Three forecast models are fit to the preintervention series-linear regression (REG), Holt-Winters (HW) non-seasonal smoothing, and autoregressive moving average (ARIMA)-and forecasts are generated into the post-intervention period. The actual observations are then compared with the forecasts to assess intervention effects. The preintervention data were fit best by HW, followed closely by ARIMA. REG fit the data poorly. The actual post-intervention observations were above the forecasts in HW and ARIMA, suggesting no intervention effect, but below the forecasts in the REG (suggesting a treatment effect), thereby raising doubts about any definitive conclusion of a treatment effect. In a single-group ITSA, treatment effects are likely to be biased if the model is misspecified. Therefore, evaluators should consider using forecast models to accurately fit the preintervention data and generate plausible counterfactual forecasts, thereby improving causal inference of treatment effects in single-group ITSA studies. © 2018 John Wiley & Sons, Ltd.

Comparison of time series models for predicting campylobacteriosis risk in New Zealand.

PubMed

Al-Sakkaf, A; Jones, G

2014-05-01

Predicting campylobacteriosis cases is a matter of considerable concern in New Zealand, after the number of the notified cases was the highest among the developed countries in 2006. Thus, there is a need to develop a model or a tool to predict accurately the number of campylobacteriosis cases as the Microbial Risk Assessment Model used to predict the number of campylobacteriosis cases failed to predict accurately the number of actual cases. We explore the appropriateness of classical time series modelling approaches for predicting campylobacteriosis. Finding the most appropriate time series model for New Zealand data has additional practical considerations given a possible structural change, that is, a specific and sudden change in response to the implemented interventions. A univariate methodological approach was used to predict monthly disease cases using New Zealand surveillance data of campylobacteriosis incidence from 1998 to 2009. The data from the years 1998 to 2008 were used to model the time series with the year 2009 held out of the data set for model validation. The best two models were then fitted to the full 1998-2009 data and used to predict for each month of 2010. The Holt-Winters (multiplicative) and ARIMA (additive) intervention models were considered the best models for predicting campylobacteriosis in New Zealand. It was noticed that the prediction by an additive ARIMA with intervention was slightly better than the prediction by a Holt-Winter multiplicative method for the annual total in year 2010, the former predicting only 23 cases less than the actual reported cases. It is confirmed that classical time series techniques such as ARIMA with intervention and Holt-Winters can provide a good prediction performance for campylobacteriosis risk in New Zealand. The results reported by this study are useful to the New Zealand Health and Safety Authority's efforts in addressing the problem of the campylobacteriosis epidemic. © 2013 Blackwell Verlag GmbH.

A Comparison of Alternative Approaches to the Analysis of Interrupted Time-Series.

ERIC Educational Resources Information Center

Harrop, John W.; Velicer, Wayne F.

1985-01-01

Computer generated data representative of 16 Auto Regressive Integrated Moving Averages (ARIMA) models were used to compare the results of interrupted time-series analysis using: (1) the known model identification, (2) an assumed (l,0,0) model, and (3) an assumed (3,0,0) model as an approximation to the General Transformation approach. (Author/BW)

Compensated Box-Jenkins transfer function for short term load forecast

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

Breipohl, A.; Yu, Z.; Lee, F.N.

In the past years, the Box-Jenkins ARIMA method and the Box-Jenkins transfer function method (BJTF) have been among the most commonly used methods for short term electrical load forecasting. But when there exists a sudden change in the temperature, both methods tend to exhibit larger errors in the forecast. This paper demonstrates that the load forecasting errors resulting from either the BJ ARIMA model or the BJTF model are not simply white noise, but rather well-patterned noise, and the patterns in the noise can be used to improve the forecasts. Thus a compensated Box-Jenkins transfer method (CBJTF) is proposed tomore » improve the accuracy of the load prediction. Some case studies have been made which result in about a 14-33% reduction of the root mean square (RMS) errors of the forecasts, depending on the compensation time period as well as the compensation method used.« less

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…

A stepwise model to predict monthly streamflow

NASA Astrophysics Data System (ADS)

Mahmood Al-Juboori, Anas; Guven, Aytac

2016-12-01

In this study, a stepwise model empowered with genetic programming is developed to predict the monthly flows of Hurman River in Turkey and Diyalah and Lesser Zab Rivers in Iraq. The model divides the monthly flow data to twelve intervals representing the number of months in a year. The flow of a month, t is considered as a function of the antecedent month's flow (t - 1) and it is predicted by multiplying the antecedent monthly flow by a constant value called K. The optimum value of K is obtained by a stepwise procedure which employs Gene Expression Programming (GEP) and Nonlinear Generalized Reduced Gradient Optimization (NGRGO) as alternative to traditional nonlinear regression technique. The degree of determination and root mean squared error are used to evaluate the performance of the proposed models. The results of the proposed model are compared with the conventional Markovian and Auto Regressive Integrated Moving Average (ARIMA) models based on observed monthly flow data. The comparison results based on five different statistic measures show that the proposed stepwise model performed better than Markovian model and ARIMA model. The R2 values of the proposed model range between 0.81 and 0.92 for the three rivers in this study.

Inter-comparison of time series models of lake levels predicted by several modeling strategies

NASA Astrophysics Data System (ADS)

Khatibi, R.; Ghorbani, M. A.; Naghipour, L.; Jothiprakash, V.; Fathima, T. A.; Fazelifard, M. H.

2014-04-01

Five modeling strategies are employed to analyze water level time series of six lakes with different physical characteristics such as shape, size, altitude and range of variations. The models comprise chaos theory, Auto-Regressive Integrated Moving Average (ARIMA) - treated for seasonality and hence SARIMA, Artificial Neural Networks (ANN), Gene Expression Programming (GEP) and Multiple Linear Regression (MLR). Each is formulated on a different premise with different underlying assumptions. Chaos theory is elaborated in a greater detail as it is customary to identify the existence of chaotic signals by a number of techniques (e.g. average mutual information and false nearest neighbors) and future values are predicted using the Nonlinear Local Prediction (NLP) technique. This paper takes a critical view of past inter-comparison studies seeking a superior performance, against which it is reported that (i) the performances of all five modeling strategies vary from good to poor, hampering the recommendation of a clear-cut predictive model; (ii) the performances of the datasets of two cases are consistently better with all five modeling strategies; (iii) in other cases, their performances are poor but the results can still be fit-for-purpose; (iv) the simultaneous good performances of NLP and SARIMA pull their underlying assumptions to different ends, which cannot be reconciled. A number of arguments are presented including the culture of pluralism, according to which the various modeling strategies facilitate an insight into the data from different vantages.

Application of the backstepping method to the prediction of increase or decrease of infected population.

PubMed

Kuniya, Toshikazu; Sano, Hideki

2016-05-10

In mathematical epidemiology, age-structured epidemic models have usually been formulated as the boundary-value problems of the partial differential equations. On the other hand, in engineering, the backstepping method has recently been developed and widely studied by many authors. Using the backstepping method, we obtained a boundary feedback control which plays the role of the threshold criteria for the prediction of increase or decrease of newly infected population. Under an assumption that the period of infectiousness is same for all infected individuals (that is, the recovery rate is given by the Dirac delta function multiplied by a sufficiently large positive constant), the prediction method is simplified to the comparison of the numbers of reported cases at the current and previous time steps. Our prediction method was applied to the reported cases per sentinel of influenza in Japan from 2006 to 2015 and its accuracy was 0.81 (404 correct predictions to the total 500 predictions). It was higher than that of the ARIMA models with different orders of the autoregressive part, differencing and moving-average process. In addition, a proposed method for the estimation of the number of reported cases, which is consistent with our prediction method, was better than that of the best-fitted ARIMA model ARIMA(1,1,0) in the sense of mean square error. Our prediction method based on the backstepping method can be simplified to the comparison of the numbers of reported cases of the current and previous time steps. In spite of its simplicity, it can provide a good prediction for the spread of influenza in Japan.

Analysis and prediction of rainfall trends over Bangladesh using Mann-Kendall, Spearman's rho tests and ARIMA model

NASA Astrophysics Data System (ADS)

Rahman, Mohammad Atiqur; Yunsheng, Lou; Sultana, Nahid

2017-08-01

In this study, 60-year monthly rainfall data of Bangladesh were analysed to detect trends. Modified Mann-Kendall, Spearman's rho tests and Sen's slope estimators were applied to find the long-term annual, dry season and monthly trends. Sequential Mann-Kendall analysis was applied to detect the potential trend turning points. Spatial variations of the trends were examined using inverse distance weighting (IDW) interpolation. AutoRegressive integrated moving average (ARIMA) model was used for the country mean rainfall and for other two stations data which depicted the highest and the lowest trend in the Mann-Kendall and Spearman's rho tests. Results showed that there is no significant trend in annual rainfall pattern except increasing trends for Cox's Bazar, Khulna, Satkhira and decreasing trend for Srimagal areas. For the dry season, only Bogra area represented significant decreasing trend. Long-term monthly trends demonstrated a mixed pattern; both negative and positive changes were found from February to September. Comilla area showed a significant decreasing trend for consecutive 3 months while Rangpur and Khulna stations confirmed the significant rising trends for three different months in month-wise trends analysis. Rangpur station data gave a maximum increasing trend in April whereas a maximum decreasing trend was found in August for Comilla station. ARIMA models predict +3.26, +8.6 and -2.30 mm rainfall per year for the country, Cox's Bazar and Srimangal areas, respectively. However, all the test results and predictions revealed a good agreement among them in the study.

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.

Using social media to monitor mental health discussions - evidence from Twitter.

PubMed

McClellan, Chandler; Ali, Mir M; Mutter, Ryan; Kroutil, Larry; Landwehr, Justin

2017-05-01

Given the public health importance of communicating about mental illness and the growing use of social media to convey information, our goal was to develop an empirical model to identify periods of heightened interest in mental health topics on Twitter. We collected data on 176 million tweets from 2011 to 2014 with content related to depression or suicide. Using an autoregressive integrated moving average (ARIMA) data analysis, we identified deviations from predicted trends in communication about depression and suicide. Two types of heightened Twitter activity regarding depression or suicide were identified in 2014: expected increases in response to planned behavioral health events, and unexpected increases in response to unanticipated events. Tweet volume following expected increases went back to the predicted level more rapidly than the volume following unexpected events. Although ARIMA models have been used extensively in other fields, they have not been used widely in public health. Our findings indicate that our ARIMA model is valid for identifying periods of heightened activity on Twitter related to behavioral health. The model offers an objective and empirically based measure to identify periods of greater interest for timing the dissemination of credible information related to mental health. Spikes in tweet volume following a behavioral health event often last for less than 2 days. Individuals and organizations that want to disseminate behavioral health messages on Twitter in response to heightened periods of interest need to take this limited time frame into account. Published by Oxford University Press on behalf of the American Medical Informatics Association 2016. This work is written by US Government employees and is in the public domain in the United States.

Time Series Analysis of Onchocerciasis Data from Mexico: A Trend towards Elimination

PubMed Central

Pérez-Rodríguez, Miguel A.; Adeleke, Monsuru A.; Orozco-Algarra, María E.; Arrendondo-Jiménez, Juan I.; Guo, Xianwu

2013-01-01

Background In Latin America, there are 13 geographically isolated endemic foci distributed among Mexico, Guatemala, Colombia, Venezuela, Brazil and Ecuador. The communities of the three endemic foci found within Mexico have been receiving ivermectin treatment since 1989. In this study, we predicted the trend of occurrence of cases in Mexico by applying time series analysis to monthly onchocerciasis data reported by the Mexican Secretariat of Health between 1988 and 2011 using the software R. Results A total of 15,584 cases were reported in Mexico from 1988 to 2011. The data of onchocerciasis cases are mainly from the main endemic foci of Chiapas and Oaxaca. The last case in Oaxaca was reported in 1998, but new cases were reported in the Chiapas foci up to 2011. Time series analysis performed for the foci in Mexico showed a decreasing trend of the disease over time. The best-fitted models with the smallest Akaike Information Criterion (AIC) were Auto-Regressive Integrated Moving Average (ARIMA) models, which were used to predict the tendency of onchocerciasis cases for two years ahead. According to the ARIMA models predictions, the cases in very low number (below 1) are expected for the disease between 2012 and 2013 in Chiapas, the last endemic region in Mexico. Conclusion The endemic regions of Mexico evolved from high onchocerciasis-endemic states to the interruption of transmission due to the strategies followed by the MSH, based on treatment with ivermectin. The extremely low level of expected cases as predicted by ARIMA models for the next two years suggest that the onchocerciasis is being eliminated in Mexico. To our knowledge, it is the first study utilizing time series for predicting case dynamics of onchocerciasis, which could be used as a benchmark during monitoring and post-treatment surveillance. PMID:23459370

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…

Population-level administration of AlcoholEdu for college: an ARIMA time-series analysis.

PubMed

Wyatt, Todd M; Dejong, William; Dixon, Elizabeth

2013-08-01

Autoregressive integrated moving averages (ARIMA) is a powerful analytic tool for conducting interrupted time-series analysis, yet it is rarely used in studies of public health campaigns or programs. This study demonstrated the use of ARIMA to assess AlcoholEdu for College, an online alcohol education course for first-year students, and other health and safety programs introduced at a moderate-size public university in the South. From 1992 to 2009, the university administered annual Core Alcohol and Drug Surveys to samples of undergraduates (Ns = 498 to 1032). AlcoholEdu and other health and safety programs that began during the study period were assessed through a series of quasi-experimental ARIMA analyses. Implementation of AlcoholEdu in 2004 was significantly associated with substantial decreases in alcohol consumption and alcohol- or drug-related negative consequences. These improvements were sustained over time as succeeding first-year classes took the course. Previous studies have shown that AlcoholEdu has an initial positive effect on students' alcohol use and associated negative consequences. This investigation suggests that these positive changes may be sustainable over time through yearly implementation of the course with first-year students. ARIMA time-series analysis holds great promise for investigating the effect of program and policy interventions to address alcohol- and drug-related problems on campus.

A Modified LS+AR Model to Improve the Accuracy of the Short-term Polar Motion Prediction

NASA Astrophysics Data System (ADS)

Wang, Z. W.; Wang, Q. X.; Ding, Y. Q.; Zhang, J. J.; Liu, S. S.

2017-03-01

There are two problems of the LS (Least Squares)+AR (AutoRegressive) model in polar motion forecast: the inner residual value of LS fitting is reasonable, but the residual value of LS extrapolation is poor; and the LS fitting residual sequence is non-linear. It is unsuitable to establish an AR model for the residual sequence to be forecasted, based on the residual sequence before forecast epoch. In this paper, we make solution to those two problems with two steps. First, restrictions are added to the two endpoints of LS fitting data to fix them on the LS fitting curve. Therefore, the fitting values next to the two endpoints are very close to the observation values. Secondly, we select the interpolation residual sequence of an inward LS fitting curve, which has a similar variation trend as the LS extrapolation residual sequence, as the modeling object of AR for the residual forecast. Calculation examples show that this solution can effectively improve the short-term polar motion prediction accuracy by the LS+AR model. In addition, the comparison results of the forecast models of RLS (Robustified Least Squares)+AR, RLS+ARIMA (AutoRegressive Integrated Moving Average), and LS+ANN (Artificial Neural Network) confirm the feasibility and effectiveness of the solution for the polar motion forecast. The results, especially for the polar motion forecast in the 1-10 days, show that the forecast accuracy of the proposed model can reach the world level.

Modeling seasonal leptospirosis transmission and its association with rainfall and temperature in Thailand using time-series and ARIMAX analyses.

PubMed

Chadsuthi, Sudarat; Modchang, Charin; Lenbury, Yongwimon; Iamsirithaworn, Sopon; Triampo, Wannapong

2012-07-01

To study the number of leptospirosis cases in relations to the seasonal pattern, and its association with climate factors. Time series analysis was used to study the time variations in the number of leptospirosis cases. The Autoregressive Integrated Moving Average (ARIMA) model was used in data curve fitting and predicting the next leptospirosis cases. We found that the amount of rainfall was correlated to leptospirosis cases in both regions of interest, namely the northern and northeastern region of Thailand, while the temperature played a role in the northeastern region only. The use of multivariate ARIMA (ARIMAX) model showed that factoring in rainfall (with an 8 months lag) yields the best model for the northern region while the model, which factors in rainfall (with a 10 months lag) and temperature (with an 8 months lag) was the best for the northeastern region. The models are able to show the trend in leptospirosis cases and closely fit the recorded data in both regions. The models can also be used to predict the next seasonal peak quite accurately. Copyright © 2012 Hainan Medical College. Published by Elsevier B.V. All rights reserved.

Model variations in predicting incidence of Plasmodium falciparum malaria using 1998-2007 morbidity and meteorological data from south Ethiopia.

PubMed

Loha, Eskindir; Lindtjørn, Bernt

2010-06-16

Malaria transmission is complex and is believed to be associated with local climate changes. However, simple attempts to extrapolate malaria incidence rates from averaged regional meteorological conditions have proven unsuccessful. Therefore, the objective of this study was to determine if variations in specific meteorological factors are able to consistently predict P. falciparum malaria incidence at different locations in south Ethiopia. Retrospective data from 42 locations were collected including P. falciparum malaria incidence for the period of 1998-2007 and meteorological variables such as monthly rainfall (all locations), temperature (17 locations), and relative humidity (three locations). Thirty-five data sets qualified for the analysis. Ljung-Box Q statistics was used for model diagnosis, and R squared or stationary R squared was taken as goodness of fit measure. Time series modelling was carried out using Transfer Function (TF) models and univariate auto-regressive integrated moving average (ARIMA) when there was no significant predictor meteorological variable. Of 35 models, five were discarded because of the significant value of Ljung-Box Q statistics. Past P. falciparum malaria incidence alone (17 locations) or when coupled with meteorological variables (four locations) was able to predict P. falciparum malaria incidence within statistical significance. All seasonal AIRMA orders were from locations at altitudes above 1742 m. Monthly rainfall, minimum and maximum temperature was able to predict incidence at four, five and two locations, respectively. In contrast, relative humidity was not able to predict P. falciparum malaria incidence. The R squared values for the models ranged from 16% to 97%, with the exception of one model which had a negative value. Models with seasonal ARIMA orders were found to perform better. However, the models for predicting P. falciparum malaria incidence varied from location to location, and among lagged effects, data transformation forms, ARIMA and TF orders. This study describes P. falciparum malaria incidence models linked with meteorological data. Variability in the models was principally attributed to regional differences, and a single model was not found that fits all locations. Past P. falciparum malaria incidence appeared to be a superior predictor than meteorology. Future efforts in malaria modelling may benefit from inclusion of non-meteorological factors.

Application of Holt exponential smoothing and ARIMA method for data population in West Java

NASA Astrophysics Data System (ADS)

Supriatna, A.; Susanti, D.; Hertini, E.

2017-01-01

One method of time series that is often used to predict data that contains trend is Holt. Holt method using different parameters used in the original data which aims to smooth the trend value. In addition to Holt, ARIMA method can be used on a wide variety of data including data pattern containing a pattern trend. Data actual of population from 1998-2015 contains the trends so can be solved by Holt and ARIMA method to obtain the prediction value of some periods. The best method is measured by looking at the smallest MAPE and MAE error. The result using Holt method is 47.205.749 populations in 2016, 47.535.324 populations in 2017, and 48.041.672 populations in 2018, with MAPE error is 0,469744 and MAE error is 189.731. While the result using ARIMA method is 46.964.682 populations in 2016, 47.342.189 in 2017, and 47.899.696 in 2018, with MAPE error is 0,4380 and MAE is 176.626.

A Hybrid Neural Network Model for Sales Forecasting Based on ARIMA and Search Popularity of Article Titles.

PubMed

Omar, Hani; Hoang, Van Hai; Liu, Duen-Ren

2016-01-01

Enhancing sales and operations planning through forecasting analysis and business intelligence is demanded in many industries and enterprises. Publishing industries usually pick attractive titles and headlines for their stories to increase sales, since popular article titles and headlines can attract readers to buy magazines. In this paper, information retrieval techniques are adopted to extract words from article titles. The popularity measures of article titles are then analyzed by using the search indexes obtained from Google search engine. Backpropagation Neural Networks (BPNNs) have successfully been used to develop prediction models for sales forecasting. In this study, we propose a novel hybrid neural network model for sales forecasting based on the prediction result of time series forecasting and the popularity of article titles. The proposed model uses the historical sales data, popularity of article titles, and the prediction result of a time series, Autoregressive Integrated Moving Average (ARIMA) forecasting method to learn a BPNN-based forecasting model. Our proposed forecasting model is experimentally evaluated by comparing with conventional sales prediction techniques. The experimental result shows that our proposed forecasting method outperforms conventional techniques which do not consider the popularity of title words.

A Hybrid Neural Network Model for Sales Forecasting Based on ARIMA and Search Popularity of Article Titles

PubMed Central

Omar, Hani; Hoang, Van Hai; Liu, Duen-Ren

2016-01-01

Enhancing sales and operations planning through forecasting analysis and business intelligence is demanded in many industries and enterprises. Publishing industries usually pick attractive titles and headlines for their stories to increase sales, since popular article titles and headlines can attract readers to buy magazines. In this paper, information retrieval techniques are adopted to extract words from article titles. The popularity measures of article titles are then analyzed by using the search indexes obtained from Google search engine. Backpropagation Neural Networks (BPNNs) have successfully been used to develop prediction models for sales forecasting. In this study, we propose a novel hybrid neural network model for sales forecasting based on the prediction result of time series forecasting and the popularity of article titles. The proposed model uses the historical sales data, popularity of article titles, and the prediction result of a time series, Autoregressive Integrated Moving Average (ARIMA) forecasting method to learn a BPNN-based forecasting model. Our proposed forecasting model is experimentally evaluated by comparing with conventional sales prediction techniques. The experimental result shows that our proposed forecasting method outperforms conventional techniques which do not consider the popularity of title words. PMID:27313605

Stationarity test with a direct test for heteroskedasticity in exchange rate forecasting models

NASA Astrophysics Data System (ADS)

Khin, Aye Aye; Chau, Wong Hong; Seong, Lim Chee; Bin, Raymond Ling Leh; Teng, Kevin Low Lock

2017-05-01

Global economic has been decreasing in the recent years, manifested by the greater exchange rates volatility on international commodity market. This study attempts to analyze some prominent exchange rate forecasting models on Malaysian commodity trading: univariate ARIMA, ARCH and GARCH models in conjunction with stationarity test on residual diagnosis direct testing of heteroskedasticity. All forecasting models utilized the monthly data from 1990 to 2015. Given a total of 312 observations, the data used to forecast both short-term and long-term exchange rate. The forecasting power statistics suggested that the forecasting performance of ARIMA (1, 1, 1) model is more efficient than the ARCH (1) and GARCH (1, 1) models. For ex-post forecast, exchange rate was increased from RM 3.50 per USD in January 2015 to RM 4.47 per USD in December 2015 based on the baseline data. For short-term ex-ante forecast, the analysis results indicate a decrease in exchange rate on 2016 June (RM 4.27 per USD) as compared with 2015 December. A more appropriate forecasting method of exchange rate is vital to aid the decision-making process and planning on the sustainable commodities' production in the world economy.

Forecasting seeing and parameters of long-exposure images by means of ARIMA

NASA Astrophysics Data System (ADS)

Kornilov, Matwey V.

2016-02-01

Atmospheric turbulence is the one of the major limiting factors for ground-based astronomical observations. In this paper, the problem of short-term forecasting seeing is discussed. The real data that were obtained by atmospheric optical turbulence (OT) measurements above Mount Shatdzhatmaz in 2007-2013 have been analysed. Linear auto-regressive integrated moving average (ARIMA) models are used for the forecasting. A new procedure for forecasting the image characteristics of direct astronomical observations (central image intensity, full width at half maximum, radius encircling 80 % of the energy) has been proposed. Probability density functions of the forecast of these quantities are 1.5-2 times thinner than the respective unconditional probability density functions. Overall, this study found that the described technique could adequately describe temporal stochastic variations of the OT power.

Naive vs. Sophisticated Methods of Forecasting Public Library Circulations.

ERIC Educational Resources Information Center

Brooks, Terrence A.

1984-01-01

Two sophisticated--autoregressive integrated moving average (ARIMA), straight-line regression--and two naive--simple average, monthly average--forecasting techniques were used to forecast monthly circulation totals of 34 public libraries. Comparisons of forecasts and actual totals revealed that ARIMA and monthly average methods had smallest mean…

A time-series analysis of the relation between unemployment rate and hospital admission for acute myocardial infarction and stroke in Brazil over more than a decade.

PubMed

Katz, Marcelo; Bosworth, Hayden B; Lopes, Renato D; Dupre, Matthew E; Morita, Fernando; Pereira, Carolina; Franco, Fabio G M; Prado, Rogerio R; Pesaro, Antonio E; Wajngarten, Mauricio

2016-12-01

The effect of socioeconomic stressors on the incidence of cardiovascular disease (CVD) is currently open to debate. Using time-series analysis, our study aimed to evaluate the relationship between unemployment rate and hospital admission for acute myocardial infarction (AMI) and stroke in Brazil over a recent 11-year span. Data on monthly hospital admissions for AMI and stroke from March 2002 to December 2013 were extracted from the Brazilian Public Health System Database. The monthly unemployment rate was obtained from the Brazilian Institute for Applied Economic Research, during the same period. The autoregressive integrated moving average (ARIMA) model was used to test the association of temporal series. Statistical significance was set at p<0.05. From March 2002 to December 2013, 778,263 admissions for AMI and 1,581,675 for stroke were recorded. During this time period, the unemployment rate decreased from 12.9% in 2002 to 4.3% in 2013, while admissions due to AMI and stroke increased. However, the adjusted ARIMA model showed a positive association between the unemployment rate and admissions for AMI but not for stroke (estimate coefficient=2.81±0.93; p=0.003 and estimate coefficient=2.40±4.34; p=0.58, respectively). From 2002 to 2013, hospital admissions for AMI and stroke increased, whereas the unemployment rate decreased. However, the adjusted ARIMA model showed a positive association between unemployment rate and admissions due to AMI but not for stroke. Further studies are warranted to validate our findings and to better explore the mechanisms by which socioeconomic stressors, such as unemployment, might impact on the incidence of CVD. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Time-Series Approaches for Forecasting the Number of Hospital Daily Discharged Inpatients.

PubMed

Ting Zhu; Li Luo; Xinli Zhang; Yingkang Shi; Wenwu Shen

2017-03-01

For hospitals where decisions regarding acceptable rates of elective admissions are made in advance based on expected available bed capacity and emergency requests, accurate predictions of inpatient bed capacity are especially useful for capacity reservation purposes. As given, the remaining unoccupied beds at the end of each day, bed capacity of the next day can be obtained by examining the forecasts of the number of discharged patients during the next day. The features of fluctuations in daily discharges like trend, seasonal cycles, special-day effects, and autocorrelation complicate decision optimizing, while time-series models can capture these features well. This research compares three models: a model combining seasonal regression and ARIMA, a multiplicative seasonal ARIMA (MSARIMA) model, and a combinatorial model based on MSARIMA and weighted Markov Chain models in generating forecasts of daily discharges. The models are applied to three years of discharge data of an entire hospital. Several performance measures like the direction of the symmetry value, normalized mean squared error, and mean absolute percentage error are utilized to capture the under- and overprediction in model selection. The findings indicate that daily discharges can be forecast by using the proposed models. A number of important practical implications are discussed, such as the use of accurate forecasts in discharge planning, admission scheduling, and capacity reservation.

Examining deterrence of adult sex crimes: A semi-parametric intervention time series approach.

PubMed

Park, Jin-Hong; Bandyopadhyay, Dipankar; Letourneau, Elizabeth

2014-01-01

Motivated by recent developments on dimension reduction (DR) techniques for time series data, the association of a general deterrent effect towards South Carolina (SC)'s registration and notification (SORN) policy for preventing sex crimes was examined. Using adult sex crime arrestee data from 1990 to 2005, the the idea of Central Mean Subspace (CMS) is extended to intervention time series analysis (CMS-ITS) to model the sequential intervention effects of 1995 (the year SC's SORN policy was initially implemented) and 1999 (the year the policy was revised to include online notification) on the time series spectrum. The CMS-ITS model estimation was achieved via kernel smoothing techniques, and compared to interrupted auto-regressive integrated time series (ARIMA) models. Simulation studies and application to the real data underscores our model's ability towards achieving parsimony, and to detect intervention effects not earlier determined via traditional ARIMA models. From a public health perspective, findings from this study draw attention to the potential general deterrent effects of SC's SORN policy. These findings are considered in light of the overall body of research on sex crime arrestee registration and notification policies, which remain controversial.

Treatment on outliers in UBJ-SARIMA models for forecasting dengue cases on age groups not eligible for vaccination in Baguio City, Philippines

NASA Astrophysics Data System (ADS)

Magsakay, Clarenz B.; De Vera, Nora U.; Libatique, Criselda P.; Addawe, Rizavel C.; Addawe, Joel M.

2017-11-01

Dengue vaccination has become a breakthrough in the fight against dengue infection. This is however not applicable to all ages. Individuals from 0 to 8 years old and adults older than 45 years old remain susceptible to the vector-borne disease dengue. Forecasting future dengue cases accurately from susceptible age groups would aid in the efforts to prevent further increase in dengue infections. For the age groups of individuals not eligible for vaccination, the presence of outliers was observed and was treated using winsorization, square root, and logarithmic transformations to create a SARIMA model. The best model for the age group 0 to 8 years old was found to be ARIMA(13,1,0)(1,0,0)12 with 10 fixed variables using square root transformation with a 95% winsorization, and the best model for the age group older than 45 years old is ARIMA(7,1,0)(1,0,0)12 with 5 fixed variables using logarithmic transformation with 90% winsorization. These models are then used to forecast the monthly dengue cases for Baguio City for the age groups considered.

Drought variability in six catchments in the UK

NASA Astrophysics Data System (ADS)

Kwok-Pan, Chun; Onof, Christian; Wheater, Howard

2010-05-01

Drought is fundamentally related to consistent low precipitation levels. Changes in global and regional drought patterns are suggested by numerous recent climate change studies. However, most of the climate change adaptation measures are at a catchment scale, and the development of a framework for studying persistence in precipitation is still at an early stage. Two stochastic approaches for modelling drought severity index (DSI) are proposed to investigate possible changes in droughts in six catchments in the UK. They are the autoregressive integrated moving average (ARIMA) and the generalised linear model (GLM) approach. Results of ARIMA modelling show that mean sea level pressure and possibly the North Atlantic Oscillation (NAO) index are important climate variables for short term drought forecasts, whereas relative humidity is not a significant climate variable despite its high correlation with the DSI series. By simulating rainfall series, the generalised linear model (GLM) approach can provide the probability density function of the DSI. GLM simulations indicate that the changes in the 10th and 50th quantiles of drought events are more noticeable than in the 90th extreme droughts. The possibility of extending the GLM approach to support risk-based water management is also discussed.

Statistical approach to the analysis of olive long-term pollen season trends in southern Spain.

PubMed

García-Mozo, H; Yaezel, L; Oteros, J; Galán, C

2014-03-01

Analysis of long-term airborne pollen counts makes it possible not only to chart pollen-season trends but also to track changing patterns in flowering phenology. Changes in higher plant response over a long interval are considered among the most valuable bioindicators of climate change impact. Phenological-trend models can also provide information regarding crop production and pollen-allergen emission. The interest of this information makes essential the election of the statistical analysis for time series study. We analysed trends and variations in the olive flowering season over a 30-year period (1982-2011) in southern Europe (Córdoba, Spain), focussing on: annual Pollen Index (PI); Pollen Season Start (PSS), Peak Date (PD), Pollen Season End (PSE) and Pollen Season Duration (PSD). Apart from the traditional Linear Regression analysis, a Seasonal-Trend Decomposition procedure based on Loess (STL) and an ARIMA model were performed. Linear regression results indicated a trend toward delayed PSE and earlier PSS and PD, probably influenced by the rise in temperature. These changes are provoking longer flowering periods in the study area. The use of the STL technique provided a clearer picture of phenological behaviour. Data decomposition on pollination dynamics enabled the trend toward an alternate bearing cycle to be distinguished from the influence of other stochastic fluctuations. Results pointed to show a rising trend in pollen production. With a view toward forecasting future phenological trends, ARIMA models were constructed to predict PSD, PSS and PI until 2016. Projections displayed a better goodness of fit than those derived from linear regression. Findings suggest that olive reproductive cycle is changing considerably over the last 30years due to climate change. Further conclusions are that STL improves the effectiveness of traditional linear regression in trend analysis, and ARIMA models can provide reliable trend projections for future years taking into account the internal fluctuations in time series. Copyright © 2013 Elsevier B.V. All rights reserved.

Energy Forecasting Models Within the Department of the Navy.

DTIC Science & Technology

1982-06-01

standing the climatic conditions responsible for the results. Both models have particular advantages in parti- cular applications and will be examined...and moving average processes. A similar notation for a model with seasonality . .- considerations will be ARIMA (p d j)(P Q) 3=12, where the upper...AD-A12l 950 ENERGY FORECASTING MODELS WITHIN THE DEPARTMENT OF THE 1/4 NAYY(U) NAVAL POSTGRADUATE SCHOOL MONTEREY CA L &I BUTTOIPH JUN 82

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

Modeling Seasonal Influenza Transmission and Its Association with Climate Factors in Thailand Using Time-Series and ARIMAX Analyses.

PubMed

Chadsuthi, Sudarat; Iamsirithaworn, Sopon; Triampo, Wannapong; Modchang, Charin

2015-01-01

Influenza is a worldwide respiratory infectious disease that easily spreads from one person to another. Previous research has found that the influenza transmission process is often associated with climate variables. In this study, we used autocorrelation and partial autocorrelation plots to determine the appropriate autoregressive integrated moving average (ARIMA) model for influenza transmission in the central and southern regions of Thailand. The relationships between reported influenza cases and the climate data, such as the amount of rainfall, average temperature, average maximum relative humidity, average minimum relative humidity, and average relative humidity, were evaluated using cross-correlation function. Based on the available data of suspected influenza cases and climate variables, the most appropriate ARIMA(X) model for each region was obtained. We found that the average temperature correlated with influenza cases in both central and southern regions, but average minimum relative humidity played an important role only in the southern region. The ARIMAX model that includes the average temperature with a 4-month lag and the minimum relative humidity with a 2-month lag is the appropriate model for the central region, whereas including the minimum relative humidity with a 4-month lag results in the best model for the southern region.

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.

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.

Are Math Grades Cyclical?

ERIC Educational Resources Information Center

Adams, Gerald J.; Dial, Micah

1998-01-01

The cyclical nature of mathematics grades was studied for a cohort of elementary school students from a large metropolitan school district in Texas over six years (average cohort size of 8495). The study used an autoregressive integrated moving average (ARIMA) model. Results indicate that grades do exhibit a significant cyclical pattern. (SLD)

Using statistical anomaly detection models to find clinical decision support malfunctions.

PubMed

Ray, Soumi; McEvoy, Dustin S; Aaron, Skye; Hickman, Thu-Trang; Wright, Adam

2018-05-11

Malfunctions in Clinical Decision Support (CDS) systems occur due to a multitude of reasons, and often go unnoticed, leading to potentially poor outcomes. Our goal was to identify malfunctions within CDS systems. We evaluated 6 anomaly detection models: (1) Poisson Changepoint Model, (2) Autoregressive Integrated Moving Average (ARIMA) Model, (3) Hierarchical Divisive Changepoint (HDC) Model, (4) Bayesian Changepoint Model, (5) Seasonal Hybrid Extreme Studentized Deviate (SHESD) Model, and (6) E-Divisive with Median (EDM) Model and characterized their ability to find known anomalies. We analyzed 4 CDS alerts with known malfunctions from the Longitudinal Medical Record (LMR) and Epic® (Epic Systems Corporation, Madison, WI, USA) at Brigham and Women's Hospital, Boston, MA. The 4 rules recommend lead testing in children, aspirin therapy in patients with coronary artery disease, pneumococcal vaccination in immunocompromised adults and thyroid testing in patients taking amiodarone. Poisson changepoint, ARIMA, HDC, Bayesian changepoint and the SHESD model were able to detect anomalies in an alert for lead screening in children and in an alert for pneumococcal conjugate vaccine in immunocompromised adults. EDM was able to detect anomalies in an alert for monitoring thyroid function in patients on amiodarone. Malfunctions/anomalies occur frequently in CDS alert systems. It is important to be able to detect such anomalies promptly. Anomaly detection models are useful tools to aid such detections.

Model variations in predicting incidence of Plasmodium falciparum malaria using 1998-2007 morbidity and meteorological data from south Ethiopia

PubMed Central

2010-01-01

Background Malaria transmission is complex and is believed to be associated with local climate changes. However, simple attempts to extrapolate malaria incidence rates from averaged regional meteorological conditions have proven unsuccessful. Therefore, the objective of this study was to determine if variations in specific meteorological factors are able to consistently predict P. falciparum malaria incidence at different locations in south Ethiopia. Methods Retrospective data from 42 locations were collected including P. falciparum malaria incidence for the period of 1998-2007 and meteorological variables such as monthly rainfall (all locations), temperature (17 locations), and relative humidity (three locations). Thirty-five data sets qualified for the analysis. Ljung-Box Q statistics was used for model diagnosis, and R squared or stationary R squared was taken as goodness of fit measure. Time series modelling was carried out using Transfer Function (TF) models and univariate auto-regressive integrated moving average (ARIMA) when there was no significant predictor meteorological variable. Results Of 35 models, five were discarded because of the significant value of Ljung-Box Q statistics. Past P. falciparum malaria incidence alone (17 locations) or when coupled with meteorological variables (four locations) was able to predict P. falciparum malaria incidence within statistical significance. All seasonal AIRMA orders were from locations at altitudes above 1742 m. Monthly rainfall, minimum and maximum temperature was able to predict incidence at four, five and two locations, respectively. In contrast, relative humidity was not able to predict P. falciparum malaria incidence. The R squared values for the models ranged from 16% to 97%, with the exception of one model which had a negative value. Models with seasonal ARIMA orders were found to perform better. However, the models for predicting P. falciparum malaria incidence varied from location to location, and among lagged effects, data transformation forms, ARIMA and TF orders. Conclusions This study describes P. falciparum malaria incidence models linked with meteorological data. Variability in the models was principally attributed to regional differences, and a single model was not found that fits all locations. Past P. falciparum malaria incidence appeared to be a superior predictor than meteorology. Future efforts in malaria modelling may benefit from inclusion of non-meteorological factors. PMID:20553590

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.

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.

Physician and nurse supply in Serbia using time-series data

PubMed Central

2013-01-01

Background Unemployment among health professionals in Serbia has risen in the recent past and continues to increase. This highlights the need to understand how to change policies to meet real and projected needs. This study identified variables that were significantly related to physician and nurse employment rates in the public healthcare sector in Serbia from 1961 to 2008 and used these to develop parameters to model physician and nurse supply in the public healthcare sector through to 2015. Methods The relationships among six variables used for planning physician and nurse employment in public healthcare sector in Serbia were identified for two periods: 1961 to 1982 and 1983 to 2008. Those variables included: the annual total national population; gross domestic product adjusted to 1994 prices; inpatient care discharges; outpatient care visits; students enrolled in the first year of medical studies at public universities; and the annual number of graduated physicians. Based on historic trends, physician supply and nurse supply in the public healthcare sector by 2015 (with corresponding 95% confidence level) have been modeled using Autoregressive Integrated Moving Average (ARIMA) / Transfer function (TF) models. Results The ARIMA/TF modeling yielded stable and significant forecasts of physician supply (stationary R2 squared = 0.71) and nurse supply (stationary R2 squared = 0.92) in the public healthcare sector in Serbia through to 2015. The most significant predictors for physician employment were the population and GDP. The supply of nursing staff was, in turn, related to the number of physicians. Physician and nurse rates per 100,000 population increased by 13%. The model predicts a seven-year mismatch between the supply of graduates and vacancies in the public healthcare sector is forecasted at 8,698 physicians - a net surplus. Conclusion The ARIMA model can be used to project trends, especially those that identify significant mismatches between forecasted supply of physicians and vacancies and can be used to guide decision-making for enrollment planning for the medical schools in Serbia. Serbia needs an inter-sectoral strategy for HRH development that is more coherent with healthcare objectives and more accountable in terms of professional mobility. PMID:23773678

Physician and nurse supply in Serbia using time-series data.

PubMed

Santric-Milicevic, Milena; Vasic, Vladimir; Marinkovic, Jelena

2013-06-17

Unemployment among health professionals in Serbia has risen in the recent past and continues to increase. This highlights the need to understand how to change policies to meet real and projected needs. This study identified variables that were significantly related to physician and nurse employment rates in the public healthcare sector in Serbia from 1961 to 2008 and used these to develop parameters to model physician and nurse supply in the public healthcare sector through to 2015. The relationships among six variables used for planning physician and nurse employment in public healthcare sector in Serbia were identified for two periods: 1961 to 1982 and 1983 to 2008. Those variables included: the annual total national population; gross domestic product adjusted to 1994 prices; inpatient care discharges; outpatient care visits; students enrolled in the first year of medical studies at public universities; and the annual number of graduated physicians. Based on historic trends, physician supply and nurse supply in the public healthcare sector by 2015 (with corresponding 95% confidence level) have been modeled using Autoregressive Integrated Moving Average (ARIMA) / Transfer function (TF) models. The ARIMA/TF modeling yielded stable and significant forecasts of physician supply (stationary R2 squared = 0.71) and nurse supply (stationary R2 squared = 0.92) in the public healthcare sector in Serbia through to 2015. The most significant predictors for physician employment were the population and GDP. The supply of nursing staff was, in turn, related to the number of physicians. Physician and nurse rates per 100,000 population increased by 13%. The model predicts a seven-year mismatch between the supply of graduates and vacancies in the public healthcare sector is forecasted at 8,698 physicians - a net surplus. The ARIMA model can be used to project trends, especially those that identify significant mismatches between forecasted supply of physicians and vacancies and can be used to guide decision-making for enrollment planning for the medical schools in Serbia. Serbia needs an inter-sectoral strategy for HRH development that is more coherent with healthcare objectives and more accountable in terms of professional mobility.

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.

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.

Time series modeling for syndromic surveillance.

PubMed

Reis, Ben Y; Mandl, Kenneth D

2003-01-23

Emergency department (ED) based syndromic surveillance systems identify abnormally high visit rates that may be an early signal of a bioterrorist attack. For example, an anthrax outbreak might first be detectable as an unusual increase in the number of patients reporting to the ED with respiratory symptoms. Reliably identifying these abnormal visit patterns requires a good understanding of the normal patterns of healthcare usage. Unfortunately, systematic methods for determining the expected number of (ED) visits on a particular day have not yet been well established. We present here a generalized methodology for developing models of expected ED visit rates. Using time-series methods, we developed robust models of ED utilization for the purpose of defining expected visit rates. The models were based on nearly a decade of historical data at a major metropolitan academic, tertiary care pediatric emergency department. The historical data were fit using trimmed-mean seasonal models, and additional models were fit with autoregressive integrated moving average (ARIMA) residuals to account for recent trends in the data. The detection capabilities of the model were tested with simulated outbreaks. Models were built both for overall visits and for respiratory-related visits, classified according to the chief complaint recorded at the beginning of each visit. The mean absolute percentage error of the ARIMA models was 9.37% for overall visits and 27.54% for respiratory visits. A simple detection system based on the ARIMA model of overall visits was able to detect 7-day-long simulated outbreaks of 30 visits per day with 100% sensitivity and 97% specificity. Sensitivity decreased with outbreak size, dropping to 94% for outbreaks of 20 visits per day, and 57% for 10 visits per day, all while maintaining a 97% benchmark specificity. Time series methods applied to historical ED utilization data are an important tool for syndromic surveillance. Accurate forecasting of emergency department total utilization as well as the rates of particular syndromes is possible. The multiple models in the system account for both long-term and recent trends, and an integrated alarms strategy combining these two perspectives may provide a more complete picture to public health authorities. The systematic methodology described here can be generalized to other healthcare settings to develop automated surveillance systems capable of detecting anomalies in disease patterns and healthcare utilization.

A Summary of the Naval Postgraduate School Research Program.

DTIC Science & Technology

1979-09-30

Research (M. G. Sovereign) 116 Review of COMWTH II Model (M. G. Sovereign and J. K. Arima ) 117 Optimization of Combat Dynamics (J. G. Taylor) 118...Studies (R. L. Elsberry) 291 4 Numerical Models of Ocean Circulation and Climate Interaction--A Review (R. L. Haney) 292 Numerical Studies of the Dynamics... climatic numerical models to investigate the various mechan- isms pertinent to the large-scale interaction between tropi- cal atmosphere and oceans. Among

Analysis of significant factors for dengue fever incidence prediction.

PubMed

Siriyasatien, Padet; Phumee, Atchara; Ongruk, Phatsavee; Jampachaisri, Katechan; Kesorn, Kraisak

2016-04-16

Many popular dengue forecasting techniques have been used by several researchers to extrapolate dengue incidence rates, including the K-H model, support vector machines (SVM), and artificial neural networks (ANN). The time series analysis methodology, particularly ARIMA and SARIMA, has been increasingly applied to the field of epidemiological research for dengue fever, dengue hemorrhagic fever, and other infectious diseases. The main drawback of these methods is that they do not consider other variables that are associated with the dependent variable. Additionally, new factors correlated to the disease are needed to enhance the prediction accuracy of the model when it is applied to areas of similar climates, where weather factors such as temperature, total rainfall, and humidity are not substantially different. Such drawbacks may consequently lower the predictive power for the outbreak. The predictive power of the forecasting model-assessed by Akaike's information criterion (AIC), Bayesian information criterion (BIC), and the mean absolute percentage error (MAPE)-is improved by including the new parameters for dengue outbreak prediction. This study's selected model outperforms all three other competing models with the lowest AIC, the lowest BIC, and a small MAPE value. The exclusive use of climate factors from similar locations decreases a model's prediction power. The multivariate Poisson regression, however, effectively forecasts even when climate variables are slightly different. Female mosquitoes and seasons were strongly correlated with dengue cases. Therefore, the dengue incidence trends provided by this model will assist the optimization of dengue prevention. The present work demonstrates the important roles of female mosquito infection rates from the previous season and climate factors (represented as seasons) in dengue outbreaks. Incorporating these two factors in the model significantly improves the predictive power of dengue hemorrhagic fever forecasting models, as confirmed by AIC, BIC, and MAPE.

Temporal and long-term trend analysis of class C notifiable diseases in China from 2009 to 2014

PubMed Central

Zhang, Xingyu; Hou, Fengsu; Qiao, Zhijiao; Li, Xiaosong; Zhou, Lijun; Liu, Yuanyuan; Zhang, Tao

2016-01-01

Objectives Time series models are effective tools for disease forecasting. This study aims to explore the time series behaviour of 11 notifiable diseases in China and to predict their incidence through effective models. Settings and participants The Chinese Ministry of Health started to publish class C notifiable diseases in 2009. The monthly reported case time series of 11 infectious diseases from the surveillance system between 2009 and 2014 was collected. Methods We performed a descriptive and a time series study using the surveillance data. Decomposition methods were used to explore (1) their seasonality expressed in the form of seasonal indices and (2) their long-term trend in the form of a linear regression model. Autoregressive integrated moving average (ARIMA) models have been established for each disease. Results The number of cases and deaths caused by hand, foot and mouth disease ranks number 1 among the detected diseases. It occurred most often in May and July and increased, on average, by 0.14126/100 000 per month. The remaining incidence models show good fit except the influenza and hydatid disease models. Both the hydatid disease and influenza series become white noise after differencing, so no available ARIMA model can be fitted for these two diseases. Conclusion Time series analysis of effective surveillance time series is useful for better understanding the occurrence of the 11 types of infectious disease. PMID:27797981

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.

The Effects of Local Economic Conditions on Navy Enlistments.

DTIC Science & Technology

1980-03-18

Standard Metropolitan Statistical Area (SMSA) as the basic economic unit, cross-sectional regression models were constructed for enlistment rate, recruiter...to eligible population suggesting that a cheaper alternative to raising mili- tary wages would be to increase the number of recruiters. Arima (1978...is faced with a number of cri- teria that must be satisfied by an acceptable test variable. As with other variables included in the model , economic

High mean water vapour pressure promotes the transmission of bacillary dysentery.

PubMed

Li, Guo-Zheng; Shao, Feng-Feng; Zhang, Hao; Zou, Chun-Pu; Li, Hui-Hui; Jin, Jue

2015-01-01

Bacillary dysentery is an infectious disease caused by Shigella dysenteriae, which has a seasonal distribution. External environmental factors, including climate, play a significant role in its transmission. This paper identifies climate-related risk factors and their role in bacillary dysentery transmission. Harbin, in northeast China, with a temperate climate, and Quzhou, in southern China, with a subtropical climate, are chosen as the study locations. The least absolute shrinkage and selectionator operator is applied to select relevant climate factors involved in the transmission of bacillary dysentery. Based on the selected relevant climate factors and incidence rates, an AutoRegressive Integrated Moving Average (ARIMA) model is established successfully as a time series prediction model. The numerical results demonstrate that the mean water vapour pressure over the previous month results in a high relative risk for bacillary dysentery transmission in both cities, and the ARIMA model can successfully perform such a prediction. These results provide better explanations for the relationship between climate factors and bacillary dysentery transmission than those put forth in other studies that use only correlation coefficients or fitting models. The findings in this paper demonstrate that the mean water vapour pressure over the previous month is an important predictor for the transmission of bacillary dysentery.

[The trial of business data analysis at the Department of Radiology by constructing the auto-regressive integrated moving-average (ARIMA) model].

PubMed

Tani, Yuji; Ogasawara, Katsuhiko

2012-01-01

This study aimed to contribute to the management of a healthcare organization by providing management information using time-series analysis of business data accumulated in the hospital information system, which has not been utilized thus far. In this study, we examined the performance of the prediction method using the auto-regressive integrated moving-average (ARIMA) model, using the business data obtained at the Radiology Department. We made the model using the data used for analysis, which was the number of radiological examinations in the past 9 years, and we predicted the number of radiological examinations in the last 1 year. Then, we compared the actual value with the forecast value. We were able to establish that the performance prediction method was simple and cost-effective by using free software. In addition, we were able to build the simple model by pre-processing the removal of trend components using the data. The difference between predicted values and actual values was 10%; however, it was more important to understand the chronological change rather than the individual time-series values. Furthermore, our method was highly versatile and adaptable compared to the general time-series data. Therefore, different healthcare organizations can use our method for the analysis and forecasting of their business data.

Developing a Markov Model for Forecasting End Strength of Selected Marine Corps Reserve (SMCR) Officers

DTIC Science & Technology

2013-03-01

moving average ( ARIMA ) model because the data is not a times series. The best a manpower planner can do at this point is to make an educated assumption...MARKOV MODEL FOR FORECASTING END STRENGTH OF SELECTED MARINE CORPS RESERVE (SMCR) OFFICERS by Anthony D. Licari March 2013 Thesis Advisor...March 2013 3. REPORT TYPE AND DATES COVERED Master’s Thesis 4. TITLE AND SUBTITLE DEVELOPING A MARKOV MODEL FOR FORECASTING END STRENGTH OF

Genetically Engineered, Live Attenuated Vaccines Protect Nonhuman Primates Against Aerosol Challenge with a Virulent IE Strain of Venezuelan Equine Encephalitis Virus

DTIC Science & Technology

2005-01-21

integrated moving average ( ARIMA ) model [15,19]. Fore- casted values for the postexposure time periods were based on the training model extrapolated...Smith JF. Genetically engineered, live attenuated vaccines or Venezuelan equine encephalitis: testing in animal models . Vaccine 2003;21(25–26):3854–62...encephalitis: testing in animal models . Vaccine 2003;21(25-26):3854-62] and IE strains of VEE viruses. 15. SUBJECT TERMS Venezuelan equine

Dynamic Forecasting of Zika Epidemics Using Google Trends

PubMed Central

Jin, Yuan; Huang, Yong; Lin, Baihan; An, Xiaoping; Feng, Dan; Tong, Yigang

2017-01-01

We developed a dynamic forecasting model for Zika virus (ZIKV), based on real-time online search data from Google Trends (GTs). It was designed to provide Zika virus disease (ZVD) surveillance and detection for Health Departments, and predictive numbers of infection cases, which would allow them sufficient time to implement interventions. In this study, we found a strong correlation between Zika-related GTs and the cumulative numbers of reported cases (confirmed, suspected and total cases; p<0.001). Then, we used the correlation data from Zika-related online search in GTs and ZIKV epidemics between 12 February and 20 October 2016 to construct an autoregressive integrated moving average (ARIMA) model (0, 1, 3) for the dynamic estimation of ZIKV outbreaks. The forecasting results indicated that the predicted data by ARIMA model, which used the online search data as the external regressor to enhance the forecasting model and assist the historical epidemic data in improving the quality of the predictions, are quite similar to the actual data during ZIKV epidemic early November 2016. Integer-valued autoregression provides a useful base predictive model for ZVD cases. This is enhanced by the incorporation of GTs data, confirming the prognostic utility of search query based surveillance. This accessible and flexible dynamic forecast model could be used in the monitoring of ZVD to provide advanced warning of future ZIKV outbreaks. PMID:28060809

Dynamic Forecasting of Zika Epidemics Using Google Trends.

PubMed

Teng, Yue; Bi, Dehua; Xie, Guigang; Jin, Yuan; Huang, Yong; Lin, Baihan; An, Xiaoping; Feng, Dan; Tong, Yigang

2017-01-01

We developed a dynamic forecasting model for Zika virus (ZIKV), based on real-time online search data from Google Trends (GTs). It was designed to provide Zika virus disease (ZVD) surveillance and detection for Health Departments, and predictive numbers of infection cases, which would allow them sufficient time to implement interventions. In this study, we found a strong correlation between Zika-related GTs and the cumulative numbers of reported cases (confirmed, suspected and total cases; p<0.001). Then, we used the correlation data from Zika-related online search in GTs and ZIKV epidemics between 12 February and 20 October 2016 to construct an autoregressive integrated moving average (ARIMA) model (0, 1, 3) for the dynamic estimation of ZIKV outbreaks. The forecasting results indicated that the predicted data by ARIMA model, which used the online search data as the external regressor to enhance the forecasting model and assist the historical epidemic data in improving the quality of the predictions, are quite similar to the actual data during ZIKV epidemic early November 2016. Integer-valued autoregression provides a useful base predictive model for ZVD cases. This is enhanced by the incorporation of GTs data, confirming the prognostic utility of search query based surveillance. This accessible and flexible dynamic forecast model could be used in the monitoring of ZVD to provide advanced warning of future ZIKV outbreaks.

Hybrid Wavelet De-noising and Rank-Set Pair Analysis approach for forecasting hydro-meteorological time series

NASA Astrophysics Data System (ADS)

WANG, D.; Wang, Y.; Zeng, X.

2017-12-01

Accurate, fast forecasting of hydro-meteorological time series is presently a major challenge in drought and flood mitigation. This paper proposes a hybrid approach, Wavelet De-noising (WD) and Rank-Set Pair Analysis (RSPA), that takes full advantage of a combination of the two approaches to improve forecasts of hydro-meteorological time series. WD allows decomposition and reconstruction of a time series by the wavelet transform, and hence separation of the noise from the original series. RSPA, a more reliable and efficient version of Set Pair Analysis, is integrated with WD to form the hybrid WD-RSPA approach. Two types of hydro-meteorological data sets with different characteristics and different levels of human influences at some representative stations are used to illustrate the WD-RSPA approach. The approach is also compared to three other generic methods: the conventional Auto Regressive Integrated Moving Average (ARIMA) method, Artificial Neural Networks (ANNs) (BP-error Back Propagation, MLP-Multilayer Perceptron and RBF-Radial Basis Function), and RSPA alone. Nine error metrics are used to evaluate the model performance. The results show that WD-RSPA is accurate, feasible, and effective. In particular, WD-RSPA is found to be the best among the various generic methods compared in this paper, even when the extreme events are included within a time series.

A hybrid wavelet de-noising and Rank-Set Pair Analysis approach for forecasting hydro-meteorological time series.

PubMed

Wang, Dong; Borthwick, Alistair G; He, Handan; Wang, Yuankun; Zhu, Jieyu; Lu, Yuan; Xu, Pengcheng; Zeng, Xiankui; Wu, Jichun; Wang, Lachun; Zou, Xinqing; Liu, Jiufu; Zou, Ying; He, Ruimin

2018-01-01

Accurate, fast forecasting of hydro-meteorological time series is presently a major challenge in drought and flood mitigation. This paper proposes a hybrid approach, wavelet de-noising (WD) and Rank-Set Pair Analysis (RSPA), that takes full advantage of a combination of the two approaches to improve forecasts of hydro-meteorological time series. WD allows decomposition and reconstruction of a time series by the wavelet transform, and hence separation of the noise from the original series. RSPA, a more reliable and efficient version of Set Pair Analysis, is integrated with WD to form the hybrid WD-RSPA approach. Two types of hydro-meteorological data sets with different characteristics and different levels of human influences at some representative stations are used to illustrate the WD-RSPA approach. The approach is also compared to three other generic methods: the conventional Auto Regressive Integrated Moving Average (ARIMA) method, Artificial Neural Networks (ANNs) (BP-error Back Propagation, MLP-Multilayer Perceptron and RBF-Radial Basis Function), and RSPA alone. Nine error metrics are used to evaluate the model performance. Compared to three other generic methods, the results generated by WD-REPA model presented invariably smaller error measures which means the forecasting capability of the WD-REPA model is better than other models. The results show that WD-RSPA is accurate, feasible, and effective. In particular, WD-RSPA is found to be the best among the various generic methods compared in this paper, even when the extreme events are included within a time series. Copyright © 2017 Elsevier Inc. All rights reserved.

Identifying trends in climate: an application to the cenozoic

NASA Astrophysics Data System (ADS)

Richards, Gordon R.

1998-05-01

The recent literature on trending in climate has raised several issues, whether trends should be modeled as deterministic or stochastic, whether trends are nonlinear, and the relative merits of statistical models versus models based on physics. This article models trending since the late Cretaceous. This 68 million-year interval is selected because the reliability of tests for trending is critically dependent on the length of time spanned by the data. Two main hypotheses are tested, that the trend has been caused primarily by CO2 forcing, and that it reflects a variety of forcing factors which can be approximated by statistical methods. The CO2 data is obtained from model simulations. Several widely-used statistical models are found to be inadequate. ARIMA methods parameterize too much of the short-term variation, and do not identify low frequency movements. Further, the unit root in the ARIMA process does not predict the long-term path of temperature. Spectral methods also have little ability to predict temperature at long horizons. Instead, the statistical trend is estimated using a nonlinear smoothing filter. Both of these paradigms make it possible to model climate as a cointegrated process, in which temperature can wander quite far from the trend path in the intermediate term, but converges back over longer horizons. Comparing the forecasting properties of the two trend models demonstrates that the optimal forecasting model includes CO2 forcing and a parametric representation of the nonlinear variability in climate.

Statistical variability comparison in MODIS and AERONET derived aerosol optical depth over Indo-Gangetic Plains using time series modeling.

PubMed

Soni, Kirti; Parmar, Kulwinder Singh; Kapoor, Sangeeta; Kumar, Nishant

2016-05-15

A lot of studies in the literature of Aerosol Optical Depth (AOD) done by using Moderate Resolution Imaging Spectroradiometer (MODIS) derived data, but the accuracy of satellite data in comparison to ground data derived from ARrosol Robotic NETwork (AERONET) has been always questionable. So to overcome from this situation, comparative study of a comprehensive ground based and satellite data for the period of 2001-2012 is modeled. The time series model is used for the accurate prediction of AOD and statistical variability is compared to assess the performance of the model in both cases. Root mean square error (RMSE), mean absolute percentage error (MAPE), stationary R-squared, R-squared, maximum absolute percentage error (MAPE), normalized Bayesian information criterion (NBIC) and Ljung-Box methods are used to check the applicability and validity of the developed ARIMA models revealing significant precision in the model performance. It was found that, it is possible to predict the AOD by statistical modeling using time series obtained from past data of MODIS and AERONET as input data. Moreover, the result shows that MODIS data can be formed from AERONET data by adding 0.251627 ± 0.133589 and vice-versa by subtracting. From the forecast available for AODs for the next four years (2013-2017) by using the developed ARIMA model, it is concluded that the forecasted ground AOD has increased trend. Copyright © 2016 Elsevier B.V. All rights reserved.

β4 systematics in rare-earth and actinide nuclei: sdg interacting boson model description

NASA Astrophysics Data System (ADS)

Devi, Y. D.; Kota, V. K. B.

1992-07-01

The observed variation of hexadecupole deformation parameter β4 with mass number A in rare-earth and actinide nuclei is studied in the sdg interacting boson model (IBM) using single j-shell Otsuka-Arima-Iachello mapped and IBM-2 to IBM-1 projected hexadecupole transition operator together with SUsdg(3) and SUsdg(5) coherent states. The SUsdg(3) limit is found to provide a good description of data.

Approche statistique de l'aridification de l'Afrique de l'Ouest

NASA Astrophysics Data System (ADS)

Hubert, Pierre; Carbonnel, Jean-Pierre

1987-11-01

The statistical study of 42 rainfall series from Niger to Senegal, the length of which is between 37 and 97 years, points out the nonstationarity of these series and suggests a climatic jump about 1969-1970. An integrated autoregressive model (ARIMA) of order ( p, 1, 0) can be fitted to most of these series but such a model remains useless for operational purposes. Some climatological, meteorological and hydrological consequences are discussed.

Eigenvalue Tests and Distributions for Small Sample Order Determination for Complex Wishart Matrices

DTIC Science & Technology

1994-08-13

theoretic order determination criteria for ARMA(p, q) models can be expressed in the form of equation 4.2. The word ARIMA should not be a distractor...subjectivity is not necessarily bad. It enables us to build tractable models and efficiently achieve reasonable results. The charge of "subjectivity" lodged...signal processing studies because it simplifies the mathematics involved and it is not a bad model for a wide range of situations. Wooding [293] is

Real time detection of farm-level swine mycobacteriosis outbreak using time series modeling of the number of condemned intestines in abattoirs.

PubMed

Adachi, Yasumoto; Makita, Kohei

2015-09-01

Mycobacteriosis in swine is a common zoonosis found in abattoirs during meat inspections, and the veterinary authority is expected to inform the producer for corrective actions when an outbreak is detected. The expected value of the number of condemned carcasses due to mycobacteriosis therefore would be a useful threshold to detect an outbreak, and the present study aims to develop such an expected value through time series modeling. The model was developed using eight years of inspection data (2003 to 2010) obtained at 2 abattoirs of the Higashi-Mokoto Meat Inspection Center, Japan. The resulting model was validated by comparing the predicted time-dependent values for the subsequent 2 years with the actual data for 2 years between 2011 and 2012. For the modeling, at first, periodicities were checked using Fast Fourier Transformation, and the ensemble average profiles for weekly periodicities were calculated. An Auto-Regressive Integrated Moving Average (ARIMA) model was fitted to the residual of the ensemble average on the basis of minimum Akaike's information criterion (AIC). The sum of the ARIMA model and the weekly ensemble average was regarded as the time-dependent expected value. During 2011 and 2012, the number of whole or partial condemned carcasses exceeded the 95% confidence interval of the predicted values 20 times. All of these events were associated with the slaughtering of pigs from three producers with the highest rate of condemnation due to mycobacteriosis.

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

Forecasting malaria cases using climatic factors in delhi, India: a time series analysis.

PubMed

Kumar, Varun; Mangal, Abha; Panesar, Sanjeet; Yadav, Geeta; Talwar, Richa; Raut, Deepak; Singh, Saudan

2014-01-01

Background. Malaria still remains a public health problem in developing countries and changing environmental and climatic factors pose the biggest challenge in fighting against the scourge of malaria. Therefore, the study was designed to forecast malaria cases using climatic factors as predictors in Delhi, India. Methods. The total number of monthly cases of malaria slide positives occurring from January 2006 to December 2013 was taken from the register maintained at the malaria clinic at Rural Health Training Centre (RHTC), Najafgarh, Delhi. Climatic data of monthly mean rainfall, relative humidity, and mean maximum temperature were taken from Regional Meteorological Centre, Delhi. Expert modeler of SPSS ver. 21 was used for analyzing the time series data. Results. Autoregressive integrated moving average, ARIMA (0,1,1) (0,1,0)(12), was the best fit model and it could explain 72.5% variability in the time series data. Rainfall (P value = 0.004) and relative humidity (P value = 0.001) were found to be significant predictors for malaria transmission in the study area. Seasonal adjusted factor (SAF) for malaria cases shows peak during the months of August and September. Conclusion. ARIMA models of time series analysis is a simple and reliable tool for producing reliable forecasts for malaria in Delhi, India.

The effect of the 1997 Texas motorcycle helmet law on motorcycle crash fatalities.

PubMed

Bavon, Al; Standerfer, Christina

2010-01-01

This study seeks to determine the effect of the Texas motorcycle helmet law on fatalities since the repeal of the universal helmet law in 1997. Texas monthly motorcycle accident data between 1994 and 2004 were obtained from the National Highway Transportation Safety Administration's Fatality Analysis Reporting System (FARS) and supplemented with motorcycle registration data from the Texas Department of Transportation. An ARIMA model was used to estimate the impact of the law. A sharp increase in fatality rates occurred immediately following the implementation of the law in September 1997. Deaths increased by 30%, fatality rates per motorcycle registrations increased by 15.2%, and fatality rates per vehicle miles traveled increased by 25% after repeal. Helmet use decreased from 77% in 1996 to 63% in 1997 and 36% in 1998 and thereafter. The parameter estimates of the ARIMA model (0,0,0) (0,1,1) show that the change in the law led to statistically significant increases of 2.3 fatalities and 1.18 fatality rate per 100 billion vehicle miles traveled. The repeal of the universal helmet law in Texas in 1997 has had a significant adverse effect on motorcyclist fatalities in Texas.

Is there an impact of global and local disasters on psychiatric inpatient admissions?

PubMed

Haker, Helene; Lauber, Christoph; Malti, Tina; Rössler, Wulf

2004-10-01

Disasters of the magnitude of September 11, 2001 have a serious public health impact. By dominating media broadcasts, this effect is not limited to the site of the disaster. We tested the hypothesis whether such extraordinary burden results in an increase of psychiatric inpatient treatment. As such we analysed all psychiatric inpatient admissions in the Canton of Zurich/Switzerland. To test the influence of proximity to a disaster, we additionally analysed the impact of a local amok run on September 27, 2001. Psychiatric inpatient admissions in the Canton of Zurich from September 2000 to September 2002 were analysed based on the data of the psychiatric case register. ARIMA modelling was employed to describe time-series of admissions per week over the 2-year period and to identify the impact of the incidents of 9/11 and 9/27, 2001. Mean numbers of weekly admissions were comparable in a time span of one month before and one month after the two incidents, thus, no significant changes were detected by the ARIMA modelling. Against widespread beliefs, for patients with severe mental disorders requiring hospitalisation illness factors seem to play a more relevant role for decompensation than external psychosocial factors such as the described incidents.

Reply to the Discussion of Space-Time Modelling with Long-Memory Dependence: Assessing Ireland’s Wind Resource

DTIC Science & Technology

1988-10-01

meteorologists’ rule-of-thumb that climatic drift manifests itself in periods greater than 30 years. For a fractionally-differenced model with our...estimates in a univariate ARIMA (p, d, q) with I d I< 0.5 has been derived by Li and McLrjd (1986). The model used by I-Iaslett an Raftery can be viewed as...Reply to the Discussion of "Space-time Modelling with Long-mnmory cDependence: Assessing Ireland’s Wind Resource" cJohn Haslett Department of

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.

The Importance of Practice in the Development of Statistics.

DTIC Science & Technology

1983-01-01

RESOLUTION TEST CHART NATIONAL BUREAU OIF STANDARDS 1963 -A NRC Technical Summary Report #2471 C THE IMORTANCE OF PRACTICE IN to THE DEVELOPMENT OF STATISTICS...component analysis, bioassay, limits for a ratio, quality control, sampling inspection, non-parametric tests , transformation theory, ARIMA time series...models, sequential tests , cumulative sum charts, data analysis plotting techniques, and a resolution of the Bayes - frequentist controversy. It appears

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.

A Summary of the Naval Postgraduate School Research Program.

DTIC Science & Technology

1981-04-01

1467-1489. K. M. Lau," Climatic Feedback Mechanisms in the Tropical Pacific,"Ocean Modeling , 28. K. M. Lau,"Oscillation in a Simple Equatorial...and 74 R. H. Shudde Support J. K. Arima Research in Officer Manpower and 75 P. R. Milch Personnel Planning R. A. Weitzman D. R. Barr C3 Technology...Procedures D. P. Gaver Modeling and Influence of Informa- 83 tion on the Progress of Conflict or Combat by Mathematical and Compu- tational Methods D. P. Gaver

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.

Acute Diarrheal Syndromic Surveillance

PubMed Central

Kam, H.J.; Choi, S.; Cho, J.P.; Min, Y.G.; Park, R.W.

2010-01-01

Objective In an effort to identify and characterize the environmental factors that affect the number of patients with acute diarrheal (AD) syndrome, we developed and tested two regional surveillance models including holiday and weather information in addition to visitor records, at emergency medical facilities in the Seoul metropolitan area of Korea. Methods With 1,328,686 emergency department visitor records from the National Emergency Department Information system (NEDIS) and the holiday and weather information, two seasonal ARIMA models were constructed: (1) The simple model (only with total patient number), (2) the environmental factor-added model. The stationary R-squared was utilized as an in-sample model goodness-of-fit statistic for the constructed models, and the cumulative mean of the Mean Absolute Percentage Error (MAPE) was used to measure post-sample forecast accuracy over the next 1 month. Results The (1,0,1)(0,1,1)7 ARIMA model resulted in an adequate model fit for the daily number of AD patient visits over 12 months for both cases. Among various features, the total number of patient visits was selected as a commonly influential independent variable. Additionally, for the environmental factor-added model, holidays and daily precipitation were selected as features that statistically significantly affected model fitting. Stationary R-squared values were changed in a range of 0.651-0.828 (simple), and 0.805-0.844 (environmental factor-added) with p<0.05. In terms of prediction, the MAPE values changed within 0.090-0.120 and 0.089-0.114, respectively. Conclusion The environmental factor-added model yielded better MAPE values. Holiday and weather information appear to be crucial for the construction of an accurate syndromic surveillance model for AD, in addition to the visitor and assessment records. PMID:23616829

Predicting Malaria occurrence in Southwest and North central Nigeria using Meteorological parameters

NASA Astrophysics Data System (ADS)

Akinbobola, A.; Omotosho, J. Bayo

2013-09-01

Malaria is a major public health problem especially in the tropics with the potential to significantly increase in response to changing weather and climate. This study explored the impact of weather and climate and its variability on the occurrence and transmission of malaria in Akure, the tropical rain forest area of southwest and Kaduna, in the savanna area of Nigeria. We investigate this supposition by looking at the relationship between rainfall, relative humidity, minimum and maximum temperature, and malaria at the two stations. This study uses monthly data of 7 years (2001-2007) for both meteorological data and record of reported cases of malaria infection. Autoregressive integrated moving average (ARIMA) models were used to evaluate the relationship between weather factors and malaria incidence. Of all the models tested, the ARIMA (1, 0, 1) model fits the malaria incidence data best for Akure and Kaduna according to normalized Bayesian information criterion (BIC) and goodness-of-fit criteria. Humidity and rainfall have almost the same trend of association in all the stations while maximum temperature share the same negative association at southwestern stations and positive in the northern station. Rainfall and humidity have a positive association with malaria incidence at lag of 1 month. In all, we found that minimum temperature is not a limiting factor for malaria transmission in Akure but otherwise in the other stations.

Forecasting of palm oil price in Malaysia using linear and nonlinear methods

NASA Astrophysics Data System (ADS)

Nor, Abu Hassan Shaari Md; Sarmidi, Tamat; Hosseinidoust, Ehsan

2014-09-01

The first question that comes to the mind is: "How can we predict the palm oil price accurately?" This question is the authorities, policy makers and economist's question for a long period of time. The first reason is that in the recent years Malaysia showed a comparative advantage in palm oil production and has become top producer and exporter in the world. Secondly, palm oil price plays significant role in government budget and represents important source of income for Malaysia, which potentially can influence the magnitude of monetary policies and eventually have an impact on inflation. Thirdly, knowledge on the future trends would be helpful in the planning and decision making procedures and will generate precise fiscal and monetary policy. Daily data on palm oil prices along with the ARIMA models, neural networks and fuzzy logic systems are employed in this paper. Empirical findings indicate that the dynamic neural network of NARX and the hybrid system of ANFIS provide higher accuracy than the ARIMA and static neural network for forecasting the palm oil price in Malaysia.

Effects of climatological parameters in modeling and forecasting seasonal influenza transmission in Abidjan, Cote d'Ivoire.

PubMed

N'gattia, A K; Coulibaly, D; Nzussouo, N Talla; Kadjo, H A; Chérif, D; Traoré, Y; Kouakou, B K; Kouassi, P D; Ekra, K D; Dagnan, N S; Williams, T; Tiembré, I

2016-09-13

In temperate regions, influenza epidemics occur in the winter and correlate with certain climatological parameters. In African tropical regions, the effects of climatological parameters on influenza epidemics are not well defined. This study aims to identify and model the effects of climatological parameters on seasonal influenza activity in Abidjan, Cote d'Ivoire. We studied the effects of weekly rainfall, humidity, and temperature on laboratory-confirmed influenza cases in Abidjan from 2007 to 2010. We used the Box-Jenkins method with the autoregressive integrated moving average (ARIMA) process to create models using data from 2007-2010 and to assess the predictive value of best model on data from 2011 to 2012. The weekly number of influenza cases showed significant cross-correlation with certain prior weeks for both rainfall, and relative humidity. The best fitting multivariate model (ARIMAX (2,0,0) _RF) included the number of influenza cases during 1-week and 2-weeks prior, and the rainfall during the current week and 5-weeks prior. The performance of this model showed an increase of >3 % for Akaike Information Criterion (AIC) and 2.5 % for Bayesian Information Criterion (BIC) compared to the reference univariate ARIMA (2,0,0). The prediction of the weekly number of influenza cases during 2011-2012 with the best fitting multivariate model (ARIMAX (2,0,0) _RF), showed that the observed values were within the 95 % confidence interval of the predicted values during 97 of 104 weeks. Including rainfall increases the performances of fitted and predicted models. The timing of influenza in Abidjan can be partially explained by rainfall influence, in a setting with little change in temperature throughout the year. These findings can help clinicians to anticipate influenza cases during the rainy season by implementing preventive measures.

Short-term prediction of rain attenuation level and volatility in Earth-to-Satellite links at EHF band

NASA Astrophysics Data System (ADS)

de Montera, L.; Mallet, C.; Barthès, L.; Golé, P.

2008-08-01

This paper shows how nonlinear models originally developed in the finance field can be used to predict rain attenuation level and volatility in Earth-to-Satellite links operating at the Extremely High Frequencies band (EHF, 20 50 GHz). A common approach to solving this problem is to consider that the prediction error corresponds only to scintillations, whose variance is assumed to be constant. Nevertheless, this assumption does not seem to be realistic because of the heteroscedasticity of error time series: the variance of the prediction error is found to be time-varying and has to be modeled. Since rain attenuation time series behave similarly to certain stocks or foreign exchange rates, a switching ARIMA/GARCH model was implemented. The originality of this model is that not only the attenuation level, but also the error conditional distribution are predicted. It allows an accurate upper-bound of the future attenuation to be estimated in real time that minimizes the cost of Fade Mitigation Techniques (FMT) and therefore enables the communication system to reach a high percentage of availability. The performance of the switching ARIMA/GARCH model was estimated using a measurement database of the Olympus satellite 20/30 GHz beacons and this model is shown to outperform significantly other existing models. The model also includes frequency scaling from the downlink frequency to the uplink frequency. The attenuation effects (gases, clouds and rain) are first separated with a neural network and then scaled using specific scaling factors. As to the resulting uplink prediction error, the error contribution of the frequency scaling step is shown to be larger than that of the downlink prediction, indicating that further study should focus on improving the accuracy of the scaling factor.

Time series models for prediction the total and dissolved heavy metals concentration in road runoff and soil solution of roadside embankments

NASA Astrophysics Data System (ADS)

Aljoumani, Basem; Kluge, Björn; sanchez, Josep; Wessolek, Gerd

2017-04-01

Highways and main roads are potential sources of contamination for the surrounding environment. High traffic rates result in elevated heavy metal concentrations in road runoff, soil and water seepage, which has attracted much attention in the recent past. Prediction of heavy metals transfer near the roadside into deeper soil layers are very important to prevent the groundwater pollution. This study was carried out on data of a number of lysimeters which were installed along the A115 highway (Germany) with a mean daily traffic of 90.000 vehicles per day. Three polyethylene (PE) lysimeters were installed at the A115 highway. They have the following dimensions: length 150 cm, width 100 cm, height 60 cm. The lysimeters were filled with different soil materials, which were recently used for embankment construction in Germany. With the obtained data, we will develop a time series analysis model to predict total and dissolved metal concentration in road runoff and in soil solution of the roadside embankments. The time series consisted of monthly measurements of heavy metals and was transformed to a stationary situation. Subsequently, the transformed data will be used to conduct analyses in the time domain in order to obtain the parameters of a seasonal autoregressive integrated moving average (ARIMA) model. Four phase approaches for identifying and fitting ARIMA models will be used: identification, parameter estimation, diagnostic checking, and forecasting. An automatic selection criterion, such as the Akaike information criterion, will use to enhance this flexible approach to model building

The reduced transition probabilities for excited states of rare-earths and actinide even-even nuclei

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

Ghumman, S. S.

The theoretical B(E2) ratios have been calculated on DF, DR and Krutov models. A simple method based on the work of Arima and Iachello is used to calculate the reduced transition probabilities within SU(3) limit of IBA-I framework. The reduced E2 transition probabilities from second excited states of rare-earths and actinide even–even nuclei calculated from experimental energies and intensities from recent data, have been found to compare better with those calculated on the Krutov model and the SU(3) limit of IBA than the DR and DF models.

Statistical description of turbulent transport for flux driven toroidal plasmas

NASA Astrophysics Data System (ADS)

Anderson, J.; Imadera, K.; Kishimoto, Y.; Li, J. Q.; Nordman, H.

2017-06-01

A novel methodology to analyze non-Gaussian probability distribution functions (PDFs) of intermittent turbulent transport in global full-f gyrokinetic simulations is presented. In this work, the auto-regressive integrated moving average (ARIMA) model is applied to time series data of intermittent turbulent heat transport to separate noise and oscillatory trends, allowing for the extraction of non-Gaussian features of the PDFs. It was shown that non-Gaussian tails of the PDFs from first principles based gyrokinetic simulations agree with an analytical estimation based on a two fluid model.

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

Voynikova, D. S., E-mail: desi-sl2000@yahoo.com; Gocheva-Ilieva, S. G., E-mail: snegocheva@yahoo.com; Ivanov, A. V., E-mail: aivanov-99@yahoo.com

Numerous time series methods are used in environmental sciences allowing the detailed investigation of air pollution processes. The goal of this study is to present the empirical analysis of various aspects of stochastic modeling and in particular the ARIMA/SARIMA methods. The subject of investigation is air pollution in the town of Kardzhali, Bulgaria with 2 problematic pollutants – sulfur dioxide (SO2) and particulate matter (PM10). Various SARIMA Transfer Function models are built taking into account meteorological factors, data transformations and the use of different horizons selected to predict future levels of concentrations of the pollutants.

Time series analysis of influenza incidence in Chinese provinces from 2004 to 2011

PubMed Central

Song, Xin; Xiao, Jun; Deng, Jiang; Kang, Qiong; Zhang, Yanyu; Xu, Jinbo

2016-01-01

Abstract Influenza as a severe infectious disease has caused catastrophes throughout human history, and every pandemic of influenza has produced a great social burden. We compiled monthly data of influenza incidence from all provinces and autonomous regions in mainland China from January 2004 to December 2011, comprehensively evaluated and classified these data, and then randomly selected 4 provinces with higher incidence (Hebei, Gansu, Guizhou, and Hunan), 2 provinces with median incidence (Tianjin and Henan), 1 province with lower incidence (Shandong), using time series analysis to construct an ARIMA model, which is based on the monthly incidence from 2004 to 2011 as the training set. We exerted the X-12-ARIMA procedure for modeling due to the seasonality these data implied. Autocorrelation function (ACF), partial autocorrelation function (PACF), and automatic model selection were to determine the order of the model parameters. The optimal model was decided by a nonseasonal and seasonal moving average test. Finally, we applied this model to predict the monthly incidence of influenza in 2012 as the test set, and the simulated incidence was compared with the observed incidence to evaluate the model's validity by the criterion of both percentage variability in regression analyses (R2) and root mean square error (RMSE). It is conceivable that SARIMA (0,1,1)(0,1,1)12 could simultaneously forecast the influenza incidence of the Hebei Province, Guizhou Province, Henan Province, and Shandong Province; SARIMA (1,0,0)(0,1,1)12 could forecast the influenza incidence in Gansu Province; SARIMA (3,1,1)(0,1,1)12 could forecast the influenza incidence in Tianjin City; and SARIMA (0,1,1)(0,0,1)12 could forecast the influenza incidence in Hunan Province. Time series analysis is a good tool for prediction of disease incidence. PMID:27367989

Temporal patterns and forecast of dengue infection in Northeastern Thailand.

PubMed

Silawan, Tassanee; Singhasivanon, Pratap; Kaewkungwal, Jaranit; Nimmanitya, Suchitra; Suwonkerd, Wanapa

2008-01-01

This study aimed to determine temporal patterns and develop a forecasting model for dengue incidence in northeastern Thailand. Reported cases were obtained from the Thailand national surveillance system. The temporal patterns were displayed by plotting monthly rates, the seasonal-trend decomposition procedure based on loess (STL) was performed using R 2.2.1 software, and the trend was assessed using Poisson regression. The forecasting model for dengue incidence was performed in R 2.2.1 and Intercooled Stata 9.2 using the seasonal Autoregressive Integrated Moving Average (ARIMA) model. The model was evaluated by comparing predicted versus actual rates of dengue for 1996 to 2005 and used to forecast monthly rates during January to December 2006. The results reveal that epidemics occurred every two years, with approximately three years per epidemic, and that the next epidemic will take place in 2006 to 2008. It was found that if a month increased, the rate ratio for dengue infection decreased by a factor 0.9919 for overall region and 0.9776 to 0.9984 for individual provinces. The amplitude of the peak, which was evident in June or July, was 11.32 to 88.08 times greater than the rest of the year. The seasonal ARIMA (2, 1, 0) (0, 1, 1)12 model was model with the best fit for regionwide data of total dengue incidence whereas the models with the best fit varied by province. The forecasted regional monthly rates during January to December 2006 should range from 0.27 to 17.89 per 100,000 population. The peak for 2006 should be much higher than the peak for 2005. The highest peaks in 2006 should be in Loei, Buri Ram, Surin, Nakhon Phanom, and Ubon Ratchathani Provinces.

An Evaluation of the Application of ISD to P-3 Pilot Training.

DTIC Science & Technology

1980-06-01

THESIS AN EVALUATION OF THE APPLICATION OF ISD TO P-3 PILOT TRAINING by William Allen. Snider June 1980 Thesis Advisor: J. K. Arima Approved for...also from quasi-governmental agencies such as RAND and HUMRRO, saw great potential in the use of techniques such as modelling to increase the precision...scores. The preceding discussion suggests that organizational climate , as well as experience, is strongly related to pilot performance on NATOPS

Spatio-Temporal Trends and Risk Factors for Shigella from 2001 to 2011 in Jiangsu Province, People's Republic of China

PubMed Central

Bao, Changjun; Hu, Jianli; Liu, Wendong; Liang, Qi; Wu, Ying; Norris, Jessie; Peng, Zhihang; Yu, Rongbin; Shen, Hongbing; Chen, Feng

2014-01-01

Objective This study aimed to describe the spatial and temporal trends of Shigella incidence rates in Jiangsu Province, People's Republic of China. It also intended to explore complex risk modes facilitating Shigella transmission. Methods County-level incidence rates were obtained for analysis using geographic information system (GIS) tools. Trend surface and incidence maps were established to describe geographic distributions. Spatio-temporal cluster analysis and autocorrelation analysis were used for detecting clusters. Based on the number of monthly Shigella cases, an autoregressive integrated moving average (ARIMA) model successfully established a time series model. A spatial correlation analysis and a case-control study were conducted to identify risk factors contributing to Shigella transmissions. Results The far southwestern and northwestern areas of Jiangsu were the most infected. A cluster was detected in southwestern Jiangsu (LLR = 11674.74, P<0.001). The time series model was established as ARIMA (1, 12, 0), which predicted well for cases from August to December, 2011. Highways and water sources potentially caused spatial variation in Shigella development in Jiangsu. The case-control study confirmed not washing hands before dinner (OR = 3.64) and not having access to a safe water source (OR = 2.04) as the main causes of Shigella in Jiangsu Province. Conclusion Improvement of sanitation and hygiene should be strengthened in economically developed counties, while access to a safe water supply in impoverished areas should be increased at the same time. PMID:24416167

0.1 Trend analysis of δ18O composition of precipitation in Germany: Combining Mann-Kendall trend test and ARIMA models to correct for higher order serial correlation

NASA Astrophysics Data System (ADS)

Klaus, Julian; Pan Chun, Kwok; Stumpp, Christine

2015-04-01

Spatio-temporal dynamics of stable oxygen (18O) and hydrogen (2H) isotopes in precipitation can be used as proxies for changing hydro-meteorological and regional and global climate patterns. While spatial patterns and distributions gained much attention in recent years the temporal trends in stable isotope time series are rarely investigated and our understanding of them is still limited. These might be a result of a lack of proper trend detection tools and effort for exploring trend processes. Here we make use of an extensive data set of stable isotope in German precipitation. In this study we investigate temporal trends of δ18O in precipitation at 17 observation station in Germany between 1978 and 2009. For that we test different approaches for proper trend detection, accounting for first and higher order serial correlation. We test if significant trends in the isotope time series based on different models can be observed. We apply the Mann-Kendall trend tests on the isotope series, using general multiplicative seasonal autoregressive integrate moving average (ARIMA) models which account for first and higher order serial correlations. With the approach we can also account for the effects of temperature, precipitation amount on the trend. Further we investigate the role of geographic parameters on isotope trends. To benchmark our proposed approach, the ARIMA results are compared to a trend-free prewhiting (TFPW) procedure, the state of the art method for removing the first order autocorrelation in environmental trend studies. Moreover, we explore whether higher order serial correlations in isotope series affects our trend results. The results show that three out of the 17 stations have significant changes when higher order autocorrelation are adjusted, and four stations show a significant trend when temperature and precipitation effects are considered. Significant trends in the isotope time series are generally observed at low elevation stations (≤315 m a.s.l.). Higher order autoregressive processes are important in the isotope time series analysis. Our results show that the widely used trend analysis with only the first order autocorrelation adjustment may not adequately take account of the high order autocorrelated processes in the stable isotope series. The investigated time series analysis method including higher autocorrelation and external climate variable adjustments is shown to be a better alternative.

Toward rigorous idiographic research in prevention science: comparison between three analytic strategies for testing preventive intervention in very small samples.

PubMed

Ridenour, Ty A; Pineo, Thomas Z; Maldonado Molina, Mildred M; Hassmiller Lich, Kristen

2013-06-01

Psychosocial prevention research lacks evidence from intensive within-person lines of research to understand idiographic processes related to development and response to intervention. Such data could be used to fill gaps in the literature and expand the study design options for prevention researchers, including lower-cost yet rigorous studies (e.g., for program evaluations), pilot studies, designs to test programs for low prevalence outcomes, selective/indicated/adaptive intervention research, and understanding of differential response to programs. This study compared three competing analytic strategies designed for this type of research: autoregressive moving average, mixed model trajectory analysis, and P-technique. Illustrative time series data were from a pilot study of an intervention for nursing home residents with diabetes (N = 4) designed to improve control of blood glucose. A within-person, intermittent baseline design was used. Intervention effects were detected using each strategy for the aggregated sample and for individual patients. The P-technique model most closely replicated observed glucose levels. ARIMA and P-technique models were most similar in terms of estimated intervention effects and modeled glucose levels. However, ARIMA and P-technique also were more sensitive to missing data, outliers and number of observations. Statistical testing suggested that results generalize both to other persons as well as to idiographic, longitudinal processes. This study demonstrated the potential contributions of idiographic research in prevention science as well as the need for simulation studies to delineate the research circumstances when each analytic approach is optimal for deriving the correct parameter estimates.

Toward Rigorous Idiographic Research in Prevention Science: Comparison Between Three Analytic Strategies for Testing Preventive Intervention in Very Small Samples

PubMed Central

Pineo, Thomas Z.; Maldonado Molina, Mildred M.; Lich, Kristen Hassmiller

2013-01-01

Psychosocial prevention research lacks evidence from intensive within-person lines of research to understand idiographic processes related to development and response to intervention. Such data could be used to fill gaps in the literature and expand the study design options for prevention researchers, including lower-cost yet rigorous studies (e.g., for program evaluations), pilot studies, designs to test programs for low prevalence outcomes, selective/indicated/ adaptive intervention research, and understanding of differential response to programs. This study compared three competing analytic strategies designed for this type of research: autoregressive moving average, mixed model trajectory analysis, and P-technique. Illustrative time series data were from a pilot study of an intervention for nursing home residents with diabetes (N=4) designed to improve control of blood glucose. A within-person, intermittent baseline design was used. Intervention effects were detected using each strategy for the aggregated sample and for individual patients. The P-technique model most closely replicated observed glucose levels. ARIMA and P-technique models were most similar in terms of estimated intervention effects and modeled glucose levels. However, ARIMA and P-technique also were more sensitive to missing data, outliers and number of observations. Statistical testing suggested that results generalize both to other persons as well as to idiographic, longitudinal processes. This study demonstrated the potential contributions of idiographic research in prevention science as well as the need for simulation studies to delineate the research circumstances when each analytic approach is optimal for deriving the correct parameter estimates. PMID:23299558

Fuzzy inductive reasoning: a consolidated approach to data-driven construction of complex dynamical systems

NASA Astrophysics Data System (ADS)

Nebot, Àngela; Mugica, Francisco

2012-10-01

Fuzzy inductive reasoning (FIR) is a modelling and simulation methodology derived from the General Systems Problem Solver. It compares favourably with other soft computing methodologies, such as neural networks, genetic or neuro-fuzzy systems, and with hard computing methodologies, such as AR, ARIMA, or NARMAX, when it is used to predict future behaviour of different kinds of systems. This paper contains an overview of the FIR methodology, its historical background, and its evolution.

Social media responses to heat waves.

PubMed

Jung, Jihoon; Uejio, Christopher K

2017-07-01

Social network services (SNSs) may benefit public health by augmenting surveillance and distributing information to the public. In this study, we collected Twitter data focusing on six different heat-related themes (air conditioning, cooling center, dehydration, electrical outage, energy assistance, and heat) for 182 days from May 7 to November 3, 2014. First, exploratory linear regression associated outdoor heat exposure to the theme-specific tweet counts for five study cities (Los Angeles, New York, Chicago, Houston, and Atlanta). Next, autoregressive integrated moving average (ARIMA) time series models formally associated heat exposure to the combined count of heat and air conditioning tweets while controlling for temporal autocorrelation. Finally, we examined the spatial and temporal distribution of energy assistance and cooling center tweets. The result indicates that the number of tweets in most themes exhibited a significant positive relationship with maximum temperature. The ARIMA model results suggest that each city shows a slightly different relationship between heat exposure and the tweet count. A one-degree change in the temperature correspondingly increased the Box-Cox transformed tweets by 0.09 for Atlanta, 0.07 for Los Angeles, and 0.01 for New York City. The energy assistance and cooling center theme tweets suggest that only a few municipalities used Twitter for public service announcements. The timing of the energy assistance tweets suggests that most jurisdictions provide heating instead of cooling energy assistance.

Spatiotemporal patterns of severe fever with thrombocytopenia syndrome in China, 2011-2016.

PubMed

Sun, Jimin; Lu, Liang; Wu, Haixia; Yang, Jun; Liu, Keke; Liu, Qiyong

2018-05-01

Severe fever with thrombocytopenia syndrome (SFTS) is emerging and the number of SFTS cases have increased year by year in China. However, spatiotemporal patterns and trends of SFTS are less clear up to date. In order to explore spatiotemporal patterns and predict SFTS incidences, we analyzed temporal trends of SFTS using autoregressive integrated moving average (ARIMA) model, spatial patterns, and spatiotemporal clusters of SFTS cases at the county level based on SFTS data in China during 2011-2016. We determined the optimal time series model was ARIMA (2, 0, 1) × (0, 0, 1) 12 which fitted the SFTS cases reasonably well during the training process and forecast process. In the spatial clustering analysis, the global autocorrelation suggested that SFTS cases were not of random distribution. Local spatial autocorrelation analysis of SFTS identified foci mainly concentrated in Hubei Province, Henan Province, Anhui Province, Shandong Province, Liaoning Province, and Zhejiang Province. A most likely cluster including 21 counties in Henan Province and Hubei Province was observed in the central region of China from April 2015 to August 2016. Our results will provide a sound evidence base for future prevention and control programs of SFTS such as allocation of the health resources, surveillance in high-risk regions, health education, improvement of diagnosis and so on. Copyright © 2018 Elsevier GmbH. All rights reserved.

Diffusive and subdiffusive dynamics of indoor microclimate: a time series modeling.

PubMed

Maciejewska, Monika; Szczurek, Andrzej; Sikora, Grzegorz; Wyłomańska, Agnieszka

2012-09-01

The indoor microclimate is an issue in modern society, where people spend about 90% of their time indoors. Temperature and relative humidity are commonly used for its evaluation. In this context, the two parameters are usually considered as behaving in the same manner, just inversely correlated. This opinion comes from observation of the deterministic components of temperature and humidity time series. We focus on the dynamics and the dependency structure of the time series of these parameters, without deterministic components. Here we apply the mean square displacement, the autoregressive integrated moving average (ARIMA), and the methodology for studying anomalous diffusion. The analyzed data originated from five monitoring locations inside a modern office building, covering a period of nearly one week. It was found that the temperature data exhibited a transition between diffusive and subdiffusive behavior, when the building occupancy pattern changed from the weekday to the weekend pattern. At the same time the relative humidity consistently showed diffusive character. Also the structures of the dependencies of the temperature and humidity data sets were different, as shown by the different structures of the ARIMA models which were found appropriate. In the space domain, the dynamics and dependency structure of the particular parameter were preserved. This work proposes an approach to describe the very complex conditions of indoor air and it contributes to the improvement of the representative character of microclimate monitoring.

Social media responses to heat waves

NASA Astrophysics Data System (ADS)

Jung, Jihoon; Uejio, Christopher K.

2017-07-01

Social network services (SNSs) may benefit public health by augmenting surveillance and distributing information to the public. In this study, we collected Twitter data focusing on six different heat-related themes (air conditioning, cooling center, dehydration, electrical outage, energy assistance, and heat) for 182 days from May 7 to November 3, 2014. First, exploratory linear regression associated outdoor heat exposure to the theme-specific tweet counts for five study cities (Los Angeles, New York, Chicago, Houston, and Atlanta). Next, autoregressive integrated moving average (ARIMA) time series models formally associated heat exposure to the combined count of heat and air conditioning tweets while controlling for temporal autocorrelation. Finally, we examined the spatial and temporal distribution of energy assistance and cooling center tweets. The result indicates that the number of tweets in most themes exhibited a significant positive relationship with maximum temperature. The ARIMA model results suggest that each city shows a slightly different relationship between heat exposure and the tweet count. A one-degree change in the temperature correspondingly increased the Box-Cox transformed tweets by 0.09 for Atlanta, 0.07 for Los Angeles, and 0.01 for New York City. The energy assistance and cooling center theme tweets suggest that only a few municipalities used Twitter for public service announcements. The timing of the energy assistance tweets suggests that most jurisdictions provide heating instead of cooling energy assistance.

Text Authorship Identified Using the Dynamics of Word Co-Occurrence Networks

PubMed Central

Akimushkin, Camilo; Amancio, Diego Raphael; Oliveira, Osvaldo Novais

2017-01-01

Automatic identification of authorship in disputed documents has benefited from complex network theory as this approach does not require human expertise or detailed semantic knowledge. Networks modeling entire books can be used to discriminate texts from different sources and understand network growth mechanisms, but only a few studies have probed the suitability of networks in modeling small chunks of text to grasp stylistic features. In this study, we introduce a methodology based on the dynamics of word co-occurrence networks representing written texts to classify a corpus of 80 texts by 8 authors. The texts were divided into sections with equal number of linguistic tokens, from which time series were created for 12 topological metrics. Since 73% of all series were stationary (ARIMA(p, 0, q)) and the remaining were integrable of first order (ARIMA(p, 1, q)), probability distributions could be obtained for the global network metrics. The metrics exhibit bell-shaped non-Gaussian distributions, and therefore distribution moments were used as learning attributes. With an optimized supervised learning procedure based on a nonlinear transformation performed by Isomap, 71 out of 80 texts were correctly classified using the K-nearest neighbors algorithm, i.e. a remarkable 88.75% author matching success rate was achieved. Hence, purely dynamic fluctuations in network metrics can characterize authorship, thus paving the way for a robust description of large texts in terms of small evolving networks. PMID:28125703

Evaluation of Decision Rules in a Tiered Assessment of Inhalation Exposure to Nanomaterials.

PubMed

Brouwer, Derk; Boessen, Ruud; van Duuren-Stuurman, Birgit; Bard, Delphine; Moehlmann, Carsten; Bekker, Cindy; Fransman, Wouter; Klein Entink, Rinke

2016-10-01

Tiered or stepwise approaches to assess occupational exposure to nano-objects, and their agglomerates and aggregates have been proposed, which require decision rules (DRs) to move to a next tier, or terminate the assessment. In a desk study the performance of a number of DRs based on the evaluation of results from direct reading instruments was investigated by both statistical simulations and the application of the DRs to real workplace data sets. A statistical model that accounts for autocorrelation patterns in time-series, i.e. autoregressive integrated moving average (ARIMA), was used as 'gold' standard. The simulations showed that none of the proposed DRs covered the entire range of simulated scenarios with respect to the ARIMA model parameters, however, a combined DR showed a slightly better agreement. Application of the DRs to real workplace datasets (n = 117) revealed sensitivity up to 0.72, whereas the lowest observed specificity was 0.95. The selection of the most appropriate DR is very much dependent on the consequences of the decision, i.e. ruling in or ruling out of scenarios for further evaluation. Since a basic assessment may also comprise of other type of measurements and information, an evaluation logic was proposed which embeds the DRs, but furthermore supports decision making in view of a tiered-approach exposure assessment. © The Author 2016. Published by Oxford University Press on behalf of the British Occupational Hygiene Society.

Forecasting daily meteorological time series using ARIMA and regression models

NASA Astrophysics Data System (ADS)

Murat, Małgorzata; Malinowska, Iwona; Gos, Magdalena; Krzyszczak, Jaromir

2018-04-01

The daily air temperature and precipitation time series recorded between January 1, 1980 and December 31, 2010 in four European sites (Jokioinen, Dikopshof, Lleida and Lublin) from different climatic zones were modeled and forecasted. In our forecasting we used the methods of the Box-Jenkins and Holt- Winters seasonal auto regressive integrated moving-average, the autoregressive integrated moving-average with external regressors in the form of Fourier terms and the time series regression, including trend and seasonality components methodology with R software. It was demonstrated that obtained models are able to capture the dynamics of the time series data and to produce sensible forecasts.

Quantitative analysis of ions in spring water in three different areas of Hyogo Prefecture in Japan by far ultraviolet spectroscopy.

PubMed

Mitsuoka, Motoki; Shinzawa, Hideyuki; Morisawa, Yusuke; Kariyama, Naomi; Higashi, Noboru; Tsuboi, Motohiro; Ozaki, Yukihiro

2011-01-01

Far-ultraviolet (FUV) spectra in the 190-300 nm region were measured for spring water in Awaji-Akashi area, Tamba area and Rokko-Arima area in Hyogo Prefecture, Japan, these areas have quite different geology features. The spectra of the spring water in the Awaji-Akashi area can be divided into two groups: the spring water samples containing large amounts of NO(3)(-) and/or Cl(-), and those containing only small amounts of NO(3)(-) and Cl(-). The former shows a saturated band below 190 nm due to NO(3)(-) and/or Cl(-). These two types of spectra correspond to different lithological areas: sedimentary lithology near the sea shore containing many ions in the seawater and gravitic lithology far from the sea side, in the Awaji-Akashi area. The spring water from the Tamba area, which is far from the sea, contains relatively small amounts of NO(3)(-) and Cl(-); it does not yield a strong band in the region observed. The FUV spectra of three of four kinds of spring water samples in the Arima Hotspring show characteristic spectral patterns. They are quite different from the spectra of the spring water samples of the Rokko area. Calibration models were developed for NO(3)(-), Cl(-), SO(4)(2-), Na(+), and Mg(2+) in the nine kinds of spring water collected in the Awaji-Akashi area, Tamba, and Rokko-Arima area by using univariate analysis of the first derivative spectra and the actual values obtained by ion chromatography. NO(3)(-) yields the best results: correlation coefficient of 0.999 and standard deviation of 0.09 ppm with the wavelength of 212 nm. Cl(-) also gives good results: correlation coefficient of 0.993 and standard deviation of 0.5 ppm with the wavelength of 192 nm.

JPRS Report, Arms Control

DTIC Science & Technology

1989-10-20

to them. This vast land has only very few people and its climate is mild. With a total area of over 100,000 square km, it is larger than Zhejiang...Mandarin 1030 GMT 17 Sep 89 ["Roster of Heroes and Model Workers"—on Sun Jingliang, chief designer of Long March Carrier Rocket IV, member of the...the building of new housing for U.S. forces and the refurbishing of present ones. Tatsu Arima , director general of the Foreign Ministry’s North

Quantifying and Reducing Uncertainty in Correlated Multi-Area Short-Term Load Forecasting

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

Sun, Yannan; Hou, Zhangshuan; Meng, Da

2016-07-17

In this study, we represent and reduce the uncertainties in short-term electric load forecasting by integrating time series analysis tools including ARIMA modeling, sequential Gaussian simulation, and principal component analysis. The approaches are mainly focusing on maintaining the inter-dependency between multiple geographically related areas. These approaches are applied onto cross-correlated load time series as well as their forecast errors. Multiple short-term prediction realizations are then generated from the reduced uncertainty ranges, which are useful for power system risk analyses.

Graph-based structural change detection for rotating machinery monitoring

NASA Astrophysics Data System (ADS)

Lu, Guoliang; Liu, Jie; Yan, Peng

2018-01-01

Detection of structural changes is critically important in operational monitoring of a rotating machine. This paper presents a novel framework for this purpose, where a graph model for data modeling is adopted to represent/capture statistical dynamics in machine operations. Meanwhile we develop a numerical method for computing temporal anomalies in the constructed graphs. The martingale-test method is employed for the change detection when making decisions on possible structural changes, where excellent performance is demonstrated outperforming exciting results such as the autoregressive-integrated-moving average (ARIMA) model. Comprehensive experimental results indicate good potentials of the proposed algorithm in various engineering applications. This work is an extension of a recent result (Lu et al., 2017).

Adaptive Resource Utilization Prediction System for Infrastructure as a Service Cloud.

PubMed

Zia Ullah, Qazi; Hassan, Shahzad; Khan, Gul Muhammad

2017-01-01

Infrastructure as a Service (IaaS) cloud provides resources as a service from a pool of compute, network, and storage resources. Cloud providers can manage their resource usage by knowing future usage demand from the current and past usage patterns of resources. Resource usage prediction is of great importance for dynamic scaling of cloud resources to achieve efficiency in terms of cost and energy consumption while keeping quality of service. The purpose of this paper is to present a real-time resource usage prediction system. The system takes real-time utilization of resources and feeds utilization values into several buffers based on the type of resources and time span size. Buffers are read by R language based statistical system. These buffers' data are checked to determine whether their data follows Gaussian distribution or not. In case of following Gaussian distribution, Autoregressive Integrated Moving Average (ARIMA) is applied; otherwise Autoregressive Neural Network (AR-NN) is applied. In ARIMA process, a model is selected based on minimum Akaike Information Criterion (AIC) values. Similarly, in AR-NN process, a network with the lowest Network Information Criterion (NIC) value is selected. We have evaluated our system with real traces of CPU utilization of an IaaS cloud of one hundred and twenty servers.

Adaptive Resource Utilization Prediction System for Infrastructure as a Service Cloud

PubMed Central

Hassan, Shahzad; Khan, Gul Muhammad

2017-01-01

Infrastructure as a Service (IaaS) cloud provides resources as a service from a pool of compute, network, and storage resources. Cloud providers can manage their resource usage by knowing future usage demand from the current and past usage patterns of resources. Resource usage prediction is of great importance for dynamic scaling of cloud resources to achieve efficiency in terms of cost and energy consumption while keeping quality of service. The purpose of this paper is to present a real-time resource usage prediction system. The system takes real-time utilization of resources and feeds utilization values into several buffers based on the type of resources and time span size. Buffers are read by R language based statistical system. These buffers' data are checked to determine whether their data follows Gaussian distribution or not. In case of following Gaussian distribution, Autoregressive Integrated Moving Average (ARIMA) is applied; otherwise Autoregressive Neural Network (AR-NN) is applied. In ARIMA process, a model is selected based on minimum Akaike Information Criterion (AIC) values. Similarly, in AR-NN process, a network with the lowest Network Information Criterion (NIC) value is selected. We have evaluated our system with real traces of CPU utilization of an IaaS cloud of one hundred and twenty servers. PMID:28811819

Nonparametric Stochastic Model for Uncertainty Quantifi cation of Short-term Wind Speed Forecasts

NASA Astrophysics Data System (ADS)

AL-Shehhi, A. M.; Chaouch, M.; Ouarda, T.

2014-12-01

Wind energy is increasing in importance as a renewable energy source due to its potential role in reducing carbon emissions. It is a safe, clean, and inexhaustible source of energy. The amount of wind energy generated by wind turbines is closely related to the wind speed. Wind speed forecasting plays a vital role in the wind energy sector in terms of wind turbine optimal operation, wind energy dispatch and scheduling, efficient energy harvesting etc. It is also considered during planning, design, and assessment of any proposed wind project. Therefore, accurate prediction of wind speed carries a particular importance and plays significant roles in the wind industry. Many methods have been proposed in the literature for short-term wind speed forecasting. These methods are usually based on modeling historical fixed time intervals of the wind speed data and using it for future prediction. The methods mainly include statistical models such as ARMA, ARIMA model, physical models for instance numerical weather prediction and artificial Intelligence techniques for example support vector machine and neural networks. In this paper, we are interested in estimating hourly wind speed measures in United Arab Emirates (UAE). More precisely, we predict hourly wind speed using a nonparametric kernel estimation of the regression and volatility functions pertaining to nonlinear autoregressive model with ARCH model, which includes unknown nonlinear regression function and volatility function already discussed in the literature. The unknown nonlinear regression function describe the dependence between the value of the wind speed at time t and its historical data at time t -1, t - 2, … , t - d. This function plays a key role to predict hourly wind speed process. The volatility function, i.e., the conditional variance given the past, measures the risk associated to this prediction. Since the regression and the volatility functions are supposed to be unknown, they are estimated using nonparametric kernel methods. In addition, to the pointwise hourly wind speed forecasts, a confidence interval is also provided which allows to quantify the uncertainty around the forecasts.

Simple model for deriving sdg interacting boson model Hamiltonians: 150Nd example

NASA Astrophysics Data System (ADS)

Devi, Y. D.; Kota, V. K. B.

1993-07-01

A simple and yet useful model for deriving sdg interacting boson model (IBM) Hamiltonians is to assume that single-boson energies derive from identical particle (pp and nn) interactions and proton, neutron single-particle energies, and that the two-body matrix elements for bosons derive from pn interaction, with an IBM-2 to IBM-1 projection of the resulting p-n sdg IBM Hamiltonian. The applicability of this model in generating sdg IBM Hamiltonians is demonstrated, using a single-j-shell Otsuka-Arima-Iachello mapping of the quadrupole and hexadecupole operators in proton and neutron spaces separately and constructing a quadrupole-quadrupole plus hexadecupole-hexadecupole Hamiltonian in the analysis of the spectra, B(E2)'s, and E4 strength distribution in the example of 150Nd.

Statistical modeling of the long-range-dependent structure of barrier island framework geology and surface geomorphology

NASA Astrophysics Data System (ADS)

Weymer, Bradley A.; Wernette, Phillipe; Everett, Mark E.; Houser, Chris

2018-06-01

Shorelines exhibit long-range dependence (LRD) and have been shown in some environments to be described in the wave number domain by a power-law characteristic of scale independence. Recent evidence suggests that the geomorphology of barrier islands can, however, exhibit scale dependence as a result of systematic variations in the underlying framework geology. The LRD of framework geology, which influences island geomorphology and its response to storms and sea level rise, has not been previously examined. Electromagnetic induction (EMI) surveys conducted along Padre Island National Seashore (PAIS), Texas, United States, reveal that the EMI apparent conductivity (σa) signal and, by inference, the framework geology exhibits LRD at scales of up to 101 to 102 km. Our study demonstrates the utility of describing EMI σa and lidar spatial series by a fractional autoregressive integrated moving average (ARIMA) process that specifically models LRD. This method offers a robust and compact way of quantifying the geological variations along a barrier island shoreline using three statistical parameters (p, d, q). We discuss how ARIMA models that use a single parameter d provide a quantitative measure for determining free and forced barrier island evolutionary behavior across different scales. Statistical analyses at regional, intermediate, and local scales suggest that the geologic framework within an area of paleo-channels exhibits a first-order control on dune height. The exchange of sediment amongst nearshore, beach, and dune in areas outside this region are scale independent, implying that barrier islands like PAIS exhibit a combination of free and forced behaviors that affect the response of the island to sea level rise.

Comparison of staging diagnosis by two magnifying endoscopy classification for superficial oesophageal cancer.

PubMed

Ebi, Masahide; Shimura, Takaya; Murakami, Kenji; Yamada, Tomonori; Hirata, Yoshikazu; Tsukamoto, Hironobu; Mizoshita, Tsutomu; Tanida, Satoshi; Kataoka, Hiromi; Kamiya, Takeshi; Joh, Takashi

2012-11-01

Due to the possibility of lymph node metastasis, surgical resection is indicated for superficial oesophageal cancer with invasion to a depth greater than the muscularis mucosa. Although two magnifying endoscopy classifications are currently used to diagnose the depth of invasion, which classification is more suitable remains controversial. To compare and evaluate the clinical outcomes of two classifications for superficial oesophageal squamous cell carcinoma. This cross-sectional study consists of 44 superficial oesophageal squamous cell carcinoma lesions with magnification image-enhanced endoscopy images. Only magnifying endoscopic images were displayed to two experienced endoscopists who independently diagnosed the depth of invasion according to both classifications. The sensitivity of invasion greater than the muscularis mucosa tended to be higher in Inoue's classification than Arima's classification (78.3±6.2% vs. 50.0±3.0%; P=0.144), whereas the specificity was significantly lower in Inoue's classification than in Arima's classification (61.9±0.0% vs. 97.6±3.4%; P=0.043). For both classifications, rates of concordance were 90.9% and 84.4%, and κ statistics were 0.81 and 0.66, respectively. Our results suggest that Arima's classification is suitable for general screening before treatment to avoid unnecessary surgery. Inoue's classification is appropriate for assessing wide lesion. Copyright © 2012 Editrice Gastroenterologica Italiana S.r.l. Published by Elsevier Ltd. All rights reserved.

sdg Interacting boson hamiltonian in the seniority scheme

NASA Astrophysics Data System (ADS)

Yoshinaga, N.

1989-03-01

The sdg interacting boson hamiltonian is derived in the seniority scheme. We use the method of Otsuka, Arima and Iachello in order to derive the boson hamiltonian from the fermion hamiltonian. To examine how good is the boson approximation in the zeroth-order, we carry out the exact shell model calculations in a single j-shell. It is found that almost all low-lying levels are reproduced quite well by diagonalizing the sdg interacting boson hamiltonian in the vibrational case. In the deformed case the introduction of g-bosons improves the reproduction of the spectra and of the binding energies which are obtained by diagonalizing the exact shell model hamiltonian. In particular the sdg interacting boson model reproduces well-developed rotational bands.

Real-time reservoir operation considering non-stationary inflow prediction

NASA Astrophysics Data System (ADS)

Zhao, J.; Xu, W.; Cai, X.; Wang, Z.

2011-12-01

Stationarity of inflow has been a basic assumption for reservoir operation rule design, which is now facing challenges due to climate change and human interferences. This paper proposes a modeling framework to incorporate non-stationary inflow prediction for optimizing the hedging operation rule of large reservoirs with multiple-year flow regulation capacity. A multi-stage optimization model is formulated and a solution algorithm based on the optimality conditions is developed to incorporate non-stationary annual inflow prediction through a rolling, dynamic framework that updates the prediction from period to period and adopt the updated prediction in reservoir operation decision. The prediction model is ARIMA(4,1,0), in which parameter 4 stands for the order of autoregressive, 1 represents a linear trend, and 0 is the order of moving average. The modeling framework and solution algorithm is applied to the Miyun reservoir in China, determining a yearly operating schedule during the period from 1996 to 2009, during which there was a significant declining trend of reservoir inflow. Different operation policy scenarios are modeled, including standard operation policy (SOP, matching the current demand as much as possible), hedging rule (i.e., leaving a certain amount of water for future to avoid large risk of water deficit) with forecast from ARIMA (HR-1), hedging (HR) with perfect forecast (HR-2 ). Compared to the results of these scenarios to that of the actual reservoir operation (AO), the utility of the reservoir operation under HR-1 is 3.0% lower than HR-2, but 3.7% higher than the AO and 14.4% higher than SOP. Note that the utility under AO is 10.3% higher than that under SOP, which shows that a certain level of hedging under some inflow prediction or forecast was used in the real-world operation. Moreover, the impacts of discount rate and forecast uncertainty level on the operation will be discussed.

Impact of Ten-Valent Pneumococcal Conjugate Vaccine Introduction on Serotype Distribution Trends in Colombia: An Interrupted Time-Series Analysis

PubMed Central

Leal, Aura Lucia; Montañez, Anita Maria; Buitrago, Giancarlo; Patiño, Jaime; Camacho, German; Moreno, Vivian Marcela; Colombia, Red Neumo

2017-01-01

Abstract Background Trends in distribution of S. pneumoniae capsular serotypes are associated with the introduction of pneumococcal conjugate vaccines (PCV) among population. In Colombia, 10-valent PCV (PCV10) has been included in the national vaccination program since 2011. As a part of the pneumococcal surveillance network (SIREVA), Colombia has gathered data of serotype distribution since 1993. The aim of this work is to determine the effect of PCV10 introduction on non-coverage serotypes by PCV10 in Colombia, specifically, the effect on 6A, 19A and 3 serotypes. Methods Information was obtained from the national surveillance program since 1993 to 2016 in children under 5 years. The isolates came from sterile sites (blood, cerebrospinal fluid, pleural fluid, articular and peritoneal fluids). All the isolates were serotyping by National Institute of Health. An interrupted time series analysis was performed to determine the effect of the PCV10 introduction on the 6A, 19A and 3 serotypes (ARIMA model). Results Serotyping was performed in 4683 isolates. The annual proportion trend of the 6A, 19A and 3 serotypes remained constant until 2012. An increase of double in the serotype proportion trends was observed after 2012 (Figure). The interrupted time-series analysis showed a positive effect of the PCV10 introduction on trends of 19A and 3 serotypes, with coefficients 20.92 (P = 0.00, ARIMA(2,0,1)) and 6.32 (P = 0.00, ARIMA(2,1,1), respectively. There was no significant effect on 6A serotype trend. Conclusion The introduction of PCV10 in the national vaccination program in Colombia, affected the distribution of PVC 13 capsular types non included in the PCV 7 and PCV 10 in children under 5 years. This information emphasizes the importance to surveillance the changes in serotype distributions to guide prevention strategies in children under 5 years in Colombia. Figure. 1 Trends in distribution of serotypes 19A, 3 and 6A in children under 5 years. Colombia. Disclosures All authors: No reported disclosures.

Trends in Average Living Children at the Time of Terminal Contraception: A Time Series Analysis Over 27 Years Using ARIMA (p, d, q) Nonseasonal Model.

PubMed

Mumbare, Sachin S; Gosavi, Shriram; Almale, Balaji; Patil, Aruna; Dhakane, Supriya; Kadu, Aniruddha

2014-10-01

India's National Family Welfare Programme is dominated by sterilization, particularly tubectomy. Sterilization, being a terminal method of contraception, decides the final number of children for that couple. Many studies have shown the declining trend in the average number of living children at the time of sterilization over a short period of time. So this study was planned to do time series analysis of the average children at the time of terminal contraception, to do forecasting till 2020 for the same and to compare the rates of change in various subgroups of the population. Data was preprocessed in MS Access 2007 by creating and running SQL queries. After testing stationarity of every series with augmented Dickey-Fuller test, time series analysis and forecasting was done using best-fit Box-Jenkins ARIMA (p, d, q) nonseasonal model. To compare the rates of change of average children in various subgroups, at sterilization, analysis of covariance (ANCOVA) was applied. Forecasting showed that the replacement level of 2.1 total fertility rate (TFR) will be achieved in 2018 for couples opting for sterilization. The same will be achieved in 2020, 2016, 2018, and 2019 for rural area, urban area, Hindu couples, and Buddhist couples, respectively. It will not be achieved till 2020 in Muslim couples. Every stratum of population showed the declining trend. The decline for male children and in rural area was significantly faster than the decline for female children and in urban area, respectively. The decline was not significantly different in Hindu, Muslim, and Buddhist couples.

Modelling the influence of climate on malaria occurrence in Chimoio Municipality, Mozambique.

PubMed

Ferrão, João Luís; Mendes, Jorge M; Painho, Marco

2017-05-25

Mozambique was recently ranked fifth in the African continent for the number of cases of malaria. In Chimoio municipality cases of malaria are increasing annually, contrary to the decreasing trend in Africa. As malaria transmission is influenced to a large extent by climatic conditions, modelling this relationship can provide useful insights for designing precision health measures for malaria control. There is a scarcity of information on the association between climatic variability and malaria transmission risk in Mozambique in general, and in Chimoio in particular. Therefore, the aim of this study is to model the association between climatic variables and malaria cases on a weekly basis, to help policy makers find adequate measures for malaria control and eradication. Time series analysis was conducted using data on weekly climatic variables and weekly malaria cases (counts) in Chimoio municipality, from 2006 to 2014. All data were analysed using SPSS-20, R 3.3.2 and BioEstat 5.0. Cross-correlation analysis, linear processes, namely ARIMA models and regression modelling, were used to develop the final model. Between 2006 and 2014, 490,561 cases of malaria were recorded in Chimoio. Both malaria and climatic data exhibit weekly and yearly systematic fluctuations. Cross-correlation analysis showed that mean temperature and precipitation present significantly lagged correlations with malaria cases. An ARIMA model (2,1,0) (2,1,1) 52 , and a regression model for a Box-Cox transformed number of malaria cases with lags 1, 2 and 3 of weekly malaria cases and lags 6 and 7 of weekly mean temperature and lags 12 of precipitation were fitted. Although, both produced similar widths for prediction intervals, the last was able to anticipate malaria outbreak more accurately. The Chimoio climate seems ideal for malaria occurrence. Malaria occurrence peaks during January to March in Chimoio. As the lag effect between climatic events and malaria occurrence is important for the prediction of malaria cases, this can be used for designing public precision health measures. The model can be used for planning specific measures for Chimoio municipality. Prospective and multidisciplinary research involving researchers from different fields is welcomed to improve the effect of climatic factors and other factors in malaria cases.

Optimization of seasonal ARIMA models using differential evolution - simulated annealing (DESA) algorithm in forecasting dengue cases in Baguio City

NASA Astrophysics Data System (ADS)

Addawe, Rizavel C.; Addawe, Joel M.; Magadia, Joselito C.

2016-10-01

Accurate forecasting of dengue cases would significantly improve epidemic prevention and control capabilities. This paper attempts to provide useful models in forecasting dengue epidemic specific to the young and adult population of Baguio City. To capture the seasonal variations in dengue incidence, this paper develops a robust modeling approach to identify and estimate seasonal autoregressive integrated moving average (SARIMA) models in the presence of additive outliers. Since the least squares estimators are not robust in the presence of outliers, we suggest a robust estimation based on winsorized and reweighted least squares estimators. A hybrid algorithm, Differential Evolution - Simulated Annealing (DESA), is used to identify and estimate the parameters of the optimal SARIMA model. The method is applied to the monthly reported dengue cases in Baguio City, Philippines.

Reduction in Male Suicide Mortality Following the 2006 Russian Alcohol Policy: An Interrupted Time Series Analysis

PubMed Central

Chamlin, Mitchell B.; Andreev, Evgeny

2013-01-01

Objectives. We took advantage of a natural experiment to assess the impact on suicide mortality of a suite of Russian alcohol policies. Methods. We obtained suicide counts from anonymous death records collected by the Russian Federal State Statistics Service. We used autoregressive integrated moving average (ARIMA) interrupted time series techniques to model the effect of the alcohol policy (implemented in January 2006) on monthly male and female suicide counts between January 2000 and December 2010. Results. Monthly male and female suicide counts decreased during the period under study. Although the ARIMA analysis showed no impact of the policy on female suicide mortality, the results revealed an immediate and permanent reduction of about 9% in male suicides (Ln ω0 = −0.096; P = .01). Conclusions. Despite a recent decrease in mortality, rates of alcohol consumption and suicide in Russia remain among the highest in the world. Our analysis revealed that the 2006 alcohol policy in Russia led to a 9% reduction in male suicide mortality, meaning the policy was responsible for saving 4000 male lives annually that would otherwise have been lost to suicide. Together with recent similar findings elsewhere, our results suggest an important role for public health and other population level interventions, including alcohol policy, in reducing alcohol-related harm. PMID:24028249

Do drug-free workplace programs prevent occupational injuries? Evidence from Washington State.

PubMed

Wickizer, Thomas M; Kopjar, Branko; Franklin, Gary; Joesch, Jutta

2004-02-01

To evaluate the effect of a publicly sponsored drug-free workplace program on reducing the risk of occupational injuries. Workers' compensation claims data from the Washington State Department of Labor and Industries covering the period 1994 through 2000 and work-hours data reported by employers served as the data sources for the analysis. We used a pre-post design with a nonequivalent comparison group to assess the impact of the intervention on injury risk, measured in terms of differences in injury incidence rates. Two hundred and sixty-one companies that enrolled in the drug-free workplace program during the latter half of 1996 were compared with approximately 20,500 nonintervention companies. We tested autoregressive, integrated moving-average (ARIMA) models to assess the robustness of our findings. The drug-free workplace intervention was associated (p < .05) with a statistically significant decrease in injury rates for three industry groups: construction, manufacturing, and services. It was associated (p < .05) with a reduction in the incidence rate of more serious injuries involving four or more days of lost work time for two industry groups: construction and services. The ARIMA analysis supported The drug-free workplace program we studied was associated with a selective, industry-specific preventive effect. The strongest evidence of an intervention effect was for the construction industry. Estimated net cost savings for this industry were positive though small in magnitude.

Forecasting short-term data center network traffic load with convolutional neural networks.

PubMed

Mozo, Alberto; Ordozgoiti, Bruno; Gómez-Canaval, Sandra

2018-01-01

Efficient resource management in data centers is of central importance to content service providers as 90 percent of the network traffic is expected to go through them in the coming years. In this context we propose the use of convolutional neural networks (CNNs) to forecast short-term changes in the amount of traffic crossing a data center network. This value is an indicator of virtual machine activity and can be utilized to shape the data center infrastructure accordingly. The behaviour of network traffic at the seconds scale is highly chaotic and therefore traditional time-series-analysis approaches such as ARIMA fail to obtain accurate forecasts. We show that our convolutional neural network approach can exploit the non-linear regularities of network traffic, providing significant improvements with respect to the mean absolute and standard deviation of the data, and outperforming ARIMA by an increasingly significant margin as the forecasting granularity is above the 16-second resolution. In order to increase the accuracy of the forecasting model, we exploit the architecture of the CNNs using multiresolution input distributed among separate channels of the first convolutional layer. We validate our approach with an extensive set of experiments using a data set collected at the core network of an Internet Service Provider over a period of 5 months, totalling 70 days of traffic at the one-second resolution.

Forecasting short-term data center network traffic load with convolutional neural networks

PubMed Central

Ordozgoiti, Bruno; Gómez-Canaval, Sandra

2018-01-01

Efficient resource management in data centers is of central importance to content service providers as 90 percent of the network traffic is expected to go through them in the coming years. In this context we propose the use of convolutional neural networks (CNNs) to forecast short-term changes in the amount of traffic crossing a data center network. This value is an indicator of virtual machine activity and can be utilized to shape the data center infrastructure accordingly. The behaviour of network traffic at the seconds scale is highly chaotic and therefore traditional time-series-analysis approaches such as ARIMA fail to obtain accurate forecasts. We show that our convolutional neural network approach can exploit the non-linear regularities of network traffic, providing significant improvements with respect to the mean absolute and standard deviation of the data, and outperforming ARIMA by an increasingly significant margin as the forecasting granularity is above the 16-second resolution. In order to increase the accuracy of the forecasting model, we exploit the architecture of the CNNs using multiresolution input distributed among separate channels of the first convolutional layer. We validate our approach with an extensive set of experiments using a data set collected at the core network of an Internet Service Provider over a period of 5 months, totalling 70 days of traffic at the one-second resolution. PMID:29408936

Wavelet regression model in forecasting crude oil price

NASA Astrophysics Data System (ADS)

Hamid, Mohd Helmie; Shabri, Ani

2017-05-01

This study presents the performance of wavelet multiple linear regression (WMLR) technique in daily crude oil forecasting. WMLR model was developed by integrating the discrete wavelet transform (DWT) and multiple linear regression (MLR) model. The original time series was decomposed to sub-time series with different scales by wavelet theory. Correlation analysis was conducted to assist in the selection of optimal decomposed components as inputs for the WMLR model. The daily WTI crude oil price series has been used in this study to test the prediction capability of the proposed model. The forecasting performance of WMLR model were also compared with regular multiple linear regression (MLR), Autoregressive Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) using root mean square errors (RMSE) and mean absolute errors (MAE). Based on the experimental results, it appears that the WMLR model performs better than the other forecasting technique tested in this study.

Application of a Health Service Growth Model to Determine the Management Requirements of Health Service Providers.

DTIC Science & Technology

1980-09-01

ANAGEMENT REQUIREMENTS OF HEALTH SERVICE PROVIDERS by 1 Patrick Allen/Shannon "/ Sep t80"_ r Thesis Advisor: J. K . Arima Cx. Approved for public release...ASORES 12 . REPORT CATS Naval Postgraduate School September 1980 Monterey, California 93940 Is .019 fPAE 14 MONITORING AGENCY NAME 6 AOO.Ismne ..itt.en...Page 1) S/Ni 0 10 3-a14- AdoI SECUOITY CLASSIVICATION OP THIS PAGE ffte Sm. 00,011) k 4 * I:.uIRv Cl*, LlGGgVC&aONegu 0, TO#$ 04@gO 110#40 f_ tfef

Network bandwidth utilization forecast model on high bandwidth networks

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

Yoo, Wuchert; Sim, Alex

With the increasing number of geographically distributed scientific collaborations and the scale of the data size growth, it has become more challenging for users to achieve the best possible network performance on a shared network. We have developed a forecast model to predict expected bandwidth utilization for high-bandwidth wide area network. The forecast model can improve the efficiency of resource utilization and scheduling data movements on high-bandwidth network to accommodate ever increasing data volume for large-scale scientific data applications. Univariate model is developed with STL and ARIMA on SNMP path utilization data. Compared with traditional approach such as Box-Jenkins methodology,more » our forecast model reduces computation time by 83.2%. It also shows resilience against abrupt network usage change. The accuracy of the forecast model is within the standard deviation of the monitored measurements.« less

Network Bandwidth Utilization Forecast Model on High Bandwidth Network

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

Yoo, Wucherl; Sim, Alex

With the increasing number of geographically distributed scientific collaborations and the scale of the data size growth, it has become more challenging for users to achieve the best possible network performance on a shared network. We have developed a forecast model to predict expected bandwidth utilization for high-bandwidth wide area network. The forecast model can improve the efficiency of resource utilization and scheduling data movements on high-bandwidth network to accommodate ever increasing data volume for large-scale scientific data applications. Univariate model is developed with STL and ARIMA on SNMP path utilization data. Compared with traditional approach such as Box-Jenkins methodology,more » our forecast model reduces computation time by 83.2percent. It also shows resilience against abrupt network usage change. The accuracy of the forecast model is within the standard deviation of the monitored measurements.« less

Fuzzy Temporal Logic Based Railway Passenger Flow Forecast Model

PubMed Central

Dou, Fei; Jia, Limin; Wang, Li; Xu, Jie; Huang, Yakun

2014-01-01

Passenger flow forecast is of essential importance to the organization of railway transportation and is one of the most important basics for the decision-making on transportation pattern and train operation planning. Passenger flow of high-speed railway features the quasi-periodic variations in a short time and complex nonlinear fluctuation because of existence of many influencing factors. In this study, a fuzzy temporal logic based passenger flow forecast model (FTLPFFM) is presented based on fuzzy logic relationship recognition techniques that predicts the short-term passenger flow for high-speed railway, and the forecast accuracy is also significantly improved. An applied case that uses the real-world data illustrates the precision and accuracy of FTLPFFM. For this applied case, the proposed model performs better than the k-nearest neighbor (KNN) and autoregressive integrated moving average (ARIMA) models. PMID:25431586

An Adaptive Network-based Fuzzy Inference System for the detection of thermal and TEC anomalies around the time of the Varzeghan, Iran, (Mw = 6.4) earthquake of 11 August 2012

NASA Astrophysics Data System (ADS)

Akhoondzadeh, M.

2013-09-01

Anomaly detection is extremely important for forecasting the date, location and magnitude of an impending earthquake. In this paper, an Adaptive Network-based Fuzzy Inference System (ANFIS) has been proposed to detect the thermal and Total Electron Content (TEC) anomalies around the time of the Varzeghan, Iran, (Mw = 6.4) earthquake jolted in 11 August 2012 NW Iran. ANFIS is the famous hybrid neuro-fuzzy network for modeling the non-linear complex systems. In this study, also the detected thermal and TEC anomalies using the proposed method are compared to the results dealing with the observed anomalies by applying the classical and intelligent methods including Interquartile, Auto-Regressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN) and Support Vector Machine (SVM) methods. The duration of the dataset which is comprised from Aqua-MODIS Land Surface Temperature (LST) night-time snapshot images and also Global Ionospheric Maps (GIM), is 62 days. It can be shown that, if the difference between the predicted value using the ANFIS method and the observed value, exceeds the pre-defined threshold value, then the observed precursor value in the absence of non seismic effective parameters could be regarded as precursory anomaly. For two precursors of LST and TEC, the ANFIS method shows very good agreement with the other implemented classical and intelligent methods and this indicates that ANFIS is capable of detecting earthquake anomalies. The applied methods detected anomalous occurrences 1 and 2 days before the earthquake. This paper indicates that the detection of the thermal and TEC anomalies derive their credibility from the overall efficiencies and potentialities of the five integrated methods.

Active layer thermal monitoring at Fildes Peninsula, King George Island, Maritime Antarctica

NASA Astrophysics Data System (ADS)

Michel, R. F. M.; Schaefer, C. E. G. R.; Simas, F. N. B.; Francelino M., R.; Fernandes-Filho, E. I.; Lyra, G. B.; Bockheim, J. G.

2014-07-01

International attention to the climate change phenomena has grown in the last decade; the active layer and permafrost are of great importance in understanding processes and future trends due to their role in energy flux regulation. The objective of the this paper is to present active layer temperature data for one CALM-S site located at Fildes Peninsula, King George Island, Maritime Antarctica over an fifth seven month period (2008-2012). The monitoring site was installed during the summer of 2008 and consists of thermistors (accuracy of ± 0.2 °C), arranged vertically with probes at different depths, recording data at hourly intervals in a~high capacity data logger. A series of statistical analysis were performed to describe the soil temperature time series, including a linear fit in order to identify global trend and a series of autoregressive integrated moving average (ARIMA) models were tested in order to define the best fit for the data. The controls of weather on the thermal regime of the active layer have been identified, providing insights about the influence of climate chance over the permafrost. The active layer thermal regime in the studied period was typical of periglacial environment, with extreme variation at the surface during summer resulting in frequent freeze and thaw cycles. The active layer thickness (ALT) over the studied period showed variability related to different annual weather conditions, reaching a maximum of 117.5 cm in 2009. The ARIMA model was considered appropriate to treat the dataset, enabling more conclusive analysis and predictions when longer data sets are available. Despite the variability when comparing temperature readings and active layer thickness over the studied period, no warming trend was detected.

Active-layer thermal monitoring on the Fildes Peninsula, King George Island, maritime Antarctica

NASA Astrophysics Data System (ADS)

Michel, R. F. M.; Schaefer, C. E. G. R.; Simas, F. M. B.; Francelino, M. R.; Fernandes-Filho, E. I.; Lyra, G. B.; Bockheim, J. G.

2014-12-01

International attention to climate change phenomena has grown in the last decade; the active layer and permafrost are of great importance in understanding processes and future trends due to their role in energy flux regulation. The objective of this paper is to present active-layer temperature data for one Circumpolar Active Layer Monitoring South hemisphere (CALM-S) site located on the Fildes Peninsula, King George Island, maritime Antarctica over an 57-month period (2008-2012). The monitoring site was installed during the summer of 2008 and consists of thermistors (accuracy of ±0.2 °C), arranged vertically with probes at different depths, recording data at hourly intervals in a high-capacity data logger. A series of statistical analyses was performed to describe the soil temperature time series, including a linear fit in order to identify global trends, and a series of autoregressive integrated moving average (ARIMA) models was tested in order to define the best fit for the data. The affects of weather on the thermal regime of the active layer have been identified, providing insights into the influence of climate change on permafrost. The active-layer thermal regime in the studied period was typical of periglacial environments, with extreme variation in surface during the summer resulting in frequent freeze and thaw cycles. The active-layer thickness (ALT) over the studied period shows a degree of variability related to different annual weather conditions, reaching a maximum of 117.5 cm in 2009. The ARIMA model could describe the data adequately and is an important tool for more conclusive analysis and predictions when longer data sets are available. Despite the variability when comparing temperature readings and ACT over the studied period, no trend can be identified.

The impact of alcohol policies on alcohol-attributable diseases in Taiwan-A population-based study.

PubMed

Ying, Yung-Hsiang; Weng, Yung-Ching; Chang, Koyin

2017-11-01

Taiwan has some of the strictest alcohol-related driving laws in the world. However, its laws continue to be toughened to reduce the ever-increasing social cost of alcohol-related harm. This study assumes that alcohol-related driving laws show a spillover effect such that behavioral changes originally meant to apply behind the wheel come to affect drinking behavior in other contexts. The effects of alcohol driving laws and taxes on alcohol-related morbidity are assessed; incidence rates of alcohol-attributable diseases (AAD) serve as our measure of morbidity. Monthly incidence rates of alcohol-attributable diseases were calculated with data from the National Health Insurance Research Database (NHIRD) from 1996 to 2011. These rates were then submitted to intervention analyses using Seasonal Autoregressive Integrated Moving Average models (ARIMA) with multivariate adaptive regression splines (MARS). ARIMA is well-suited to time series analysis while MARS helps fit the regression model to the cubic curvature form of the irregular AAD incidence rates of hospitalization (AIRH). Alcoholic liver disease, alcohol abuse and dependence syndrome, and alcohol psychoses were the most common AADs in Taiwan. Compared to women, men had a higher incidence of AADs and their AIRH were more responsive to changes in the laws governing permissible blood alcohol. The adoption of tougher blood alcohol content (BAC) laws had significant effects on AADs, controlling for overall consumption of alcoholic beverages. Blood alcohol level laws and alcohol taxation effectively reduced alcohol-attributable morbidities with the exception of alcohol dependence and abuse, a disease to which middle-aged, lower income people are particularly susceptible. Attention should be focused on this cohort to protect this vulnerable population. Copyright © 2017 Elsevier B.V. All rights reserved.

Using time-series intervention analysis to understand U.S. Medicaid expenditures on antidepressant agents.

PubMed

Ferrand, Yann; Kelton, Christina M L; Guo, Jeff J; Levy, Martin S; Yu, Yan

2011-03-01

Medicaid programs' spending on antidepressants increased from $159 million in 1991 to $2 billion in 2005. The National Institute for Health Care Management attributed this expenditure growth to increases in drug utilization, entry of newer higher-priced antidepressants, and greater prescription drug insurance coverage. Rising enrollment in Medicaid has also contributed to this expenditure growth. This research examines the impact of specific events, including branded-drug and generic entry, a black box warning, direct-to-consumer advertising (DTCA), and new indication approval, on Medicaid spending on antidepressants. Using quarterly expenditure data for 1991-2005 from the national Medicaid pharmacy claims database maintained by the Centers for Medicare and Medicaid Services, a time-series autoregressive integrated moving average (ARIMA) intervention analysis was performed on 6 specific antidepressant drugs and on overall antidepressant spending. Twenty-nine potentially relevant interventions and their dates of occurrence were identified from the literature. Each was tested for an impact on the time series. Forecasts from the models were compared with a holdout sample of actual expenditure data. Interventions with significant impacts on Medicaid expenditures included the patent expiration of Prozac® (P<0.01) and the entry of generic paroxetine producers (P=0.04), which reduced expenditures on Prozac® and Paxil®, respectively, and the 1997 increase in DTCA (P=0.05), which increased spending on Wellbutrin®. Except for Paxil®, the ARIMA models had low prediction errors. Generic entry at the aggregate level did not lead to a reduction in overall expenditures (P>0.05), implying that the expanding market for antidepressants overwhelmed the effect of generic competition. Copyright © 2011 Elsevier Inc. All rights reserved.

A travel time forecasting model based on change-point detection method

NASA Astrophysics Data System (ADS)

LI, Shupeng; GUANG, Xiaoping; QIAN, Yongsheng; ZENG, Junwei

2017-06-01

Travel time parameters obtained from road traffic sensors data play an important role in traffic management practice. A travel time forecasting model is proposed for urban road traffic sensors data based on the method of change-point detection in this paper. The first-order differential operation is used for preprocessing over the actual loop data; a change-point detection algorithm is designed to classify the sequence of large number of travel time data items into several patterns; then a travel time forecasting model is established based on autoregressive integrated moving average (ARIMA) model. By computer simulation, different control parameters are chosen for adaptive change point search for travel time series, which is divided into several sections of similar state.Then linear weight function is used to fit travel time sequence and to forecast travel time. The results show that the model has high accuracy in travel time forecasting.

Climate variables as predictors for seasonal forecast of dengue occurrence in Chennai, Tamil Nadu

NASA Astrophysics Data System (ADS)

Subash Kumar, D. D.; Andimuthu, R.

2013-12-01

Background Dengue is a recently emerging vector borne diseases in Chennai. As per the WHO report in 2011 dengue is one of eight climate sensitive disease of this century. Objective Therefore an attempt has been made to explore the influence of climate parameters on dengue occurrence and use for forecasting. Methodology Time series analysis has been applied to predict the number of dengue cases in Chennai, a metropolitan city which is the capital of Tamil Nadu, India. Cross correlation of the climate variables with dengue cases revealed that the most influential parameters were monthly relative humidity, minimum temperature at 4 months lag and rainfall at one month lag (Table 1). However due to intercorrelation of relative humidity and rainfall was high and therefore for predictive purpose the rainfall at one month lag was used for the model development. Autoregressive Integrated Moving Average (ARIMA) models have been applied to forecast the occurrence of dengue. Results and Discussion The best fit model was ARIMA (1,0,1). It was seen that the monthly minimum temperature at four months lag (β= 3.612, p = 0.02) and rainfall at one month lag (β= 0.032, p = 0.017) were associated with dengue occurrence and they had a very significant effect. Mean Relative Humidity had a directly significant positive correlation at 99% confidence level, but the lagged effect was not prominent. The model predicted dengue cases showed significantly high correlation of 0.814(Figure 1) with the observed cases. The RMSE of the model was 18.564 and MAE was 12.114. The model is limited by the scarcity of the dataset. Inclusion of socioeconomic conditions and population offset are further needed to be incorporated for effective results. Conclusion Thus it could be claimed that the change in climatic parameters is definitely influential in increasing the number of dengue occurrence in Chennai. The climate variables therefore can be used for seasonal forecasting of dengue with rise in minimum temperature and rainfall at a city level. Table 1. Cross correlation of climate variables with dengue cases in Chennai ** p<0.01,*p<0.05

The endurance of the effects of the penalty point system in Spain three years after. Main influencing factors.

PubMed

Izquierdo, F Aparicio; Ramírez, B Arenas; McWilliams, J M Mira; Ayuso, J Páez

2011-05-01

In this work we have used ARIMA time series models to analyse the contribution of the penalty point system, the most important legislative measure for driving licences, in reducing the number of fatalities over 24h on the roads in Spain during the study period (January 1995 to June 2009). In addition, because of this long period of analysis, other control variables were introduced to model the enactment of the Reform of the Penal Code in December 2007, together with other more specific effects needed to fit the model correctly. The ARIMA intervention models methodology combines the basic features of specific times series models: it controls the trend and seasonal variation in data that is present when modelling the structure through autoregressive and moving average parameters and allows for inserting step or impulse input variables for checking and evaluating the effects of deterministic measures, such as legislative changes which are the object of study in this work. This paper analyses the surveillance and control measures introduced in the periods before and after the implementation of the penalty point system and helps to partly explain its apparent endurance over time. The results show that the introduction of the penalty point system in Spain had a very positive effect in reducing the number of fatalities (over 24h) on the road, and that this effect has endured up to the present time. This success may be due to the continuing increase in surveillance measures and fines as well the significantly growing interest shown by the news media in road safety since the measures were introduced. All this has led to positive changes in driver behaviour. It is, therefore, a combination of three factors: the penalty point system, the gradual stepping up of surveillance measures and sanctions, and the publicity given to road safety issues in the mass media would appear to be the key to success. The absence of any of these three factors would have predictably led to a far less positive evolution of the accident rate on Spanish roads. Copyright © 2010 Elsevier Ltd. All rights reserved.

Two levels ARIMAX and regression models for forecasting time series data with calendar variation effects

NASA Astrophysics Data System (ADS)

Suhartono, Lee, Muhammad Hisyam; Prastyo, Dedy Dwi

2015-12-01

The aim of this research is to develop a calendar variation model for forecasting retail sales data with the Eid ul-Fitr effect. The proposed model is based on two methods, namely two levels ARIMAX and regression methods. Two levels ARIMAX and regression models are built by using ARIMAX for the first level and regression for the second level. Monthly men's jeans and women's trousers sales in a retail company for the period January 2002 to September 2009 are used as case study. In general, two levels of calendar variation model yields two models, namely the first model to reconstruct the sales pattern that already occurred, and the second model to forecast the effect of increasing sales due to Eid ul-Fitr that affected sales at the same and the previous months. The results show that the proposed two level calendar variation model based on ARIMAX and regression methods yields better forecast compared to the seasonal ARIMA model and Neural Networks.

Historical Data Analysis of Hospital Discharges Related to the Amerithrax Attack in Florida

PubMed Central

Burke, Lauralyn K.; Brown, C. Perry; Johnson, Tammie M.

2016-01-01

Interrupted time-series analysis (ITSA) can be used to identify, quantify, and evaluate the magnitude and direction of an event on the basis of time-series data. This study evaluates the impact of the bioterrorist anthrax attacks (“Amerithrax”) on hospital inpatient discharges in the metropolitan statistical area of Palm Beach, Broward, and Miami-Dade counties in the fourth quarter of 2001. Three statistical methods—standardized incidence ratio (SIR), segmented regression, and an autoregressive integrated moving average (ARIMA)—were used to determine whether Amerithrax influenced inpatient utilization. The SIR found a non–statistically significant 2 percent decrease in hospital discharges. Although the segmented regression test found a slight increase in the discharge rate during the fourth quarter, it was also not statistically significant; therefore, it could not be attributed to Amerithrax. Segmented regression diagnostics preparing for ARIMA indicated that the quarterly data time frame was not serially correlated and violated one of the assumptions for the use of the ARIMA method and therefore could not properly evaluate the impact on the time-series data. Lack of data granularity of the time frames hindered the successful evaluation of the impact by the three analytic methods. This study demonstrates that the granularity of the data points is as important as the number of data points in a time series. ITSA is important for the ability to evaluate the impact that any hazard may have on inpatient utilization. Knowledge of hospital utilization patterns during disasters offer healthcare and civic professionals valuable information to plan, respond, mitigate, and evaluate any outcomes stemming from biothreats. PMID:27843420

Clinical and Molecular Investigations Into Ciliopathies

ClinicalTrials.gov

2018-03-27

Autosomal Recessive Polycystic Kidney Disease; Congenital Hepatic Fibrosis; Caroli's Disease; Polycystic Kidney Disease; Joubert Syndrome; Cerebro-Oculo-Renal Syndromes; COACH Syndrome; Senior-Loken Syndrome; Dekaban-Arima Syndrome; Cogan Oculomotor Apraxia; Nephronophthisis; Bardet-Biedl Syndrome; Alstrom Syndrome; Oral-Facial-Digital Syndrome

Forecast of severe fever with thrombocytopenia syndrome incidence with meteorological factors.

PubMed

Sun, Ji-Min; Lu, Liang; Liu, Ke-Ke; Yang, Jun; Wu, Hai-Xia; Liu, Qi-Yong

2018-06-01

Severe fever with thrombocytopenia syndrome (SFTS) is emerging and some studies reported that SFTS incidence was associated with meteorological factors, while no report on SFTS forecast models was reported up to date. In this study, we constructed and compared three forecast models using autoregressive integrated moving average (ARIMA) model, negative binomial regression model (NBM), and quasi-Poisson generalized additive model (GAM). The dataset from 2011 to 2015 were used for model construction and the dataset in 2016 were used for external validity assessment. All the three models fitted the SFTS cases reasonably well during the training process and forecast process, while the NBM model forecasted better than other two models. Moreover, we demonstrated that temperature and relative humidity played key roles in explaining the temporal dynamics of SFTS occurrence. Our study contributes to better understanding of SFTS dynamics and provides predictive tools for the control and prevention of SFTS. Copyright © 2018 Elsevier B.V. All rights reserved.

Short-Term Forecasting of Loads and Wind Power for Latvian Power System: Accuracy and Capacity of the Developed Tools

NASA Astrophysics Data System (ADS)

Radziukynas, V.; Klementavičius, A.

2016-04-01

The paper analyses the performance results of the recently developed short-term forecasting suit for the Latvian power system. The system load and wind power are forecasted using ANN and ARIMA models, respectively, and the forecasting accuracy is evaluated in terms of errors, mean absolute errors and mean absolute percentage errors. The investigation of influence of additional input variables on load forecasting errors is performed. The interplay of hourly loads and wind power forecasting errors is also evaluated for the Latvian power system with historical loads (the year 2011) and planned wind power capacities (the year 2023).

Assessing air quality in Aksaray with time series analysis

NASA Astrophysics Data System (ADS)

Kadilar, Gamze Özel; Kadilar, Cem

2017-04-01

Sulphur dioxide (SO2) is a major air pollutant caused by the dominant usage of diesel, petrol and fuels by vehicles and industries. One of the most air-polluted city in Turkey is Aksaray. Hence, in this study, the level of SO2 is analyzed in Aksaray based on the database monitored at air quality monitoring station of Turkey. Seasonal Autoregressive Integrated Moving Average (SARIMA) approach is used to forecast the level of SO2 air quality parameter. The results indicate that the seasonal ARIMA model provides reliable and satisfactory predictions for the air quality parameters and expected to be an alternative tool for practical assessment and justification.

Development of S-ARIMA Model for Forecasting Demand in a Beverage Supply Chain

NASA Astrophysics Data System (ADS)

Mircetic, Dejan; Nikolicic, Svetlana; Maslaric, Marinko; Ralevic, Nebojsa; Debelic, Borna

2016-11-01

Demand forecasting is one of the key activities in planning the freight flows in supply chains, and accordingly it is essential for planning and scheduling of logistic activities within observed supply chain. Accurate demand forecasting models directly influence the decrease of logistics costs, since they provide an assessment of customer demand. Customer demand is a key component for planning all logistic processes in supply chain, and therefore determining levels of customer demand is of great interest for supply chain managers. In this paper we deal with exactly this kind of problem, and we develop the seasonal Autoregressive IntegratedMoving Average (SARIMA) model for forecasting demand patterns of a major product of an observed beverage company. The model is easy to understand, flexible to use and appropriate for assisting the expert in decision making process about consumer demand in particular periods.

Preliminary analysis on hybrid Box-Jenkins - GARCH modeling in forecasting gold price

NASA Astrophysics Data System (ADS)

Yaziz, Siti Roslindar; Azizan, Noor Azlinna; Ahmad, Maizah Hura; Zakaria, Roslinazairimah; Agrawal, Manju; Boland, John

2015-02-01

Gold has been regarded as a valuable precious metal and the most popular commodity as a healthy return investment. Hence, the analysis and prediction of gold price become very significant to investors. This study is a preliminary analysis on gold price and its volatility that focuses on the performance of hybrid Box-Jenkins models together with GARCH in analyzing and forecasting gold price. The Box-Cox formula is used as the data transformation method due to its potential best practice in normalizing data, stabilizing variance and reduces heteroscedasticity using 41-year daily gold price data series starting 2nd January 1973. Our study indicates that the proposed hybrid model ARIMA-GARCH with t-innovation can be a new potential approach in forecasting gold price. This finding proves the strength of GARCH in handling volatility in the gold price as well as overcomes the non-linear limitation in the Box-Jenkins modeling.

Time series modelling of global mean temperature for managerial decision-making.

PubMed

Romilly, Peter

2005-07-01

Climate change has important implications for business and economic activity. Effective management of climate change impacts will depend on the availability of accurate and cost-effective forecasts. This paper uses univariate time series techniques to model the properties of a global mean temperature dataset in order to develop a parsimonious forecasting model for managerial decision-making over the short-term horizon. Although the model is estimated on global temperature data, the methodology could also be applied to temperature data at more localised levels. The statistical techniques include seasonal and non-seasonal unit root testing with and without structural breaks, as well as ARIMA and GARCH modelling. A forecasting evaluation shows that the chosen model performs well against rival models. The estimation results confirm the findings of a number of previous studies, namely that global mean temperatures increased significantly throughout the 20th century. The use of GARCH modelling also shows the presence of volatility clustering in the temperature data, and a positive association between volatility and global mean temperature.

RADON CONCENTRATION TIME SERIES MODELING AND APPLICATION DISCUSSION.

PubMed

Stránský, V; Thinová, L

2017-11-01

In the year 2010 a continual radon measurement was established at Mladeč Caves in the Czech Republic using a continual radon monitor RADIM3A. In order to model radon time series in the years 2010-15, the Box-Jenkins Methodology, often used in econometrics, was applied. Because of the behavior of radon concentrations (RCs), a seasonal integrated, autoregressive moving averages model with exogenous variables (SARIMAX) has been chosen to model the measured time series. This model uses the time series seasonality, previously acquired values and delayed atmospheric parameters, to forecast RC. The developed model for RC time series is called regARIMA(5,1,3). Model residuals could be retrospectively compared with seismic evidence of local or global earthquakes, which occurred during the RCs measurement. This technique enables us to asses if continuously measured RC could serve an earthquake precursor. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Arima syndrome caused by CEP290 specific variant and accompanied with pathological cilium; clinical comparison with Joubert syndrome and its related diseases.

PubMed

Itoh, Masayuki; Ide, Shuhei; Iwasaki, Yuji; Saito, Takashi; Narita, Keishi; Dai, Hongmei; Yamakura, Shinji; Furue, Takeki; Kitayama, Hirotsugu; Maeda, Keiko; Takahashi, Eihiko; Matsui, Kiyoshi; Goto, Yu-Ichi; Takeda, Sen; Arima, Masataka

2018-04-01

Arima syndrome (AS) is a rare disease and its clinical features mimic those of Joubert syndrome or Joubert syndrome-related diseases (JSRD). Recently, we clarified the AS diagnostic criteria and its severe phenotype. However, genetic evidence of AS remains unknown. We explored causative genes of AS and compared the clinical and genetic features of AS with the other JSRD. We performed genetic analyses of 4 AS patients of 3 families with combination of whole-exome sequencing and Sanger sequencing. Furthermore, we studied cell biology with the cultured fibroblasts of 3 AS patients. All patients had a specific homozygous variant (c.6012-12T>A, p.Arg2004Serfs*7) or compound heterozygous variants (c.1711+1G>A; c.6012-12T>A, p.Gly570Aspfs*19;Arg2004Serfs*7) in centrosomal protein 290 kDa (CEP290) gene. These unique variants lead to abnormal splicing and premature termination. Morphological analysis of cultured fibroblasts from AS patients revealed a marked decrease of the CEP290-positive cell number with significantly longer cilium and naked and protruded ciliary axoneme without ciliary membrane into the cytoplasm. AS resulted in cilia dysfunction from centrosome disruption. The unique variant of CEP290 could be strongly linked to AS pathology. Here, we provided AS specific genetic evidence, which steers the structure and functions of centrosome that is responsible for normal ciliogenesis. This is the first report that has demonstrated the molecular basis of Arima syndrome. Copyright © 2017 The Japanese Society of Child Neurology. Published by Elsevier B.V. All rights reserved.

Nowcasting sunshine number using logistic modeling

NASA Astrophysics Data System (ADS)

Brabec, Marek; Badescu, Viorel; Paulescu, Marius

2013-04-01

In this paper, we present a formalized approach to statistical modeling of the sunshine number, binary indicator of whether the Sun is covered by clouds introduced previously by Badescu (Theor Appl Climatol 72:127-136, 2002). Our statistical approach is based on Markov chain and logistic regression and yields fully specified probability models that are relatively easily identified (and their unknown parameters estimated) from a set of empirical data (observed sunshine number and sunshine stability number series). We discuss general structure of the model and its advantages, demonstrate its performance on real data and compare its results to classical ARIMA approach as to a competitor. Since the model parameters have clear interpretation, we also illustrate how, e.g., their inter-seasonal stability can be tested. We conclude with an outlook to future developments oriented to construction of models allowing for practically desirable smooth transition between data observed with different frequencies and with a short discussion of technical problems that such a goal brings.

Stochastic nature of Landsat MSS data

NASA Technical Reports Server (NTRS)

Labovitz, M. L.; Masuoka, E. J.

1987-01-01

A multiple series generalization of the ARIMA models is used to model Landsat MSS scan lines as sequences of vectors, each vector having four elements (bands). The purpose of this work is to investigate if Landsat scan lines can be described by a general multiple series linear stochastic model and if the coefficients of such a model vary as a function of satellite system and target attributes. To accomplish this objective, an exploratory experimental design was set up incorporating six factors, four representing target attributes - location, cloud cover, row (within location), and column (within location) - and two factors representing system attributes - satellite number and detector bank. Each factor was included in the design at two levels and, with two replicates per treatment, 128 scan lines were analyzed. The results of the analysis suggests that a multiple AR(4) model is an adequate representation across all scan lines. Furthermore, the coefficients of the AR(4) model vary with location, particularly changes in physiography (slope regimes), and with percent cloud cover, but are insensitive to changes in system attributes.

Freely chosen cadence during a covert manipulation of ambient temperature.

PubMed

Hartley, Geoffrey L; Cheung, Stephen S

2013-01-01

The present study investigated relationships between changes in power output (PO) to torque (TOR) or freely chosen cadence (FCC) during thermal loading. Twenty participants cycled at a constant rating of perceived exertion while ambient temperature (Ta) was covertly manipulated at 20-min intervals of 20 °C, 35 °C, and 20 °C. The magnitude responses of PO, FCC and TOR were analyzed using repeated-measures ANOVA, while the temporal correlations were analyzed using Auto-Regressive Integrated Moving Averages (ARIMA). Increases in Ta caused significant thermal strain (p < .01), and subsequently, a decrease in PO and TOR magnitude (p < .01), whereas FCC remained unchanged (p = .51). ARIMA indicates that changes in PO were highly correlated to TOR (stationary r2 = .954, p = .04), while FCC was moderately correlated (stationary r2 = .717, p = .01) to PO. In conclusion, changes in PO are caused by a modulation in TOR, whereas FCC remains unchanged and therefore, unaffected by thermal stressors.

Assessment and prediction of road accident injuries trend using time-series models in Kurdistan.

PubMed

Parvareh, Maryam; Karimi, Asrin; Rezaei, Satar; Woldemichael, Abraha; Nili, Sairan; Nouri, Bijan; Nasab, Nader Esmail

2018-01-01

Road traffic accidents are commonly encountered incidents that can cause high-intensity injuries to the victims and have direct impacts on the members of the society. Iran has one of the highest incident rates of road traffic accidents. The objective of this study was to model the patterns of road traffic accidents leading to injury in Kurdistan province, Iran. A time-series analysis was conducted to characterize and predict the frequency of road traffic accidents that lead to injury in Kurdistan province. The injuries were categorized into three separate groups which were related to the car occupants, motorcyclists and pedestrian road traffic accident injuries. The Box-Jenkins time-series analysis was used to model the injury observations applying autoregressive integrated moving average (ARIMA) and seasonal autoregressive integrated moving average (SARIMA) from March 2009 to February 2015 and to predict the accidents up to 24 months later (February 2017). The analysis was carried out using R-3.4.2 statistical software package. A total of 5199 pedestrians, 9015 motorcyclists, and 28,906 car occupants' accidents were observed. The mean (SD) number of car occupant, motorcyclist and pedestrian accident injuries observed were 401.01 (SD 32.78), 123.70 (SD 30.18) and 71.19 (SD 17.92) per year, respectively. The best models for the pattern of car occupant, motorcyclist, and pedestrian injuries were the ARIMA (1, 0, 0), SARIMA (1, 0, 2) (1, 0, 0) 12 , and SARIMA (1, 1, 1) (0, 0, 1) 12 , respectively. The motorcyclist and pedestrian injuries showed a seasonal pattern and the peak was during summer (August). The minimum frequency for the motorcyclist and pedestrian injuries were observed during the late autumn and early winter (December and January). Our findings revealed that the observed motorcyclist and pedestrian injuries had a seasonal pattern that was explained by air temperature changes overtime. These findings call the need for close monitoring of the accidents during the high-risk periods in order to control and decrease the rate of the injuries.

Prediction of South China sea level using seasonal ARIMA models

NASA Astrophysics Data System (ADS)

Fernandez, Flerida Regine; Po, Rodolfo; Montero, Neil; Addawe, Rizavel

2017-11-01

Accelerating sea level rise is an indicator of global warming and poses a threat to low-lying places and coastal countries. This study aims to fit a Seasonal Autoregressive Integrated Moving Average (SARIMA) model to the time series obtained from the TOPEX and Jason series of satellite radar altimetries of the South China Sea from the year 2008 to 2015. With altimetric measurements taken in a 10-day repeat cycle, monthly averages of the satellite altimetry measurements were taken to compose the data set used in the study. SARIMA models were then tried and fitted to the time series in order to find the best-fit model. Results show that the SARIMA(1,0,0)(0,1,1)12 model best fits the time series and was used to forecast the values for January 2016 to December 2016. The 12-month forecast using SARIMA(1,0,0)(0,1,1)12 shows that the sea level gradually increases from January to September 2016, and decreases until December 2016.

Rising gasoline prices increase new motorcycle sales and fatalities.

PubMed

Zhu, He; Wilson, Fernando A; Stimpson, Jim P; Hilsenrath, Peter E

2015-12-01

We examined whether sales of new motorcycles was a mechanism to explain the relationship between motorcycle fatalities and gasoline prices. The data came from the Motorcycle Industry Council, Energy Information Administration and Fatality Analysis Reporting System for 1984-2009. Autoregressive integrated moving average (ARIMA) regressions estimated the effect of inflation-adjusted gasoline price on motorcycle sales and logistic regressions estimated odds ratios (ORs) between new and old motorcycle fatalities when gasoline prices increase. New motorcycle sales were positively correlated with gasoline prices (r = 0.78) and new motorcycle fatalities (r = 0.92). ARIMA analysis estimated that a US$1 increase in gasoline prices would result in 295,000 new motorcycle sales and, consequently, 233 new motorcycle fatalities. Compared to crashes on older motorcycle models, those on new motorcycles were more likely to be young riders, occur in the afternoon, in clear weather, with a large engine displacement, and without alcohol involvement. Riders on new motorcycles were more likely to be in fatal crashes relative to older motorcycles (OR 1.14, 95 % confidence interval (CI) 1.02-1.28) when gasoline prices increase. Our findings suggest that, in response to increasing gasoline prices, people tend to purchase new motorcycles, and this is accompanied with significantly increased crash risk. There are several policy mechanisms that can be used to lower the risk of motorcycle crash injuries through the mechanism of gas prices and motorcycle sales such as raising awareness of motorcycling risks, enhancing licensing and testing requirements, limiting motorcycle power-to-weight ratios for inexperienced riders, and developing mandatory training programs for new riders.

On the Effects of Artificial Feeding on Bee Colony Dynamics: A Mathematical Model

PubMed Central

Paiva, Juliana Pereira Lisboa Mohallem; Paiva, Henrique Mohallem; Esposito, Elisa; Morais, Michelle Manfrini

2016-01-01

This paper proposes a new mathematical model to evaluate the effects of artificial feeding on bee colony population dynamics. The proposed model is based on a classical framework and contains differential equations that describe the changes in the number of hive bees, forager bees, and brood cells, as a function of amounts of natural and artificial food. The model includes the following elements to characterize the artificial feeding scenario: a function to model the preference of the bees for natural food over artificial food; parameters to quantify the quality and palatability of artificial diets; a function to account for the efficiency of the foragers in gathering food under different environmental conditions; and a function to represent different approaches used by the beekeeper to feed the hive with artificial food. Simulated results are presented to illustrate the main characteristics of the model and its behavior under different scenarios. The model results are validated with experimental data from the literature involving four different artificial diets. A good match between simulated and experimental results was achieved. PMID:27875589

Experimental Validation of Model Updating and Damage Detection via Eigenvalue Sensitivity Methods with Artificial Boundary Conditions

DTIC Science & Technology

2017-09-01

VALIDATION OF MODEL UPDATING AND DAMAGE DETECTION VIA EIGENVALUE SENSITIVITY METHODS WITH ARTIFICIAL BOUNDARY CONDITIONS by Matthew D. Bouwense...VALIDATION OF MODEL UPDATING AND DAMAGE DETECTION VIA EIGENVALUE SENSITIVITY METHODS WITH ARTIFICIAL BOUNDARY CONDITIONS 5. FUNDING NUMBERS 6. AUTHOR...unlimited. EXPERIMENTAL VALIDATION OF MODEL UPDATING AND DAMAGE DETECTION VIA EIGENVALUE SENSITIVITY METHODS WITH ARTIFICIAL BOUNDARY

Crude oil price forecasting based on hybridizing wavelet multiple linear regression model, particle swarm optimization techniques, and principal component analysis.

PubMed

Shabri, Ani; Samsudin, Ruhaidah

2014-01-01

Crude oil prices do play significant role in the global economy and are a key input into option pricing formulas, portfolio allocation, and risk measurement. In this paper, a hybrid model integrating wavelet and multiple linear regressions (MLR) is proposed for crude oil price forecasting. In this model, Mallat wavelet transform is first selected to decompose an original time series into several subseries with different scale. Then, the principal component analysis (PCA) is used in processing subseries data in MLR for crude oil price forecasting. The particle swarm optimization (PSO) is used to adopt the optimal parameters of the MLR model. To assess the effectiveness of this model, daily crude oil market, West Texas Intermediate (WTI), has been used as the case study. Time series prediction capability performance of the WMLR model is compared with the MLR, ARIMA, and GARCH models using various statistics measures. The experimental results show that the proposed model outperforms the individual models in forecasting of the crude oil prices series.

Crude Oil Price Forecasting Based on Hybridizing Wavelet Multiple Linear Regression Model, Particle Swarm Optimization Techniques, and Principal Component Analysis

PubMed Central

Shabri, Ani; Samsudin, Ruhaidah

2014-01-01

Crude oil prices do play significant role in the global economy and are a key input into option pricing formulas, portfolio allocation, and risk measurement. In this paper, a hybrid model integrating wavelet and multiple linear regressions (MLR) is proposed for crude oil price forecasting. In this model, Mallat wavelet transform is first selected to decompose an original time series into several subseries with different scale. Then, the principal component analysis (PCA) is used in processing subseries data in MLR for crude oil price forecasting. The particle swarm optimization (PSO) is used to adopt the optimal parameters of the MLR model. To assess the effectiveness of this model, daily crude oil market, West Texas Intermediate (WTI), has been used as the case study. Time series prediction capability performance of the WMLR model is compared with the MLR, ARIMA, and GARCH models using various statistics measures. The experimental results show that the proposed model outperforms the individual models in forecasting of the crude oil prices series. PMID:24895666

A Comparison of Missing-Data Procedures for Arima Time-Series Analysis

ERIC Educational Resources Information Center

Velicer, Wayne F.; Colby, Suzanne M.

2005-01-01

Missing data are a common practical problem for longitudinal designs. Time-series analysis is a longitudinal method that involves a large number of observations on a single unit. Four different missing-data methods (deletion, mean substitution, mean of adjacent observations, and maximum likelihood estimation) were evaluated. Computer-generated…

Artificial Exo-Society Modeling: a New Tool for SETI Research

NASA Astrophysics Data System (ADS)

Gardner, James N.

2002-01-01

One of the newest fields of complexity research is artificial society modeling. Methodologically related to artificial life research, artificial society modeling utilizes agent-based computer simulation tools like SWARM and SUGARSCAPE developed by the Santa Fe Institute, Los Alamos National Laboratory and the Bookings Institution in an effort to introduce an unprecedented degree of rigor and quantitative sophistication into social science research. The broad aim of artificial society modeling is to begin the development of a more unified social science that embeds cultural evolutionary processes in a computational environment that simulates demographics, the transmission of culture, conflict, economics, disease, the emergence of groups and coadaptation with an environment in a bottom-up fashion. When an artificial society computer model is run, artificial societal patterns emerge from the interaction of autonomous software agents (the "inhabitants" of the artificial society). Artificial society modeling invites the interpretation of society as a distributed computational system and the interpretation of social dynamics as a specialized category of computation. Artificial society modeling techniques offer the potential of computational simulation of hypothetical alien societies in much the same way that artificial life modeling techniques offer the potential to model hypothetical exobiological phenomena. NASA recently announced its intention to begin exploring the possibility of including artificial life research within the broad portfolio of scientific fields comprised by the interdisciplinary astrobiology research endeavor. It may be appropriate for SETI researchers to likewise commence an exploration of the possible inclusion of artificial exo-society modeling within the SETI research endeavor. Artificial exo-society modeling might be particularly useful in a post-detection environment by (1) coherently organizing the set of data points derived from a detected ETI signal, (2) mapping trends in the data points over time (assuming receipt of an extended ETI signal), and (3) projecting such trends forward to derive alternative cultural evolutionary scenarios for the exo-society under analysis. The latter exercise might be particularly useful to compensate for the inevitable time lag between generation of an ETI signal and receipt of an ETI signal on Earth. For this reason, such an exercise might be a helpful adjunct to the decisional process contemplated by Paragraph 9 of the Declaration of Principles Concerning Activities Following the Detection of Extraterrestrial Intelligence.

Long-term behaviour and cross-correlation water quality analysis of the River Elbe, Germany.

PubMed

Lehmann, A; Rode, M

2001-06-01

This study analyses weekly data samples from the river Elbe at Magdeburg between 1984 and 1996 to investigate the changes in metabolism and water quality in the river Elbe since the German reunification in 1990. Modelling water quality variables by autoregressive component models and ARIMA models reveals the improvement of water quality due to the reduction of waste water emissions since 1990. The models are used to determine the long-term and seasonal behaviour of important water quality variables. Organic and heavy metal pollution parameters showed a significant decrease since 1990, however, no significant change of chlorophyll-a as a measure for primary production could be found. A new procedure for testing the significance of a sample correlation coefficient is discussed, which is able to detect spurious sample correlation coefficients without making use of time-consuming prewhitening. The cross-correlation analysis is applied to hydrophysical, biological, and chemical water quality variables of the river Elbe since 1984. Special emphasis is laid on the detection of spurious sample correlation coefficients.

Forecasting malaria incidence based on monthly case reports and environmental factors in Karuzi, Burundi, 1997–2003

PubMed Central

Gomez-Elipe, Alberto; Otero, Angel; van Herp, Michel; Aguirre-Jaime, Armando

2007-01-01

Background The objective of this work was to develop a model to predict malaria incidence in an area of unstable transmission by studying the association between environmental variables and disease dynamics. Methods The study was carried out in Karuzi, a province in the Burundi highlands, using time series of monthly notifications of malaria cases from local health facilities, data from rain and temperature records, and the normalized difference vegetation index (NDVI). Using autoregressive integrated moving average (ARIMA) methodology, a model showing the relation between monthly notifications of malaria cases and the environmental variables was developed. Results The best forecasting model (R2adj = 82%, p < 0.0001 and 93% forecasting accuracy in the range ± 4 cases per 100 inhabitants) included the NDVI, mean maximum temperature, rainfall and number of malaria cases in the preceding month. Conclusion This model is a simple and useful tool for producing reasonably reliable forecasts of the malaria incidence rate in the study area. PMID:17892540

Impact of Australia's introduction of tobacco plain packs on adult smokers’ pack-related perceptions and responses: results from a continuous tracking survey

PubMed Central

Dunlop, Sally M; Dobbins, Timothy; Young, Jane M; Perez, Donna; Currow, David C

2014-01-01

Objectives To investigate the impact of Australia's plain tobacco packaging policy on two stated purposes of the legislation—increasing the impact of health warnings and decreasing the promotional appeal of packaging—among adult smokers. Design Serial cross-sectional study with weekly telephone surveys (April 2006–May 2013). Interrupted time-series analyses using ARIMA modelling and linear regression models were used to investigate intervention effects. Participants 15 745 adult smokers (aged 18 years and above) in New South Wales (NSW), Australia. Random selection of participants involved recruiting households using random digit dialling and selecting the nth oldest smoker for interview. Intervention The introduction of the legislation on 1 October 2012. Outcomes Salience of tobacco pack health warnings, cognitive and emotional responses to warnings, avoidance of warnings, perceptions regarding one's cigarette pack. Results Adjusting for background trends, seasonality, antismoking advertising activity and cigarette costliness, results from ARIMA modelling showed that, 2–3 months after the introduction of the new packs, there was a significant increase in the absolute proportion of smokers having strong cognitive (9.8% increase, p=0.005), emotional (8.6% increase, p=0.01) and avoidant (9.8% increase, p=0.0005) responses to on-pack health warnings. Similarly, there was a significant increase in the proportion of smokers strongly disagreeing that the look of their cigarette pack is attractive (57.5% increase, p<0.0001), says something good about them (54.5% increase, p<0.0001), influences the brand they buy (40.6% increase, p<0.0001), makes their pack stand out (55.6% increase, p<0.0001), is fashionable (44.7% increase, p<0.0001) and matches their style (48.1% increase, p<0.0001). Changes in these outcomes were maintained 6 months postintervention. Conclusions The introductory effects of the plain packaging legislation among adult smokers are consistent with the specific objectives of the legislation in regard to reducing promotional appeal and increasing effectiveness of health warnings. PMID:25524542

Impact of Australia's introduction of tobacco plain packs on adult smokers' pack-related perceptions and responses: results from a continuous tracking survey.

PubMed

Dunlop, Sally M; Dobbins, Timothy; Young, Jane M; Perez, Donna; Currow, David C

2014-12-18

To investigate the impact of Australia's plain tobacco packaging policy on two stated purposes of the legislation--increasing the impact of health warnings and decreasing the promotional appeal of packaging--among adult smokers. Serial cross-sectional study with weekly telephone surveys (April 2006-May 2013). Interrupted time-series analyses using ARIMA modelling and linear regression models were used to investigate intervention effects. 15,745 adult smokers (aged 18 years and above) in New South Wales (NSW), Australia. Random selection of participants involved recruiting households using random digit dialling and selecting the nth oldest smoker for interview. The introduction of the legislation on 1 October 2012. Salience of tobacco pack health warnings, cognitive and emotional responses to warnings, avoidance of warnings, perceptions regarding one's cigarette pack. Adjusting for background trends, seasonality, antismoking advertising activity and cigarette costliness, results from ARIMA modelling showed that, 2-3 months after the introduction of the new packs, there was a significant increase in the absolute proportion of smokers having strong cognitive (9.8% increase, p=0.005), emotional (8.6% increase, p=0.01) and avoidant (9.8% increase, p=0.0005) responses to on-pack health warnings. Similarly, there was a significant increase in the proportion of smokers strongly disagreeing that the look of their cigarette pack is attractive (57.5% increase, p<0.0001), says something good about them (54.5% increase, p<0.0001), influences the brand they buy (40.6% increase, p<0.0001), makes their pack stand out (55.6% increase, p<0.0001), is fashionable (44.7% increase, p<0.0001) and matches their style (48.1% increase, p<0.0001). Changes in these outcomes were maintained 6 months postintervention. The introductory effects of the plain packaging legislation among adult smokers are consistent with the specific objectives of the legislation in regard to reducing promotional appeal and increasing effectiveness of health warnings. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Changing Hot Pursuit Policy: An Empirical Assessment of the Impact on Pursuit Behavior.

ERIC Educational Resources Information Center

Crew, Robert E., Jr.; And Others

1994-01-01

Using a two-year time series, the impact on law enforcement pursuit behavior of two changes in pursuit policy in a police department was studied using the ARIMA computer program and Tobit analysis. Each policy change produced significant reductions in pursuits engaged in by police officers. (SLD)

Performance vs. Paper-And-Pencil Estimates of Cognitive Abilities.

ERIC Educational Resources Information Center

Arima, James K.

Arima's Discrimination Learning Test (DLT) was reconfigured, made into a self-paced mode, and administered to potential recruits in order to determine if: (1) a previous study indicating a lack of difference in learning performance between white and nonwhites would hold up; and (2) the correlations between scores attained on the DLT and scores…

Application of artificial intelligence to the management of urological cancer.

PubMed

Abbod, Maysam F; Catto, James W F; Linkens, Derek A; Hamdy, Freddie C

2007-10-01

Artificial intelligence techniques, such as artificial neural networks, Bayesian belief networks and neuro-fuzzy modeling systems, are complex mathematical models based on the human neuronal structure and thinking. Such tools are capable of generating data driven models of biological systems without making assumptions based on statistical distributions. A large amount of study has been reported of the use of artificial intelligence in urology. We reviewed the basic concepts behind artificial intelligence techniques and explored the applications of this new dynamic technology in various aspects of urological cancer management. A detailed and systematic review of the literature was performed using the MEDLINE and Inspec databases to discover reports using artificial intelligence in urological cancer. The characteristics of machine learning and their implementation were described and reports of artificial intelligence use in urological cancer were reviewed. While most researchers in this field were found to focus on artificial neural networks to improve the diagnosis, staging and prognostic prediction of urological cancers, some groups are exploring other techniques, such as expert systems and neuro-fuzzy modeling systems. Compared to traditional regression statistics artificial intelligence methods appear to be accurate and more explorative for analyzing large data cohorts. Furthermore, they allow individualized prediction of disease behavior. Each artificial intelligence method has characteristics that make it suitable for different tasks. The lack of transparency of artificial neural networks hinders global scientific community acceptance of this method but this can be overcome by neuro-fuzzy modeling systems.

Time Prediction Models for Echinococcosis Based on Gray System Theory and Epidemic Dynamics.

PubMed

Zhang, Liping; Wang, Li; Zheng, Yanling; Wang, Kai; Zhang, Xueliang; Zheng, Yujian

2017-03-04

Echinococcosis, which can seriously harm human health and animal husbandry production, has become an endemic in the Xinjiang Uygur Autonomous Region of China. In order to explore an effective human Echinococcosis forecasting model in Xinjiang, three grey models, namely, the traditional grey GM(1,1) model, the Grey-Periodic Extensional Combinatorial Model (PECGM(1,1)), and the Modified Grey Model using Fourier Series (FGM(1,1)), in addition to a multiplicative seasonal ARIMA(1,0,1)(1,1,0)₄ model, are applied in this study for short-term predictions. The accuracy of the different grey models is also investigated. The simulation results show that the FGM(1,1) model has a higher performance ability, not only for model fitting, but also for forecasting. Furthermore, considering the stability and the modeling precision in the long run, a dynamic epidemic prediction model based on the transmission mechanism of Echinococcosis is also established for long-term predictions. Results demonstrate that the dynamic epidemic prediction model is capable of identifying the future tendency. The number of human Echinococcosis cases will increase steadily over the next 25 years, reaching a peak of about 1250 cases, before eventually witnessing a slow decline, until it finally ends.

The relationship between gasoline price and patterns of motorcycle fatalities and injuries.

PubMed

Zhu, He; Wilson, Fernando A; Stimpson, Jim P

2015-06-01

Economic factors such as rising gasoline prices may contribute to the crash trends by shaping individuals' choices of transportation modalities. This study examines the relationship of gasoline prices with fatal and non-fatal motorcycle injuries. Data on fatal and non-fatal motorcycle injuries come from California's Statewide Integrated Traffic Records System for 2002-2011. Autoregressive integrated moving average (ARIMA) regressions were used to estimate the impact of inflation-adjusted gasoline price per gallon on trends of motorcycle injuries. Motorcycle fatalities and severe and minor injuries in California were highly correlated with increasing gasoline prices from 2002 to 2011 (r=0.76, 0.88 and 0.85, respectively). In 2008, the number of fatalities and injuries reached 13,457--a 34% increase since 2002, a time period in which inflation-adjusted gasoline prices increased about $0.30 per gallon every year. The majority of motorcycle riders involved in crashes were male (92.5%), middle-aged (46.2%) and non-Hispanic white (67.9%). Using ARIMA modelling, we estimated that rising gasoline prices resulted in an additional 800 fatalities and 10,290 injuries from 2002 to 2011 in California. Our findings suggest that increasing gasoline prices led to more motorcycle riders on the roads and, consequently, more injuries. Aside from mandatory helmet laws and their enforcement, other strategies may include raising risk awareness of motorcyclists and investment in public transportation as an alternative transportation modality to motorcycling. In addition, universally mandated training courses and strict licensing tests of riding skills should be emphasised to help reduce the motorcycle fatal and non-fatal injuries. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Stochastic Modeling of Airlines' Scheduled Services Revenue

NASA Technical Reports Server (NTRS)

Hamed, M. M.

1999-01-01

Airlines' revenue generated from scheduled services account for the major share in the total revenue. As such, predicting airlines' total scheduled services revenue is of great importance both to the governments (in case of national airlines) and private airlines. This importance stems from the need to formulate future airline strategic management policies, determine government subsidy levels, and formulate governmental air transportation policies. The prediction of the airlines' total scheduled services revenue is dealt with in this paper. Four key components of airline's scheduled services are considered. These include revenues generated from passenger, cargo, mail, and excess baggage. By addressing the revenue generated from each schedule service separately, air transportation planners and designers arc able to enhance their ability to formulate specific strategies for each component. Estimation results clearly indicate that the four stochastic processes (scheduled services components) are represented by different Box-Jenkins ARIMA models. The results demonstrate the appropriateness of the developed models and their ability to provide air transportation planners with future information vital to the planning and design processes.

Stochastic Modeling of Airlines' Scheduled Services Revenue

NASA Technical Reports Server (NTRS)

Hamed, M. M.

1999-01-01

Airlines' revenue generated from scheduled services account for the major share in the total revenue. As such, predicting airlines' total scheduled services revenue is of great importance both to the governments (in case of national airlines) and private airlines. This importance stems from the need to formulate future airline strategic management policies, determine government subsidy levels, and formulate governmental air transportation policies. The prediction of the airlines' total scheduled services revenue is dealt with in this paper. Four key components of airline's scheduled services are considered. These include revenues generated from passenger, cargo, mail, and excess baggage. By addressing the revenue generated from each schedule service separately, air transportation planners and designers are able to enhance their ability to formulate specific strategies for each component. Estimation results clearly indicate that the four stochastic processes (scheduled services components) are represented by different Box-Jenkins ARIMA models. The results demonstrate the appropriateness of the developed models and their ability to provide air transportation planners with future information vital to the planning and design processes.

Time series analysis of gold production in Malaysia

NASA Astrophysics Data System (ADS)

Muda, Nora; Hoon, Lee Yuen

2012-05-01

Gold is a soft, malleable, bright yellow metallic element and unaffected by air or most reagents. It is highly valued as an asset or investment commodity and is extensively used in jewellery, industrial application, dentistry and medical applications. In Malaysia, gold mining is limited in several areas such as Pahang, Kelantan, Terengganu, Johor and Sarawak. The main purpose of this case study is to obtain a suitable model for the production of gold in Malaysia. The model can also be used to predict the data of Malaysia's gold production in the future. Box-Jenkins time series method was used to perform time series analysis with the following steps: identification, estimation, diagnostic checking and forecasting. In addition, the accuracy of prediction is tested using mean absolute percentage error (MAPE). From the analysis, the ARIMA (3,1,1) model was found to be the best fitted model with MAPE equals to 3.704%, indicating the prediction is very accurate. Hence, this model can be used for forecasting. This study is expected to help the private and public sectors to understand the gold production scenario and later plan the gold mining activities in Malaysia.

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.

The Satellite Clock Bias Prediction Method Based on Takagi-Sugeno Fuzzy Neural Network

NASA Astrophysics Data System (ADS)

Cai, C. L.; Yu, H. G.; Wei, Z. C.; Pan, J. D.

2017-05-01

The continuous improvement of the prediction accuracy of Satellite Clock Bias (SCB) is the key problem of precision navigation. In order to improve the precision of SCB prediction and better reflect the change characteristics of SCB, this paper proposes an SCB prediction method based on the Takagi-Sugeno fuzzy neural network. Firstly, the SCB values are pre-treated based on their characteristics. Then, an accurate Takagi-Sugeno fuzzy neural network model is established based on the preprocessed data to predict SCB. This paper uses the precise SCB data with different sampling intervals provided by IGS (International Global Navigation Satellite System Service) to realize the short-time prediction experiment, and the results are compared with the ARIMA (Auto-Regressive Integrated Moving Average) model, GM(1,1) model, and the quadratic polynomial model. The results show that the Takagi-Sugeno fuzzy neural network model is feasible and effective for the SCB short-time prediction experiment, and performs well for different types of clocks. The prediction results for the proposed method are better than the conventional methods obviously.

Impact of the Illinois Seat Belt Use Law on Accidents, Deaths, and Injuries.

ERIC Educational Resources Information Center

Rock, Steven M.

1992-01-01

The impact of the 1985 Illinois seat belt law is explored using Box-Jenkins Auto-Regressive, Integrated Moving Averages (ARIMA) techniques and monthly accident statistical data from the state department of transportation for January-July 1990. A conservative estimate is that the law provides benefits of $15 million per month in Illinois. (SLD)

The Use of Time Series Analysis and t Tests with Serially Correlated Data Tests.

ERIC Educational Resources Information Center

Nicolich, Mark J.; Weinstein, Carol S.

1981-01-01

Results of three methods of analysis applied to simulated autocorrelated data sets with an intervention point (varying in autocorrelation degree, variance of error term, and magnitude of intervention effect) are compared and presented. The three methods are: t tests; maximum likelihood Box-Jenkins (ARIMA); and Bayesian Box Jenkins. (Author/AEF)

Examining the Impact of External Influences on Police Use of Deadly Force over Time.

ERIC Educational Resources Information Center

White, Michael D.

2002-01-01

Used interrupted time-series analysis (ARIMA) to study the impact of legislation and judicial intervention on the use of deadly force by police officers in Philadelphia, Pennsylvania. Findings generally suggest that dynamic changes in the internal working environment can outweigh the influence of external mechanisms on deadly force use. Findings…

Models for Train Passenger Forecasting of Java and Sumatra

NASA Astrophysics Data System (ADS)

Sartono

2017-04-01

People tend to take public transportation to avoid high traffic, especially in Java. In Jakarta, the number of railway passengers is over than the capacity of the train at peak time. This is an opportunity as well as a challenge. If it is managed well then the company can get high profit. Otherwise, it may lead to disaster. This article discusses models for the train passengers, hence, finding the reasonable models to make a prediction overtimes. The Box-Jenkins method is occupied to develop a basic model. Then, this model is compared to models obtained using exponential smoothing method and regression method. The result shows that Holt-Winters model is better to predict for one-month, three-month, and six-month ahead for the passenger in Java. In addition, SARIMA(1,1,0)(2,0,0) is more accurate for nine-month and twelve-month oversee. On the other hand, for Sumatra passenger forecasting, SARIMA(1,1,1)(0,0,2) gives a better approximation for one-month ahead, and ARIMA model is best for three-month ahead prediction. The rest, Trend Seasonal and Liner Model has the least of RMSE to forecast for six-month, nine-month, and twelve-month ahead.

An Evaluation of Artificial Neural Network Modeling for Manpower Analysis

DTIC Science & Technology

1993-09-01

NAVAL POSTGRADUATE SCHOOL Monterey, California 0- I 1 ’(ft ADV "’r-"A THESIS AN EVALUATION OF ARTIFICIAL NEURAL NETWORK MODELING FOR MANPOWER...AGENCY USE ONLY (Leave blank) 2. REPORT DATE 3. REPORT TYPE AND DATES COVERED September, 1993 4. TITLE AND SUBTITLE An Evaluation Of Artificial Neural Network 5...unlimited. An Evaluation of Artificial Neural Network Modeling for Manpower Analysis by Brian J. Byrne Captain, United States Marine Corps B.S

Evaluation of simulations to understand effects of groundwater development and artificial recharge on the surface water and riparian vegetation Sierra Vista subwatershed, Upper San Pedro Basin, Arizona

USGS Publications Warehouse

Leake, Stanley A.; Gungle, Bruce

2012-01-01

In 2007, the U.S. Geological Survey documented a five-layer groundwater flow model of the Sierra Vista and Sonoran subwatersheds of the Upper San Pedro Basin. The model has been applied by a private consultant to evaluate the effects of projected groundwater pumping through 2105 and effects of artificial recharge at three near-stream sites for 2012-2111. The main concern regarding simulations of long-term groundwater pumping is the effect of artificial model boundaries on modeled response, particularly for pumping near Cananea, Sonora, Mexico, which is adjacent to an artificial no-flow boundary. Concerns regarding the simulations of the effects of artificial recharge near streams include the resolution of the model and the representation of the model properties at the site scale; a possible limited ability of the model to correctly apportion recharge response between increased streamflow and increased evapotranspiration; a limited ability of the model to simulate detailed geometries of artificial recharge areas and evapotranspiration areas; and stream locations with the 820-foot grid spacing of the basin-scale model. In spite of these concerns, use of the U.S. Geological Survey five-layer groundwater flow model by the consultant are reasonable and valid.

Generalized Adaptive Artificial Neural Networks

NASA Technical Reports Server (NTRS)

Tawel, Raoul

1993-01-01

Mathematical model of supervised learning by artificial neural network provides for simultaneous adjustments of both temperatures of neurons and synaptic weights, and includes feedback as well as feedforward synaptic connections. Extension of mathematical model described in "Adaptive Neurons For Artificial Neural Networks" (NPO-17803). Dynamics of neural network represented in new model by less-restrictive continuous formalism.

Sources of variation in Landsat autocorrelation

NASA Technical Reports Server (NTRS)

Craig, R. G.; Labovitz, M. L.

1980-01-01

Analysis of sixty-four scan lines representing diverse conditions across satellites, channels, scanners, locations and cloud cover confirms that Landsat data are autocorrelated and consistently follow an Arima (1,0,1) pattern. The AR parameter varies significantly with location and the MA coefficient with cloud cover. Maximum likelihood classification functions are considerably in error unless this autocorrelation is compensated for in sampling.

Time Prediction Models for Echinococcosis Based on Gray System Theory and Epidemic Dynamics

PubMed Central

Zhang, Liping; Wang, Li; Zheng, Yanling; Wang, Kai; Zhang, Xueliang; Zheng, Yujian

2017-01-01

Echinococcosis, which can seriously harm human health and animal husbandry production, has become an endemic in the Xinjiang Uygur Autonomous Region of China. In order to explore an effective human Echinococcosis forecasting model in Xinjiang, three grey models, namely, the traditional grey GM(1,1) model, the Grey-Periodic Extensional Combinatorial Model (PECGM(1,1)), and the Modified Grey Model using Fourier Series (FGM(1,1)), in addition to a multiplicative seasonal ARIMA(1,0,1)(1,1,0)4 model, are applied in this study for short-term predictions. The accuracy of the different grey models is also investigated. The simulation results show that the FGM(1,1) model has a higher performance ability, not only for model fitting, but also for forecasting. Furthermore, considering the stability and the modeling precision in the long run, a dynamic epidemic prediction model based on the transmission mechanism of Echinococcosis is also established for long-term predictions. Results demonstrate that the dynamic epidemic prediction model is capable of identifying the future tendency. The number of human Echinococcosis cases will increase steadily over the next 25 years, reaching a peak of about 1250 cases, before eventually witnessing a slow decline, until it finally ends. PMID:28273856

Modeling Training Site Vegetation Coverage Probability with a Random Optimizing Procedure: An Artificial Neural Network Approach.

DTIC Science & Technology

1998-05-01

Coverage Probability with a Random Optimization Procedure: An Artificial Neural Network Approach by Biing T. Guan, George Z. Gertner, and Alan B...Modeling Training Site Vegetation Coverage Probability with a Random Optimizing Procedure: An Artificial Neural Network Approach 6. AUTHOR(S) Biing...coverage based on past coverage. Approach A literature survey was conducted to identify artificial neural network analysis techniques applicable for

Three-dimensional hysteresis compensation enhances accuracy of robotic artificial muscles

NASA Astrophysics Data System (ADS)

Zhang, Jun; Simeonov, Anthony; Yip, Michael C.

2018-03-01

Robotic artificial muscles are compliant and can generate straight contractions. They are increasingly popular as driving mechanisms for robotic systems. However, their strain and tension force often vary simultaneously under varying loads and inputs, resulting in three-dimensional hysteretic relationships. The three-dimensional hysteresis in robotic artificial muscles poses difficulties in estimating how they work and how to make them perform designed motions. This study proposes an approach to driving robotic artificial muscles to generate designed motions and forces by modeling and compensating for their three-dimensional hysteresis. The proposed scheme captures the nonlinearity by embedding two hysteresis models. The effectiveness of the model is confirmed by testing three popular robotic artificial muscles. Inverting the proposed model allows us to compensate for the hysteresis among temperature surrogate, contraction length, and tension force of a shape memory alloy (SMA) actuator. Feedforward control of an SMA-actuated robotic bicep is demonstrated. This study can be generalized to other robotic artificial muscles, thus enabling muscle-powered machines to generate desired motions.

3D artificial bones for bone repair prepared by computed tomography-guided fused deposition modeling for bone repair.

PubMed

Xu, Ning; Ye, Xiaojian; Wei, Daixu; Zhong, Jian; Chen, Yuyun; Xu, Guohua; He, Dannong

2014-09-10

The medical community has expressed significant interest in the development of new types of artificial bones that mimic natural bones. In this study, computed tomography (CT)-guided fused deposition modeling (FDM) was employed to fabricate polycaprolactone (PCL)/hydroxyapatite (HA) and PCL 3D artificial bones to mimic natural goat femurs. The in vitro mechanical properties, in vitro cell biocompatibility, and in vivo performance of the artificial bones in a long load-bearing goat femur bone segmental defect model were studied. All of the results indicate that CT-guided FDM is a simple, convenient, relatively low-cost method that is suitable for fabricating natural bonelike artificial bones. Moreover, PCL/HA 3D artificial bones prepared by CT-guided FDM have more close mechanics to natural bone, good in vitro cell biocompatibility, biodegradation ability, and appropriate in vivo new bone formation ability. Therefore, PCL/HA 3D artificial bones could be potentially be of use in the treatment of patients with clinical bone defects.

Structural modeling of age specific fertility curves in Peninsular Malaysia: An approach of Lee Carter method

NASA Astrophysics Data System (ADS)

Hanafiah, Hazlenah; Jemain, Abdul Aziz

2013-11-01

In recent years, the study of fertility has been getting a lot of attention among research abroad following fear of deterioration of fertility led by the rapid economy development. Hence, this study examines the feasibility of developing fertility forecasts based on age structure. Lee Carter model (1992) is applied in this study as it is an established and widely used model in analysing demographic aspects. A singular value decomposition approach is incorporated with an ARIMA model to estimate age specific fertility rates in Peninsular Malaysia over the period 1958-2007. Residual plots is used to measure the goodness of fit of the model. Fertility index forecast using random walk drift is then utilised to predict the future age specific fertility. Results indicate that the proposed model provides a relatively good and reasonable data fitting. In addition, there is an apparent and continuous decline in age specific fertility curves in the next 10 years, particularly among mothers' in their early 20's and 40's. The study on the fertility is vital in order to maintain a balance between the population growth and the provision of facilities related resources.

Hyperviscosity for unstructured ALE meshes

NASA Astrophysics Data System (ADS)

Cook, Andrew W.; Ulitsky, Mark S.; Miller, Douglas S.

2013-01-01

An artificial viscosity, originally designed for Eulerian schemes, is adapted for use in arbitrary Lagrangian-Eulerian simulations. Changes to the Eulerian model (dubbed 'hyperviscosity') are discussed, which enable it to work within a Lagrangian framework. New features include a velocity-weighted grid scale and a generalised filtering procedure, applicable to either structured or unstructured grids. The model employs an artificial shear viscosity for treating small-scale vorticity and an artificial bulk viscosity for shock capturing. The model is based on the Navier-Stokes form of the viscous stress tensor, including the diagonal rate-of-expansion tensor. A second-order version of the model is presented, in which Laplacian operators act on the velocity divergence and the grid-weighted strain-rate magnitude to ensure that the velocity field remains smooth at the grid scale. Unlike sound-speed-based artificial viscosities, the hyperviscosity model is compatible with the low Mach number limit. The new model outperforms a commonly used Lagrangian artificial viscosity on a variety of test problems.

Intelligence: Real or artificial?

PubMed Central

Schlinger, Henry D.

1992-01-01

Throughout the history of the artificial intelligence movement, researchers have strived to create computers that could simulate general human intelligence. This paper argues that workers in artificial intelligence have failed to achieve this goal because they adopted the wrong model of human behavior and intelligence, namely a cognitive essentialist model with origins in the traditional philosophies of natural intelligence. An analysis of the word “intelligence” suggests that it originally referred to behavior-environment relations and not to inferred internal structures and processes. It is concluded that if workers in artificial intelligence are to succeed in their general goal, then they must design machines that are adaptive, that is, that can learn. Thus, artificial intelligence researchers must discard their essentialist model of natural intelligence and adopt a selectionist model instead. Such a strategic change should lead them to the science of behavior analysis. PMID:22477051

Rapid Simulation of Blast Wave Propagation in Built Environments Using Coarse-Grain Based Intelligent Modeling Methods

DTIC Science & Technology

2011-04-01

experiments was performed using an artificial neural network to try to capture the nonlinearities. The radial Gaussian artificial neural network system...Modeling Blast-Wave Propagation using Artificial Neural Network Methods‖, in International Journal of Advanced Engineering Informatics, Elsevier

Setup of a Parameterized FE Model for the Die Roll Prediction in Fine Blanking using Artificial Neural Networks

NASA Astrophysics Data System (ADS)

Stanke, J.; Trauth, D.; Feuerhack, A.; Klocke, F.

2017-09-01

Die roll is a morphological feature of fine blanked sheared edges. The die roll reduces the functional part of the sheared edge. To compensate for the die roll thicker sheet metal strips and secondary machining must be used. However, in order to avoid this, the influence of various fine blanking process parameters on the die roll has been experimentally and numerically studied, but there is still a lack of knowledge on the effects of some factors and especially factor interactions on the die roll. Recent changes in the field of artificial intelligence motivate the hybrid use of the finite element method and artificial neural networks to account for these non-considered parameters. Therefore, a set of simulations using a validated finite element model of fine blanking is firstly used to train an artificial neural network. Then the artificial neural network is trained with thousands of experimental trials. Thus, the objective of this contribution is to develop an artificial neural network that reliably predicts the die roll. Therefore, in this contribution, the setup of a fully parameterized 2D FE model is presented that will be used for batch training of an artificial neural network. The FE model enables an automatic variation of the edge radii of blank punch and die plate, the counter and blank holder force, the sheet metal thickness and part diameter, V-ring height and position, cutting velocity as well as material parameters covered by the Hensel-Spittel model for 16MnCr5 (1.7131, AISI/SAE 5115). The FE model is validated using experimental trails. The results of this contribution is a FE model suitable to perform 9.623 simulations and to pass the simulated die roll width and height automatically to an artificial neural network.

Advanced Artificial Science. The development of an artificial science and engineering research infrastructure to facilitate innovative computational modeling, analysis, and application to interdisciplinary areas of scientific investigation.

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

Saffer, Shelley

2014-12-01

This is a final report of the DOE award DE-SC0001132, Advanced Artificial Science. The development of an artificial science and engineering research infrastructure to facilitate innovative computational modeling, analysis, and application to interdisciplinary areas of scientific investigation. This document describes the achievements of the goals, and resulting research made possible by this award.

Artificial Neural Networks: A New Approach to Predicting Application Behavior.

ERIC Educational Resources Information Center

Gonzalez, Julie M. Byers; DesJardins, Stephen L.

2002-01-01

Applied the technique of artificial neural networks to predict which students were likely to apply to one research university. Compared the results to the traditional analysis tool, logistic regression modeling. Found that the addition of artificial intelligence models was a useful new tool for predicting student application behavior. (EV)

Transforming Systems Engineering through Model-Centric Engineering

DTIC Science & Technology

2018-02-28

intelligence (e.g., Artificial Intelligence , etc.), because they provide a means for representing knowledge. We see these capabilities coming to use in both...level, including:  Performance is measured by degree of success of a mission  Artificial Intelligence (AI) is applied to counterparties so that they...Modeling, Artificial Intelligence , Simulation and Modeling, 1989. [140] SAE ARP4761. Guidelines and Methods for Conducting the Safety Assessment Process

Adaptive Modeling and Real-Time Simulation

DTIC Science & Technology

1984-01-01

34 Artificial Inteligence , Vol. 13, pp. 27-39 (1980). Describes circumscription which is just the assumption that everything that is known to have a particular... Artificial Intelligence Truth Maintenance Planning Resolution Modeling Wcrld Models ~ .. ~2.. ASSTR AT (Coninue n evrse sieIf necesaran Identfy by...represents a marriage of (1) the procedural-network st, planning technology developed in artificial intelligence with (2) the PERT/CPM technology developed in

A Multiagent Based Model for Tactical Planning

DTIC Science & Technology

2002-10-01

Pub. Co. 1985. [10] Castillo, J.M. Aproximación mediante procedimientos de Inteligencia Artificial al planeamiento táctico. Doctoral Thesis...been developed under the same conceptual model and using similar Artificial Intelligence Tools. We use four different stimulus/response agents in...The conceptual model is built on base of the Agents theory. To implement the different agents we have used Artificial Intelligence techniques such

Climate and Leishmaniasis in French Guiana

PubMed Central

Roger, Amaury; Nacher, Mathieu; Hanf, Matthieu; Drogoul, Anne Sophie; Adenis, Antoine; Basurko, Celia; Dufour, Julie; Sainte Marie, Dominique; Blanchet, Denis; Simon, Stephane; Carme, Bernard; Couppié, Pierre

2013-01-01

To study the link between climatic variables and the incidence of leishmaniasis a study was conducted in Cayenne, French Guiana. Patients infected between January 1994 and December 2010. Meteorological data were studied in relation to the incidence of leishmaniasis using an ARIMA model. In the final model, the infections were negatively correlated with rainfall (with a 2-month lag) and with the number of days with rainfall > 50 mm (lags of 4 and 7 months). The variables that were positively correlated were temperature and the Multivariate El Niño Southern Oscillation Index with lags of 8 and 4 months, respectively. Significantly greater correlations were observed in March for rainfall and in November for the Multivariate El Niño/Southern Oscillation Index. Climate thus seems to be a non-negligible explanatory variable for the fluctuations of leishmaniasis. A decrease in rainfall is linked to increased cases 2 months later. This easily perceptible point could lead to an interesting prevention message. PMID:23939706

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.

Lethal firearm-related violence against Canadian women: did tightening gun laws have an impact on women's health and safety?

PubMed

McPhedran, Samara; Mauser, Gary

2013-01-01

Domestic violence remains a significant public health issue around the world, and policy makers continually strive to implement effective legislative frameworks to reduce lethal violence against women. This article examines whether the 1995 Firearms Act (Bill C-68) had a significant impact on female firearm homicide victimization rates in Canada. Time series of gender-disaggregated data from 1974 to 2009 were examined. Two different analytic approaches were used: the autoregressive integrated moving average (ARIMA) modelling and the Zivot-Andrews (ZA) structural breakpoint tests. There was little evidence to suggest that increased firearms legislation in Canada had a significant impact on preexisting trends in lethal firearm violence against women. These results do not support the view that increasing firearms legislation is associated with a reduced incidence of firearm-related female domestic homicide victimization.

Ozone and its projection in regard to climate change

NASA Astrophysics Data System (ADS)

Melkonyan, Ani; Wagner, Patrick

2013-03-01

In this paper, the dependence of ozone-forming potential on temperature was analysed based on data from two stations (with an industrial and rural background, respectively) in North Rhine-Westphalia, Germany, for the period of 1983-2007. After examining the interrelations between ozone, NOx and temperature, a projection of the days with ozone exceedance (over a limit value of a daily maximum 8-h average ≥ 120 μg m-3 for 25 days per year averaged for 3 years) in terms of global climate change was made using probability theory and an autoregression integrated moving average (ARIMA) model. The results show that with a temperature increase of 3 K, the frequency of days when ozone exceeds its limit value will increase by 135% at the industrial station and by 87% at the rural background station.

Preliminary evidence for the influence of physiography and scale upon the autocorrelation function of remotely sensed data

NASA Technical Reports Server (NTRS)

Labovitz, M. L.; Toll, D. L.; Kennard, R. E.

1980-01-01

Previously established results demonstrate that LANDSAT data are autocorrelated and can be described by a univariate linear stochastic process known as auto-regressive-integrated-moving-average model of degree 1, 0, 1 or ARIMA (1, 0, 1). This model has two coefficients of interest for interpretation phi(1) and theta(1). In a comparison of LANDSAT thematic mapper simulator (TMS) data and LANDSAT MSS data several results were established: (1) The form of the relatedness as described by this model is not dependent upon system look angle or pixel size. (2) The phi(1) coefficient increases with decreasing pixel size and increasing topographic complexity. (3) Changes in topography have a greater influence upon phi(1) than changes in land cover class. (4) The theta(1) seems to vary with the amount of atmospheric haze. These patterns of variation in phi(1) and theta(1) are potentially exploitable by the remote sensing community to yield stochastically independent sets of observations, characterize topography, and reduce the number of bytes needed to store remotely sensed data.

Identification of mathematical model of human breathing in system “Artificial lungs – self-contained breathing apparatus”

NASA Astrophysics Data System (ADS)

Onevsky, P. M.; Onevsky, M. P.; Pogonin, V. A.

2018-03-01

The structure and mathematical models of the main subsystems of the control system of the “Artificial Lungs” are presented. This structure implements the process of imitation of human external respiration in the system “Artificial lungs - self-contained breathing apparatus”. A presented algorithm for parametric identification of the model is based on spectral operators, which allows using it in real time.

Plant Growth Models Using Artificial Neural Networks

NASA Technical Reports Server (NTRS)

Bubenheim, David

1997-01-01

In this paper, we descrive our motivation and approach to devloping models and the neural network architecture. Initial use of the artificial neural network for modeling the single plant process of transpiration is presented.

Public broadcasting, media engagement, and 2-1-1: using mass communication to increase the use of social services.

PubMed

Shah, Dhavan V; McLeod, Douglas M; Rojas, Hernando; Sayre, Benjamin G; Vraga, Emily; Scholl, Rosanne M; Jones, Clive; Shaw, Amy

2012-12-01

The 2008-2009 subprime mortgage crisis was catastrophic, not only for the global economy but for families across the social spectrum. The resultant economic upheaval threatened the livelihoods, well-being, and health of many citizens, who were often unsure where to turn for help. At this critical juncture, public broadcasting stations worked to connect viewers to support resources through 2-1-1. This study was designed to evaluate the ability of public broadcasting to increase the use of information and referral services. Autoregressive integrated moving average (ARIMA) modeling and regression analysis document the relationship between public broadcasting initiatives and 2-1-1 call volume in 35 highly affected U.S. markets. Time-series data from St. Louis MO were collected and analyzed in 2008. Station-level data from across the nation were collected during 2009-2010 and analyzed in 2010. ARIMA results show a distinct linkage between the timing and duration of Channel 9 in St. Louis MO (KETC) programming and a subsequent (approximately 400%) increase in 2-1-1 calls regarding financial services and assistance. Regression path analysis not only found evidence of this same effect nationally but also showed that differences in the broadcaster's orientation and approach mediated effects. Specifically, stations' orientations toward engagement were mediated through strong outreach strategies to increase 2-1-1 use. This study documents the ability of public broadcasting to help citizens in need connect with social resources through 2-1-1 services. By focusing attention on the mortgage crisis and its attendant consequences, and by publicizing 2-1-1 services as a gateway to supportive resources, public broadcasters fostered linkages between those in need and social resources. Moreover, the level of a station's commitment to engaging citizens had a strong bearing on the success of its programming initiatives and community partnerships with organizations such as 2-1-1. Copyright © 2012 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.

Forecasting Japanese encephalitis incidence from historical morbidity patterns: Statistical analysis with 27 years of observation in Assam, India.

PubMed

Handique, Bijoy K; Khan, Siraj A; Mahanta, J; Sudhakar, S

2014-09-01

Japanese encephalitis (JE) is one of the dreaded mosquito-borne viral diseases mostly prevalent in south Asian countries including India. Early warning of the disease in terms of disease intensity is crucial for taking adequate and appropriate intervention measures. The present study was carried out in Dibrugarh district in the state of Assam located in the northeastern region of India to assess the accuracy of selected forecasting methods based on historical morbidity patterns of JE incidence during the past 22 years (1985-2006). Four selected forecasting methods, viz. seasonal average (SA), seasonal adjustment with last three observations (SAT), modified method adjusting long-term and cyclic trend (MSAT), and autoregressive integrated moving average (ARIMA) have been employed to assess the accuracy of each of the forecasting methods. The forecasting methods were validated for five consecutive years from 2007-2012 and accuracy of each method has been assessed. The forecasting method utilising seasonal adjustment with long-term and cyclic trend emerged as best forecasting method among the four selected forecasting methods and outperformed the even statistically more advanced ARIMA method. Peak of the disease incidence could effectively be predicted with all the methods, but there are significant variations in magnitude of forecast errors among the selected methods. As expected, variation in forecasts at primary health centre (PHC) level is wide as compared to that of district level forecasts. The study showed that adopted forecasting techniques could reasonably forecast the intensity of JE cases at PHC level without considering the external variables. The results indicate that the understanding of long-term and cyclic trend of the disease intensity will improve the accuracy of the forecasts, but there is a need for making the forecast models more robust to explain sudden variation in the disease intensity with detail analysis of parasite and host population dynamics.

Modeling of mass transfer of Phospholipids in separation process with supercritical CO2 fluid by RBF artificial neural networks

USDA-ARS?s Scientific Manuscript database

An artificial Radial Basis Function (RBF) neural network model was developed for the prediction of mass transfer of the phospholipids from canola meal in supercritical CO2 fluid. The RBF kind of artificial neural networks (ANN) with orthogonal least squares (OLS) learning algorithm were used for mod...

Effects of artificial gravity on the cardiovascular system: Computational approach

NASA Astrophysics Data System (ADS)

Diaz Artiles, Ana; Heldt, Thomas; Young, Laurence R.

2016-09-01

Artificial gravity has been suggested as a multisystem countermeasure against the negative effects of weightlessness. However, many questions regarding the appropriate configuration are still unanswered, including optimal g-level, angular velocity, gravity gradient, and exercise protocol. Mathematical models can provide unique insight into these questions, particularly when experimental data is very expensive or difficult to obtain. In this research effort, a cardiovascular lumped-parameter model is developed to simulate the short-term transient hemodynamic response to artificial gravity exposure combined with ergometer exercise, using a bicycle mounted on a short-radius centrifuge. The model is thoroughly described and preliminary simulations are conducted to show the model capabilities and potential applications. The model consists of 21 compartments (including systemic circulation, pulmonary circulation, and a cardiac model), and it also includes the rapid cardiovascular control systems (arterial baroreflex and cardiopulmonary reflex). In addition, the pressure gradient resulting from short-radius centrifugation is captured in the model using hydrostatic pressure sources located at each compartment. The model also includes the cardiovascular effects resulting from exercise such as the muscle pump effect. An initial set of artificial gravity simulations were implemented using the Massachusetts Institute of Technology (MIT) Compact-Radius Centrifuge (CRC) configuration. Three centripetal acceleration (artificial gravity) levels were chosen: 1 g, 1.2 g, and 1.4 g, referenced to the subject's feet. Each simulation lasted 15.5 minutes and included a baseline period, the spin-up process, the ergometer exercise period (5 minutes of ergometer exercise at 30 W with a simulated pedal cadence of 60 RPM), and the spin-down process. Results showed that the cardiovascular model is able to predict the cardiovascular dynamics during gravity changes, as well as the expected steady-state cardiovascular behavior during sustained artificial gravity and exercise. Further validation of the model was performed using experimental data from the combined exercise and artificial gravity experiments conducted on the MIT CRC, and these results will be presented separately in future publications. This unique computational framework can be used to simulate a variety of centrifuge configuration and exercise intensities to improve understanding and inform decisions about future implementation of artificial gravity in space.

Draft Genome Sequence of Meiothermus ruber H328, Which Degrades Chicken Feathers, and Identification of Proteases and Peptidases Responsible for Degradation

PubMed Central

Inada, Shuhei

2013-01-01

Meiothermus ruber H328 was isolated from Arima Hot Springs, Kobe, Japan, as a moderate thermophile. It has a strong ability to degrade intact chicken feathers. The enzymatic mechanism of the strain for feather degradation is unclear. The draft genome suggests potent enzyme candidates for degradation of keratin, a hard-to-degrade protein found in feathers. PMID:23640376

Career Planning: Individual and Organizational Perspectives.

DTIC Science & Technology

1981-07-01

1978; Louis, 1980a). The substantial impact of the transition period and the increasingly prevalent occurrence of major worklife changes involving...Individual and Organization~al "P CO/f RED Perspectivesi /Tcnia_ 00WE 7. AUHOR~s III CONTRACT OR GRANT NUMSCR(s) James K.Arima (Ed.) 9. PERFORMING ...participants and their superb contributions. Many thanks are due to Karen Brown for typing the manuscript and performing the many chores incidental to its

Multigrid solvers and multigrid preconditioners for the solution of variational data assimilation problems

NASA Astrophysics Data System (ADS)

Debreu, Laurent; Neveu, Emilie; Simon, Ehouarn; Le Dimet, Francois Xavier; Vidard, Arthur

2014-05-01

In order to lower the computational cost of the variational data assimilation process, we investigate the use of multigrid methods to solve the associated optimal control system. On a linear advection equation, we study the impact of the regularization term on the optimal control and the impact of discretization errors on the efficiency of the coarse grid correction step. We show that even if the optimal control problem leads to the solution of an elliptic system, numerical errors introduced by the discretization can alter the success of the multigrid methods. The view of the multigrid iteration as a preconditioner for a Krylov optimization method leads to a more robust algorithm. A scale dependent weighting of the multigrid preconditioner and the usual background error covariance matrix based preconditioner is proposed and brings significant improvements. [1] Laurent Debreu, Emilie Neveu, Ehouarn Simon, François-Xavier Le Dimet and Arthur Vidard, 2014: Multigrid solvers and multigrid preconditioners for the solution of variational data assimilation problems, submitted to QJRMS, http://hal.inria.fr/hal-00874643 [2] Emilie Neveu, Laurent Debreu and François-Xavier Le Dimet, 2011: Multigrid methods and data assimilation - Convergence study and first experiments on non-linear equations, ARIMA, 14, 63-80, http://intranet.inria.fr/international/arima/014/014005.html

Computational Models of Neuron-Astrocyte Interactions Lead to Improved Efficacy in the Performance of Neural Networks

PubMed Central

Alvarellos-González, Alberto; Pazos, Alejandro; Porto-Pazos, Ana B.

2012-01-01

The importance of astrocytes, one part of the glial system, for information processing in the brain has recently been demonstrated. Regarding information processing in multilayer connectionist systems, it has been shown that systems which include artificial neurons and astrocytes (Artificial Neuron-Glia Networks) have well-known advantages over identical systems including only artificial neurons. Since the actual impact of astrocytes in neural network function is unknown, we have investigated, using computational models, different astrocyte-neuron interactions for information processing; different neuron-glia algorithms have been implemented for training and validation of multilayer Artificial Neuron-Glia Networks oriented toward classification problem resolution. The results of the tests performed suggest that all the algorithms modelling astrocyte-induced synaptic potentiation improved artificial neural network performance, but their efficacy depended on the complexity of the problem. PMID:22649480

Computational models of neuron-astrocyte interactions lead to improved efficacy in the performance of neural networks.

PubMed

Alvarellos-González, Alberto; Pazos, Alejandro; Porto-Pazos, Ana B

2012-01-01

The importance of astrocytes, one part of the glial system, for information processing in the brain has recently been demonstrated. Regarding information processing in multilayer connectionist systems, it has been shown that systems which include artificial neurons and astrocytes (Artificial Neuron-Glia Networks) have well-known advantages over identical systems including only artificial neurons. Since the actual impact of astrocytes in neural network function is unknown, we have investigated, using computational models, different astrocyte-neuron interactions for information processing; different neuron-glia algorithms have been implemented for training and validation of multilayer Artificial Neuron-Glia Networks oriented toward classification problem resolution. The results of the tests performed suggest that all the algorithms modelling astrocyte-induced synaptic potentiation improved artificial neural network performance, but their efficacy depended on the complexity of the problem.

Nondestructive pavement evaluation using ILLI-PAVE based artificial neural network models.

DOT National Transportation Integrated Search

2008-09-01

The overall objective in this research project is to develop advanced pavement structural analysis models for more accurate solutions with fast computation schemes. Soft computing and modeling approaches, specifically the Artificial Neural Network (A...

Modeling a thick unsaturated zone at San Gorgonio Pass, California: lessons learned after five years of artificial recharge

USGS Publications Warehouse

Flint, Alan L.; Ellett, Kevin M.; Christensen, Allen H.; Martin, Peter

2012-01-01

The information flow among the tasks of framework assessment, numerical modeling, model forecasting and hind casting, and system-performance monitoring is illustrated. Results provide an understanding of artificial recharge in high-altitude desert settings where large vertical distances may separate application ponds from their target aquifers.Approximately 3.8 million cubic meters of surface water was applied to spreading ponds from 2003–2007 to artificially recharge the underlying aquifer through a 200-meter thick unsaturated zone in the San Gorgonio Pass area in southern California. A study was conducted between 1997 and 2003, and a numerical model was developed to help determine the suitability of the site for artificial recharge. Ongoing monitoring results indicated that the existing model needed to be modified and recalibrated to more accurately predict artificial recharge at the site. The objective of this work was to recalibrate the model by using observation of the application rates, the rise and fall of the water level above a perching layer, and the approximate arrival time to the water table during the 5-yr monitoring period following initiation of long-term artificial recharge. Continuous monitoring of soil-matric potential, temperature, and water levels beneath the site indicated that artificial recharge reached the underlying water table between 3.75 and 4.5 yr after the initial application of the recharge water. The model was modified to allow the simulation to more adequately match the perching layer dynamics and the time of arrival at the water table. The instrumentation also showed that the lag time between changes in application of water at the surface and the response at the perching layer decreased from about 4 mo to less than 1 mo due to the wet-up of the unsaturated zone and the increase in relative permeability. The results of this study demonstrate the importance of iteratively monitoring and modeling the unsaturated zone in layered alluvial systems in the context of artificial recharge. They show that adequate geologic and hydraulic-property data on perching layers are critical to success. Continuous monitoring in the unsaturated and saturated zones beneath a site provides data to develop and constrain numerical models, better understand local unsaturated zone process, manage artificial recharge operations, and to determine the timing and volume of recoverable water for consumptive use.

Forecasting the mortality rates of Malaysian population using Heligman-Pollard model

NASA Astrophysics Data System (ADS)

Ibrahim, Rose Irnawaty; Mohd, Razak; Ngataman, Nuraini; Abrisam, Wan Nur Azifah Wan Mohd

2017-08-01

Actuaries, demographers and other professionals have always been aware of the critical importance of mortality forecasting due to declining trend of mortality and continuous increases in life expectancy. Heligman-Pollard model was introduced in 1980 and has been widely used by researchers in modelling and forecasting future mortality. This paper aims to estimate an eight-parameter model based on Heligman and Pollard's law of mortality. Since the model involves nonlinear equations that are explicitly difficult to solve, the Matrix Laboratory Version 7.0 (MATLAB 7.0) software will be used in order to estimate the parameters. Statistical Package for the Social Sciences (SPSS) will be applied to forecast all the parameters according to Autoregressive Integrated Moving Average (ARIMA). The empirical data sets of Malaysian population for period of 1981 to 2015 for both genders will be considered, which the period of 1981 to 2010 will be used as "training set" and the period of 2011 to 2015 as "testing set". In order to investigate the accuracy of the estimation, the forecast results will be compared against actual data of mortality rates. The result shows that Heligman-Pollard model fit well for male population at all ages while the model seems to underestimate the mortality rates for female population at the older ages.

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.

Artificial retina model for the retinally blind based on wavelet transform

NASA Astrophysics Data System (ADS)

Zeng, Yan-an; Song, Xin-qiang; Jiang, Fa-gang; Chang, Da-ding

2007-01-01

Artificial retina is aimed for the stimulation of remained retinal neurons in the patients with degenerated photoreceptors. Microelectrode arrays have been developed for this as a part of stimulator. Design such microelectrode arrays first requires a suitable mathematical method for human retinal information processing. In this paper, a flexible and adjustable human visual information extracting model is presented, which is based on the wavelet transform. With the flexible of wavelet transform to image information processing and the consistent to human visual information extracting, wavelet transform theory is applied to the artificial retina model for the retinally blind. The response of the model to synthetic image is shown. The simulated experiment demonstrates that the model behaves in a manner qualitatively similar to biological retinas and thus may serve as a basis for the development of an artificial retina.

Cognitive computing and eScience in health and life science research: artificial intelligence and obesity intervention programs.

PubMed

Marshall, Thomas; Champagne-Langabeer, Tiffiany; Castelli, Darla; Hoelscher, Deanna

2017-12-01

To present research models based on artificial intelligence and discuss the concept of cognitive computing and eScience as disruptive factors in health and life science research methodologies. The paper identifies big data as a catalyst to innovation and the development of artificial intelligence, presents a framework for computer-supported human problem solving and describes a transformation of research support models. This framework includes traditional computer support; federated cognition using machine learning and cognitive agents to augment human intelligence; and a semi-autonomous/autonomous cognitive model, based on deep machine learning, which supports eScience. The paper provides a forward view of the impact of artificial intelligence on our human-computer support and research methods in health and life science research. By augmenting or amplifying human task performance with artificial intelligence, cognitive computing and eScience research models are discussed as novel and innovative systems for developing more effective adaptive obesity intervention programs.

Modelling and forecasting long-term dynamics of Western Baltic macrobenthic fauna in relation to climate signals and environmental change

NASA Astrophysics Data System (ADS)

Gröger, Joachim; Rumohr, Heye

2006-05-01

Long-term macrobenthos data from Kiel Bight in the Western Baltic collected between 1968 and 2000 have been correlated with the winter NAO index (North Atlantic Oscillation Index) and other environmental data such as temperature, salinity and oxygen content in the bottom water in order to detect systematic patterns related to so far unexplained abiotic signals in the dynamics of zoobenthic species assemblages. The benthos data come from a cluster of five stations (Süderfahrt/ Millionenviertel) in Kiel Bay. Our investigations concentrated on the macrobenthic dynamics with a focus on the number of species m - 2 (species richness). Using logarithms and the time series analysis approach of Box/Jenkins (ARIMA modelling, transfer function modelling) it was shown that species richness was strongly influenced by the winter NAO (adjusted for a linear time trend within the 1968-2000 period) and salinity (with a shift/lag of four years). Bootstrapping experiments (i.e. sampling from the error process) and analysis of prediction power (by means of the one- or more-years leaving-out method) showed that the parameter estimates behaved in a stable way, leading to a relatively robust model.

[Approximation to the dynamics of meningococcal meningitis through dynamic systems and time series].

PubMed

Canals, M

1996-02-01

Meningococcal meningitis is subjected to epidemiological surveillance due to its severity and the occasional presentation of epidemic outbreaks. This work analyses previous disease models, generate new ones and analyses monthly cases using ARIMA time series models. The results show that disease dynamics for closed populations is epidemic and the epidemic size is related to the proportion of carriers and the transmissiveness of the agent. In open populations, disease dynamics depends on the admission rate of susceptible and the relative admission of infected individuals. Our model considers a logistic populational growth and carrier admission proportional to populational size, generating an endemic dynamics. Considering a non-instantaneous system response, a greater realism is obtained establishing that the endemic situation may present a dynamics highly sensitive to initial conditions, depending on the transmissiveness and proportion of susceptible individuals in the population. Time series model showed an adequate predictive capacity in terms no longer than 10 months. The lack of long term predictability was attributed to local changes in the proportion of carriers or on transmissiveness that lead to chaotic dynamics over a seasonal pattern. Predictions for 1995 and 1996 were obtained.

Neural networks to predict exosphere temperature corrections

NASA Astrophysics Data System (ADS)

Choury, Anna; Bruinsma, Sean; Schaeffer, Philippe

2013-10-01

Precise orbit prediction requires a forecast of the atmospheric drag force with a high degree of accuracy. Artificial neural networks are universal approximators derived from artificial intelligence and are widely used for prediction. This paper presents a method of artificial neural networking for prediction of the thermosphere density by forecasting exospheric temperature, which will be used by the semiempirical thermosphere Drag Temperature Model (DTM) currently developed. Artificial neural network has shown to be an effective and robust forecasting model for temperature prediction. The proposed model can be used for any mission from which temperature can be deduced accurately, i.e., it does not require specific training. Although the primary goal of the study was to create a model for 1 day ahead forecast, the proposed architecture has been generalized to 2 and 3 days prediction as well. The impact of artificial neural network predictions has been quantified for the low-orbiting satellite Gravity Field and Steady-State Ocean Circulation Explorer in 2011, and an order of magnitude smaller orbit errors were found when compared with orbits propagated using the thermosphere model DTM2009.

Moderate hypothermia technique for chronic implantation of a total artificial heart in calves.

PubMed

Karimov, Jamshid H; Grady, Patrick; Sinkewich, Martin; Sunagawa, Gengo; Dessoffy, Raymond; Byram, Nicole; Moazami, Nader; Fukamachi, Kiyotaka

2017-06-01

The benefit of whole-body hypothermia in preventing ischemic injury during cardiac surgical operations is well documented. However, application of hypothermia during in vivo total artificial heart implantation has not become widespread because of limited understanding of the proper techniques and restrictions implied by constitutional and physiological characteristics specific to each animal model. Similarly, the literature on hypothermic set-up in total artificial heart implantation has also been limited. Herein we present our experience using hypothermia in bovine models implanted with the Cleveland Clinic continuous-flow total artificial heart.

Prediction of U-Mo dispersion nuclear fuels with Al-Si alloy using artificial neural network

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

Susmikanti, Mike, E-mail: mike@batan.go.id; Sulistyo, Jos, E-mail: soj@batan.go.id

2014-09-30

Dispersion nuclear fuels, consisting of U-Mo particles dispersed in an Al-Si matrix, are being developed as fuel for research reactors. The equilibrium relationship for a mixture component can be expressed in the phase diagram. It is important to analyze whether a mixture component is in equilibrium phase or another phase. The purpose of this research it is needed to built the model of the phase diagram, so the mixture component is in the stable or melting condition. Artificial neural network (ANN) is a modeling tool for processes involving multivariable non-linear relationships. The objective of the present work is to developmore » code based on artificial neural network models of system equilibrium relationship of U-Mo in Al-Si matrix. This model can be used for prediction of type of resulting mixture, and whether the point is on the equilibrium phase or in another phase region. The equilibrium model data for prediction and modeling generated from experimentally data. The artificial neural network with resilient backpropagation method was chosen to predict the dispersion of nuclear fuels U-Mo in Al-Si matrix. This developed code was built with some function in MATLAB. For simulations using ANN, the Levenberg-Marquardt method was also used for optimization. The artificial neural network is able to predict the equilibrium phase or in the phase region. The develop code based on artificial neural network models was built, for analyze equilibrium relationship of U-Mo in Al-Si matrix.« less

The use of artificial neural networks in experimental data acquisition and aerodynamic design

NASA Technical Reports Server (NTRS)

Meade, Andrew J., Jr.

1991-01-01

It is proposed that an artificial neural network be used to construct an intelligent data acquisition system. The artificial neural networks (ANN) model has a potential for replacing traditional procedures as well as for use in computational fluid dynamics validation. Potential advantages of the ANN model are listed. As a proof of concept, the author modeled a NACA 0012 airfoil at specific conditions, using the neural network simulator NETS, developed by James Baffes of the NASA Johnson Space Center. The neural network predictions were compared to the actual data. It is concluded that artificial neural networks can provide an elegant and valuable class of mathematical tools for data analysis.

Color regeneration from reflective color sensor using an artificial intelligent technique.

PubMed

Saracoglu, Ömer Galip; Altural, Hayriye

2010-01-01

A low-cost optical sensor based on reflective color sensing is presented. Artificial neural network models are used to improve the color regeneration from the sensor signals. Analog voltages of the sensor are successfully converted to RGB colors. The artificial intelligent models presented in this work enable color regeneration from analog outputs of the color sensor. Besides, inverse modeling supported by an intelligent technique enables the sensor probe for use of a colorimetric sensor that relates color changes to analog voltages.

Introduction to Concepts in Artificial Neural Networks

NASA Technical Reports Server (NTRS)

Niebur, Dagmar

1995-01-01

This introduction to artificial neural networks summarizes some basic concepts of computational neuroscience and the resulting models of artificial neurons. The terminology of biological and artificial neurons, biological and machine learning and neural processing is introduced. The concepts of supervised and unsupervised learning are explained with examples from the power system area. Finally, a taxonomy of different types of neurons and different classes of artificial neural networks is presented.

Application of Time-series Model to Predict Groundwater Quality Parameters for Agriculture: (Plain Mehran Case Study)

NASA Astrophysics Data System (ADS)

Mehrdad Mirsanjari, Mir; Mohammadyari, Fatemeh

2018-03-01

Underground water is regarded as considerable water source which is mainly available in arid and semi arid with deficient surface water source. Forecasting of hydrological variables are suitable tools in water resources management. On the other hand, time series concepts is considered efficient means in forecasting process of water management. In this study the data including qualitative parameters (electrical conductivity and sodium adsorption ratio) of 17 underground water wells in Mehran Plain has been used to model the trend of parameters change over time. Using determined model, the qualitative parameters of groundwater is predicted for the next seven years. Data from 2003 to 2016 has been collected and were fitted by AR, MA, ARMA, ARIMA and SARIMA models. Afterward, the best model is determined using information criterion or Akaike (AIC) and correlation coefficient. After modeling parameters, the map of agricultural land use in 2016 and 2023 were generated and the changes between these years were studied. Based on the results, the average of predicted SAR (Sodium Adsorption Rate) in all wells in the year 2023 will increase compared to 2016. EC (Electrical Conductivity) average in the ninth and fifteenth holes and decreases in other wells will be increased. The results indicate that the quality of groundwater for Agriculture Plain Mehran will decline in seven years.

Standard representation and unified stability analysis for dynamic artificial neural network models.

PubMed

Kim, Kwang-Ki K; Patrón, Ernesto Ríos; Braatz, Richard D

2018-02-01

An overview is provided of dynamic artificial neural network models (DANNs) for nonlinear dynamical system identification and control problems, and convex stability conditions are proposed that are less conservative than past results. The three most popular classes of dynamic artificial neural network models are described, with their mathematical representations and architectures followed by transformations based on their block diagrams that are convenient for stability and performance analyses. Classes of nonlinear dynamical systems that are universally approximated by such models are characterized, which include rigorous upper bounds on the approximation errors. A unified framework and linear matrix inequality-based stability conditions are described for different classes of dynamic artificial neural network models that take additional information into account such as local slope restrictions and whether the nonlinearities within the DANNs are odd. A theoretical example shows reduced conservatism obtained by the conditions. Copyright © 2017. Published by Elsevier Ltd.

Model-Based Reasoning in the Detection of Satellite Anomalies

DTIC Science & Technology

1990-12-01

Conference on Artificial Intellegence . 1363-1368. Detroit, Michigan, August 89. Chu, Wei-Hai. "Generic Expert System Shell for Diagnostic Reasoning... Intellegence . 1324-1330. Detroit, Michigan, August 89. de Kleer, Johan and Brian C. Williams. "Diagnosing Multiple Faults," Artificial Intellegence , 32(1): 97...Benjamin Kuipers. "Model-Based Monitoring of Dynamic Systems," Proceedings of the Eleventh Intematianal Joint Conference on Artificial Intellegence . 1238

EV Charging Algorithm Implementation with User Price Preference

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

Wang, Bin; Hu, Boyang; Qiu, Charlie

2015-02-17

in this paper, we propose and implement a smart Electric Vehicle (EV) charging algorithm to control the EV charging infrastructures according to users’ price preferences. EVSE (Electric Vehicle Supply Equipment), equipped with bidirectional communication devices and smart meters, can be remotely monitored by the proposed charging algorithm applied to EV control center and mobile app. On the server side, ARIMA model is utilized to fit historical charging load data and perform day-ahead prediction. A pricing strategy with energy bidding policy is proposed and implemented to generate a charging price list to be broadcasted to EV users through mobile app. Onmore » the user side, EV drivers can submit their price preferences and daily travel schedules to negotiate with Control Center to consume the expected energy and minimize charging cost simultaneously. The proposed algorithm is tested and validated through the experimental implementations in UCLA parking lots.« less

Multiresolution analysis of Bursa Malaysia KLCI time series

NASA Astrophysics Data System (ADS)

Ismail, Mohd Tahir; Dghais, Amel Abdoullah Ahmed

2017-05-01

In general, a time series is simply a sequence of numbers collected at regular intervals over a period. Financial time series data processing is concerned with the theory and practice of processing asset price over time, such as currency, commodity data, and stock market data. The primary aim of this study is to understand the fundamental characteristics of selected financial time series by using the time as well as the frequency domain analysis. After that prediction can be executed for the desired system for in sample forecasting. In this study, multiresolution analysis which the assist of discrete wavelet transforms (DWT) and maximal overlap discrete wavelet transform (MODWT) will be used to pinpoint special characteristics of Bursa Malaysia KLCI (Kuala Lumpur Composite Index) daily closing prices and return values. In addition, further case study discussions include the modeling of Bursa Malaysia KLCI using linear ARIMA with wavelets to address how multiresolution approach improves fitting and forecasting results.

Mortality impact of extreme winter temperatures

NASA Astrophysics Data System (ADS)

Díaz, Julio; García, Ricardo; López, César; Linares, Cristina; Tobías, Aurelio; Prieto, Luis

2005-01-01

During the last few years great attention has been paid to the evaluation of the impact of extreme temperatures on human health. This paper examines the effect of extreme winter temperature on mortality in Madrid for people older than 65, using ARIMA and GAM models. Data correspond to 1,815 winter days over the period 1986 1997, during which time a total of 133,000 deaths occurred. The daily maximum temperature (Tmax) was shown to be the best thermal indicator of the impact of climate on mortality. When total mortality was considered, the maximum impact occured 7 8 days after a temperature extreme; for circulatory diseases the lag was between 7 and 14 days. When respiratory causes were considered, two mortality peaks were evident at 4 5 and 11 days. When the impact of winter extreme temperatures was compared with that associated with summer extremes, it was found to occur over a longer term, and appeared to be more indirect.

Impacts of Climatic Variability on Vibrio parahaemolyticus Outbreaks in Taiwan

PubMed Central

Hsiao, Hsin-I; Jan, Man-Ser; Chi, Hui-Ju

2016-01-01

This study aimed to investigate and quantify the relationship between climate variation and incidence of Vibrio parahaemolyticus in Taiwan. Specifically, seasonal autoregressive integrated moving average (ARIMA) models (including autoregression, seasonality, and a lag-time effect) were employed to predict the role of climatic factors (including temperature, rainfall, relative humidity, ocean temperature and ocean salinity) on the incidence of V. parahaemolyticus in Taiwan between 2000 and 2011. The results indicated that average temperature (+), ocean temperature (+), ocean salinity of 6 months ago (+), maximum daily rainfall (current (−) and one month ago (−)), and average relative humidity (current and 9 months ago (−)) had significant impacts on the incidence of V. parahaemolyticus. Our findings offer a novel view of the quantitative relationship between climate change and food poisoning by V. parahaemolyticus in Taiwan. An early warning system based on climate change information for the disease control management is required in future. PMID:26848675

On nonstationarity and antipersistency in global temperature series

NASA Astrophysics Data System (ADS)

KäRner, O.

2002-10-01

Statistical analysis is carried out for satellite-based global daily tropospheric and stratospheric temperature anomaly and solar irradiance data sets. Behavior of the series appears to be nonstationary with stationary daily increments. Estimating long-range dependence between the increments reveals a remarkable difference between the two temperature series. Global average tropospheric temperature anomaly behaves similarly to the solar irradiance anomaly. Their daily increments show antipersistency for scales longer than 2 months. The property points at a cumulative negative feedback in the Earth climate system governing the tropospheric variability during the last 22 years. The result emphasizes a dominating role of the solar irradiance variability in variations of the tropospheric temperature and gives no support to the theory of anthropogenic climate change. The global average stratospheric temperature anomaly proceeds like a 1-dim random walk at least up to 11 years, allowing good presentation by means of the autoregressive integrated moving average (ARIMA) models for monthly series.

Changing incidence of psychotic disorders among the young in Zurich.

PubMed

Ajdacic-Gross, Vladeta; Lauber, Christoph; Warnke, Inge; Haker, Helene; Murray, Robin M; Rössler, Wulf

2007-09-01

There is controversy over whether the incidence rates of schizophrenia and psychotic disorders have changed in recent decades. To detect deviations from trends in incidence, we analysed admission data of patients with an ICD-8/9/10 diagnosis of psychotic disorders in the Canton Zurich / Switzerland, for the period 1977-2005. The data was derived from the central psychiatric register of the Canton Zurich. Ex-post forecasting with ARIMA (Autoregressive Integrated Moving Average) models was used to assess departures from existing trends. In addition, age-period-cohort analysis was applied to determine hidden birth cohort effects. First admission rates of patients with psychotic disorders were constant in men and showed a downward trend in women. However, the rates in the youngest age groups showed a strong increase in the second half of the 1990's. The trend reversal among the youngest age groups coincides with the increased use of cannabis among young Swiss in the 1990's.

Impacts of Climatic Variability on Vibrio parahaemolyticus Outbreaks in Taiwan.

PubMed

Hsiao, Hsin-I; Jan, Man-Ser; Chi, Hui-Ju

2016-02-03

This study aimed to investigate and quantify the relationship between climate variation and incidence of Vibrio parahaemolyticus in Taiwan. Specifically, seasonal autoregressive integrated moving average (ARIMA) models (including autoregression, seasonality, and a lag-time effect) were employed to predict the role of climatic factors (including temperature, rainfall, relative humidity, ocean temperature and ocean salinity) on the incidence of V. parahaemolyticus in Taiwan between 2000 and 2011. The results indicated that average temperature (+), ocean temperature (+), ocean salinity of 6 months ago (+), maximum daily rainfall (current (-) and one month ago (-)), and average relative humidity (current and 9 months ago (-)) had significant impacts on the incidence of V. parahaemolyticus. Our findings offer a novel view of the quantitative relationship between climate change and food poisoning by V. parahaemolyticus in Taiwan. An early warning system based on climate change information for the disease control management is required in future.

Development of Urban Inundation Warning Model at Cyclic Artificial Water Way in Song-do International City, Republic of Korea

NASA Astrophysics Data System (ADS)

Lee, T.; Lee, C.; Kim, H.

2016-12-01

Abstract Song-do international city was constructed by reclaiming land from the coastal waters of Yeonsu-gu, Incheon Metropolitan City, Republic of Korea. The □-shaped cyclic artificial water way has been considered for improving water quality, waterfront and internal drainage in Song-do international city. By improving water quality, various marine facilities, such as marina, artificial beach, marine terminal, and so on, will be set up around the artificial water way for the waterfront. Since the water stage of the artificial water way changes depending on water gates operations, it is necessary to develop an urban inundation warning model to evaluate safeties of the waterfront facilities and its passengers. By considering characteristics of urban watershed, we calculate discharge flowing into the water way using XP-SWMM model. As a result of estimating 100-year flood frequency, although there are slight differences in drainage sections, the maximum flood discharge occurs in 90-min rainfall duration. In order to consider impacts of tide and hydraulic structure, we establish Inland drainage plans through the analysis of unsteady flow using HEC-RAS. The urban inundation warning model is configured to issue a warning when the water plain elevation exceeds EL. 1.5m which is usually managed at EL. 1.0m. In this study, the design flood stage of artificial water way and urban inundation warning model are developed for Song-do international city, and therefore it is expected that a reliability of management and operation of the waterfront facilities is improved. Keywords : Artificial Water Way; Waterfront; Urban Inundation Warning Model. Acknowlegement This research was supported by a grant [MPSS-NH-2015-79] through the Disaster and Safety Management Institute funded by Ministry of Public Safety and Security of Korean government.

Artificial Neural Networks for Modeling Knowing and Learning in Science.

ERIC Educational Resources Information Center

Roth, Wolff-Michael

2000-01-01

Advocates artificial neural networks as models for cognition and development. Provides an example of how such models work in the context of a well-known Piagetian developmental task and school science activity: balance beam problems. (Contains 59 references.) (Author/WRM)

Artificial Neural Networks: an overview and their use in the analysis of the AMPHORA-3 dataset.

PubMed

Buscema, Paolo Massimo; Massini, Giulia; Maurelli, Guido

2014-10-01

The Artificial Adaptive Systems (AAS) are theories with which generative algebras are able to create artificial models simulating natural phenomenon. Artificial Neural Networks (ANNs) are the more diffused and best-known learning system models in the AAS. This article describes an overview of ANNs, noting its advantages and limitations for analyzing dynamic, complex, non-linear, multidimensional processes. An example of a specific ANN application to alcohol consumption in Spain, as part of the EU AMPHORA-3 project, during 1961-2006 is presented. Study's limitations are noted and future needed research using ANN methodologies are suggested.

Variable camber wing based on pneumatic artificial muscles

NASA Astrophysics Data System (ADS)

Yin, Weilong; Liu, Libo; Chen, Yijin; Leng, Jinsong

2009-07-01

As a novel bionic actuator, pneumatic artificial muscle has high power to weight ratio. In this paper, a variable camber wing with the pneumatic artificial muscle is developed. Firstly, the experimental setup to measure the static output force of pneumatic artificial muscle is designed. The relationship between the static output force and the air pressure is investigated. Experimental result shows the static output force of pneumatic artificial muscle decreases nonlinearly with increasing contraction ratio. Secondly, the finite element model of the variable camber wing is developed. Numerical results show that the tip displacement of the trailing-edge increases linearly with increasing external load and limited with the maximum static output force of pneumatic artificial muscles. Finally, the variable camber wing model is manufactured to validate the variable camber concept. Experimental result shows that the wing camber increases with increasing air pressure and that it compare very well with the FEM result.

Development and validation of deterioration models for concrete bridge decks - phase 1 : artificial intelligence models and bridge management system.

DOT National Transportation Integrated Search

2013-06-01

This research documents the development and evaluation of artificial neural network (ANN) models to predict the condition ratings of concrete highway bridge decks in Michigan. Historical condition assessments chronicled in the national bridge invento...

Systems in Science: Modeling Using Three Artificial Intelligence Concepts.

ERIC Educational Resources Information Center

Sunal, Cynthia Szymanski; Karr, Charles L.; Smith, Coralee; Sunal, Dennis W.

2003-01-01

Describes an interdisciplinary course focusing on modeling scientific systems. Investigates elementary education majors' applications of three artificial intelligence concepts used in modeling scientific systems before and after the course. Reveals a great increase in understanding of concepts presented but inconsistent application. (Author/KHR)

Artificial intelligence in the diagnosis of low back pain.

PubMed

Mann, N H; Brown, M D

1991-04-01

Computerized methods are used to recognize the characteristics of patient pain drawings. Artificial neural network (ANN) models are compared with expert predictions and traditional statistical classification methods when placing the pain drawings of low back pain patients into one of five clinically significant categories. A discussion is undertaken outlining the differences in these classifiers and the potential benefits of the ANN model as an artificial intelligence technique.

Actors: A Model of Concurrent Computation in Distributed Systems.

DTIC Science & Technology

1985-06-01

Artificial Intelligence Labora- tory of the Massachusetts Institute of Technology. Support for the labora- tory’s aritificial intelligence research is...RD-A157 917 ACTORS: A MODEL OF CONCURRENT COMPUTATION IN 1/3- DISTRIBUTED SY𔃿TEMS(U) MASSACHUSETTS INST OF TECH CRMBRIDGE ARTIFICIAL INTELLIGENCE ...Computation In Distributed Systems Gui A. Aghai MIT Artificial Intelligence Laboratory Thsdocument ha. been cipp-oved I= pblicrelease and sale; itsI

Generative Computer-Assisted Instruction and Artificial Intelligence. Report No. 5.

ERIC Educational Resources Information Center

Sinnott, Loraine T.

This paper reviews the state-of-the-art in generative computer-assisted instruction and artificial intelligence. It divides relevant research into three areas of instructional modeling: models of the subject matter; models of the learner's state of knowledge; and models of teaching strategies. Within these areas, work sponsored by Advanced…

Experience in using a numerical scheme with artificial viscosity at solving the Riemann problem for a multi-fluid model of multiphase flow

NASA Astrophysics Data System (ADS)

Bulovich, S. V.; Smirnov, E. M.

2018-05-01

The paper covers application of the artificial viscosity technique to numerical simulation of unsteady one-dimensional multiphase compressible flows on the base of the multi-fluid approach. The system of the governing equations is written under assumption of the pressure equilibrium between the "fluids" (phases). No interfacial exchange is taken into account. A model for evaluation of the artificial viscosity coefficient that (i) assumes identity of this coefficient for all interpenetrating phases and (ii) uses the multiphase-mixture Wood equation for evaluation of a scale speed of sound has been suggested. Performance of the artificial viscosity technique has been evaluated via numerical solution of a model problem of pressure discontinuity breakdown in a three-fluid medium. It has been shown that a relatively simple numerical scheme, explicit and first-order, combined with the suggested artificial viscosity model, predicts a physically correct behavior of the moving shock and expansion waves, and a subsequent refinement of the computational grid results in a monotonic approaching to an asymptotic time-dependent solution, without non-physical oscillations.

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.

Investigation of an artificial intelligence technology--Model trees. Novel applications for an immediate release tablet formulation database.

PubMed

Shao, Q; Rowe, R C; York, P

2007-06-01

This study has investigated an artificial intelligence technology - model trees - as a modelling tool applied to an immediate release tablet formulation database. The modelling performance was compared with artificial neural networks that have been well established and widely applied in the pharmaceutical product formulation fields. The predictability of generated models was validated on unseen data and judged by correlation coefficient R(2). Output from the model tree analyses produced multivariate linear equations which predicted tablet tensile strength, disintegration time, and drug dissolution profiles of similar quality to neural network models. However, additional and valuable knowledge hidden in the formulation database was extracted from these equations. It is concluded that, as a transparent technology, model trees are useful tools to formulators.

Consumption of fast food, sugar-sweetened beverages, artificially-sweetened beverages and allostatic load among young adults.

PubMed

van Draanen, Jenna; Prelip, Michael; Upchurch, Dawn M

2018-06-01

This study investigates the associations between recent consumption of fast foods, sugar-sweetened beverages, and artificially-sweetened beverages on level of allostatic load, a measure of cumulative biological risk, in young adults in the US. Data from Wave IV of the National Longitudinal Study of Adolescent to Adult Health were analyzed. Negative binomial regression models were used to estimate the associations between consumption of fast foods, sugar-sweetened, and artificially-sweetened beverages and allostatic load. Poisson and logistic regression models were used to estimate the associations between these diet parameters and combined biomarkers of physiological subsystems that comprise our measure of allostatic load. All analyses were weighted and findings are representative of young adults in the US, ages 24-34 in 2008 (n = 11,562). Consumption of fast foods, sugar-sweetened, and artificially-sweetened beverages were associated with higher allostatic load at a bivariate level. Accounting for demographics and medication use, only artificially-sweetened beverages remained significantly associated with allostatic load. When all three dietary components were simultaneously included in a model, both sugar- and artificially-sweetened beverage consumption were associated with higher allostatic load. Differences in allostatic load emerge early in the life course and young adults consuming sugar- or artificially-sweetened beverages have higher allostatic load, net of demographics and medication use. Public health messages to young adults may need to include cautions about both sugar- and artificially-sweetened beverages.

Simulation of Blood flow in Artificial Heart Valve Design through Left heart

NASA Astrophysics Data System (ADS)

Hafizah Mokhtar, N.; Abas, Aizat

2018-05-01

In this work, an artificial heart valve is designed for use in real heart with further consideration on the effect of thrombosis, vorticity, and stress. The design of artificial heart valve model is constructed by Computer-aided design (CAD) modelling and simulated using Computational fluid dynamic (CFD) software. The effect of blood flow pattern, velocity and vorticity of the artificial heart valve design has been analysed in this research work. Based on the results, the artificial heart valve design shows that it has a Doppler velocity index that is less than the allowable standards for the left heart with values of more than 0.30 and less than 2.2. These values are safe to be used as replacement of the human heart valve.

Forecasting daily lake levels using artificial intelligence approaches

NASA Astrophysics Data System (ADS)

Kisi, Ozgur; Shiri, Jalal; Nikoofar, Bagher

2012-04-01

Accurate prediction of lake-level variations is important for planning, design, construction, and operation of lakeshore structures and also in the management of freshwater lakes for water supply purposes. In the present paper, three artificial intelligence approaches, namely artificial neural networks (ANNs), adaptive-neuro-fuzzy inference system (ANFIS), and gene expression programming (GEP), were applied to forecast daily lake-level variations up to 3-day ahead time intervals. The measurements at the Lake Iznik in Western Turkey, for the period of January 1961-December 1982, were used for training, testing, and validating the employed models. The results obtained by the GEP approach indicated that it performs better than ANFIS and ANNs in predicting lake-level variations. A comparison was also made between these artificial intelligence approaches and convenient autoregressive moving average (ARMA) models, which demonstrated the superiority of GEP, ANFIS, and ANN models over ARMA models.

[Modelling the effect of local climatic variability on dengue transmission in Medellin (Colombia) by means of time series analysis].

PubMed

Rúa-Uribe, Guillermo L; Suárez-Acosta, Carolina; Chauca, José; Ventosilla, Palmira; Almanza, Rita

2013-09-01

Dengue fever is a major impact on public health vector-borne disease, and its transmission is influenced by entomological, sociocultural and economic factors. Additionally, climate variability plays an important role in the transmission dynamics. A large scientific consensus has indicated that the strong association between climatic variables and disease could be used to develop models to explain the incidence of the disease. To develop a model that provides a better understanding of dengue transmission dynamics in Medellin and predicts increases in the incidence of the disease. The incidence of dengue fever was used as dependent variable, and weekly climatic factors (maximum, mean and minimum temperature, relative humidity and precipitation) as independent variables. Expert Modeler was used to develop a model to better explain the behavior of the disease. Climatic variables with significant association to the dependent variable were selected through ARIMA models. The model explains 34% of observed variability. Precipitation was the climatic variable showing statistically significant association with the incidence of dengue fever, but with a 20 weeks delay. In Medellin, the transmission of dengue fever was influenced by climate variability, especially precipitation. The strong association dengue fever/precipitation allowed the construction of a model to help understand dengue transmission dynamics. This information will be useful to develop appropriate and timely strategies for dengue control.

Nonlinear Prediction Model for Hydrologic Time Series Based on Wavelet Decomposition

NASA Astrophysics Data System (ADS)

Kwon, H.; Khalil, A.; Brown, C.; Lall, U.; Ahn, H.; Moon, Y.

2005-12-01

Traditionally forecasting and characterizations of hydrologic systems is performed utilizing many techniques. Stochastic linear methods such as AR and ARIMA and nonlinear ones such as statistical learning theory based tools have been extensively used. The common difficulty to all methods is the determination of sufficient and necessary information and predictors for a successful prediction. Relationships between hydrologic variables are often highly nonlinear and interrelated across the temporal scale. A new hybrid approach is proposed for the simulation of hydrologic time series combining both the wavelet transform and the nonlinear model. The present model employs some merits of wavelet transform and nonlinear time series model. The Wavelet Transform is adopted to decompose a hydrologic nonlinear process into a set of mono-component signals, which are simulated by nonlinear model. The hybrid methodology is formulated in a manner to improve the accuracy of a long term forecasting. The proposed hybrid model yields much better results in terms of capturing and reproducing the time-frequency properties of the system at hand. Prediction results are promising when compared to traditional univariate time series models. An application of the plausibility of the proposed methodology is provided and the results conclude that wavelet based time series model can be utilized for simulating and forecasting of hydrologic variable reasonably well. This will ultimately serve the purpose of integrated water resources planning and management.

Three Gorges Dam: Impact of Water Level Changes on the Density of Schistosome-Transmitting Snail Oncomelania hupensis in Dongting Lake Area, China

PubMed Central

Wu, Jin-Yi; Zhou, Yi-Biao; Chen, Yue; Liang, Song; Li, Lin-Han; Zheng, Sheng-Bang; Zhu, Shao-ping; Ren, Guang-Hui; Song, Xiu-Xia; Jiang, Qing-Wu

2015-01-01

Background Schistosomiasis remains an important public health issue in China and worldwide. Oncomelania hupensis is the unique intermediate host of schistosoma japonicum, and its change influences the distribution of S. japonica. The Three Gorges Dam (TGD) has substantially changed the ecology and environment in the Dongting Lake region. This study investigated the impact of water level and elevation on the survival and habitat of the snails. Methods Data were collected for 16 bottomlands around 4 hydrological stations, which included water, density of living snails (form the Anxiang Station for Schistosomiasis Control) and elevation (from Google Earth). Based on the elevation, sixteen bottomlands were divided into 3 groups. ARIMA models were built to predict the density of living snails in different elevation areas. Results Before closure of TGD, 7 out of 9 years had a water level beyond the warning level at least once at Anxiang hydrological station, compared with only 3 out of 10 years after closure of TGD. There were two severe droughts that happened in 2006 and 2011, with much fewer number of flooding per year compared with other study years. Overall, there was a correlation between water level changing and density of living snails variation in all the elevations areas. The density of living snails in all elevations areas was decreasing after the TGD was built. The relationship between number of flooding per year and the density of living snails was more pronounced in the medium and high elevation areas; the density of living snails kept decreasing from 2003 to 2014. In low elevation area however, the density of living snails decreased after 2003 first and turned to increase after 2011. Our ARIMA prediction models indicated that the snails would not disappear in the Dongting Lake region in the next 7 years. In the low elevation area, the density of living snails would increase slightly, and then stabilize after the year 2017. In the medium elevation region, the change of the density of living snails would be more obvious and would increase till the year 2020. In the high elevation area, the density of living snails would remain stable after the year 2015. Conclusion The TGD influenced water levels and reduced the risk of flooding and the density of living snails in the study region. Based on our prediction models, the density of living snails in all elevations tends to be stabilized. Control of S. japonica would continue to be an important task in the study area in the coming decade. PMID:26114956

Artificial intelligence in process control: Knowledge base for the shuttle ECS model

NASA Technical Reports Server (NTRS)

Stiffler, A. Kent

1989-01-01

The general operation of KATE, an artificial intelligence controller, is outlined. A shuttle environmental control system (ECS) demonstration system for KATE is explained. The knowledge base model for this system is derived. An experimental test procedure is given to verify parameters in the model.

Nuclear Physics Activities in Asia and ANPhA

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

Sakai, H.

2011-05-06

On 18 July 2009 the Asian Nuclear Physics Association (ANPhA) has been officially launched in Beijing by the representatives from China, Korea, Japan and Vietnam. Since then Australia, India, Mongolia and Taiwan have joined to ANPhA and now the member country/region has increased to eight. Some activities and features on ANPhA are introduced. In addition, pleasant collaboration with Professor Arima by the author in regard to the Gamow-Teller quenching problem is also briefly mentioned.

Plant calendar pattern based on rainfall forecast and the probability of its success in Deli Serdang regency of Indonesia

NASA Astrophysics Data System (ADS)

Darnius, O.; Sitorus, S.

2018-03-01

The objective of this study was to determine the pattern of plant calendar of three types of crops; namely, palawija, rice, andbanana, based on rainfall in Deli Serdang Regency. In the first stage, we forecasted rainfall by using time series analysis, and obtained appropriate model of ARIMA (1,0,0) (1,1,1)12. Based on the forecast result, we designed a plant calendar pattern for the three types of plant. Furthermore, the probability of success in the plant types following the plant calendar pattern was calculated by using the Markov process by discretizing the continuous rainfall data into three categories; namely, Below Normal (BN), Normal (N), and Above Normal (AN) to form the probability transition matrix. Finally, the combination of rainfall forecasting models and the Markov process were used to determine the pattern of cropping calendars and the probability of success in the three crops. This research used rainfall data of Deli Serdang Regency taken from the office of BMKG (Meteorologist Climatology and Geophysics Agency), Sampali Medan, Indonesia.

Long Term Ground Based Precipitation Data Analysis in California's 7 Climate Divisions: Spatial and Temporal Variability

NASA Astrophysics Data System (ADS)

Rodriguez, L.; El-Askary, H. M.; Rakovski, C.; Allai, M.

2015-12-01

California is an area of diverse topography and has what many scientists call a Mediterranean climate. Various precipitation patterns exist due to El Niño Southern Oscillation (ENSO) which can cause abnormal precipitation or droughts. As temperature increases mainly due to the increase of CO2 in the atmosphere, it is rapidly changing the climate of not only California but the world. An increase in temperature is leading to droughts in certain areas as other areas are experiencing heavy rainfall/flooding. Droughts in return are providing a foundation for fires harming the ecosystem and nearby population. Various natural hazards can be induced due to the coupling effects from inconsistent precipitation patterns and vice versa. Using wavelets and ARIMA modeling, we were able to identify anomalies of high precipitation and droughts within California's 7 climate divisions using NOAA's hourly precipitation data from rain gauges and compared the results with modeled data, SOI, PDO, and AMO. The identification of anomalies can be used to compare and correct remote sensing measurements of precipitation and droughts.

Systematic strategies for the third industrial accident prevention plan in Korea.

PubMed

Kang, Young-sig; Yang, Sung-hwan; Kim, Tae-gu; Kim, Day-sung

2012-01-01

To minimize industrial accidents, it's critical to evaluate a firm's priorities for prevention factors and strategies since such evaluation provides decisive information for preventing industrial accidents and maintaining safety management. Therefore, this paper proposes the evaluation of priorities through statistical testing of prevention factors with a cause analysis in a cause and effect model. A priority matrix criterion is proposed to apply the ranking and for the objectivity of questionnaire results. This paper used regression method (RA), exponential smoothing method (ESM), double exponential smoothing method (DESM), autoregressive integrated moving average (ARIMA) model and proposed analytical function method (PAFM) to analyze trends of accident data that will lead to an accurate prediction. This paper standardized the questionnaire results of workers and managers in manufacturing and construction companies with less than 300 employees, located in the central Korean metropolitan areas where fatal accidents have occurred. Finally, a strategy was provided to construct safety management for the third industrial accident prevention plan and a forecasting method for occupational accident rates and fatality rates for occupational accidents per 10,000 people.

Modelling Simple Experimental Platform for In Vitro Study of Drug Elution from Drug Eluting Stents (DES)

NASA Astrophysics Data System (ADS)

Kalachev, L. V.

2016-06-01

We present a simple model of experimental setup for in vitro study of drug release from drug eluting stents and drug propagation in artificial tissue samples representing blood vessels. The model is further reduced using the assumption on vastly different characteristic diffusion times in the stent coating and in the artificial tissue. The model is used to derive a relationship between the times at which the measurements have to be taken for two experimental platforms, with corresponding artificial tissue samples made of different materials with different drug diffusion coefficients, to properly compare the drug release characteristics of drug eluting stents.

Climate variation and incidence of Ross river virus in Cairns, Australia: a time-series analysis.

PubMed Central

Tong, S; Hu, W

2001-01-01

In this study we assessed the impact of climate variability on the Ross River virus (RRv) transmission and validated an epidemic-forecasting model in Cairns, Australia. Data on the RRv cases recorded between 1985 and 1996 were obtained from the Queensland Department of Health. Climate and population data were supplied by the Australian Bureau of Meteorology and the Australian Bureau of Statistics, respectively. The cross-correlation function (CCF) showed that maximum temperature in the current month and rainfall and relative humidity at a lag of 2 months were positively and significantly associated with the monthly incidence of RRv, whereas relative humidity at a lag of 5 months was inversely associated with the RRv transmission. We developed autoregressive integrated moving average (ARIMA) models on the data collected between 1985 to 1994, and then validated the models using the data collected between 1995 and 1996. The results show that the relative humidity at a lag of 5 months (p < 0.001) and the rainfall at a lag of 2 months (p < 0.05) appeared to play significant roles in the transmission of RRv disease in Cairns. Furthermore, the regressive forecast curves were consistent with the pattern of actual values. PMID:11748035

Forecasting the mortality rates using Lee-Carter model and Heligman-Pollard model

NASA Astrophysics Data System (ADS)

Ibrahim, R. I.; Ngataman, N.; Abrisam, W. N. A. Wan Mohd

2017-09-01

Improvement in life expectancies has driven further declines in mortality. The sustained reduction in mortality rates and its systematic underestimation has been attracting the significant interest of researchers in recent years because of its potential impact on population size and structure, social security systems, and (from an actuarial perspective) the life insurance and pensions industry worldwide. Among all forecasting methods, the Lee-Carter model has been widely accepted by the actuarial community and Heligman-Pollard model has been widely used by researchers in modelling and forecasting future mortality. Therefore, this paper only focuses on Lee-Carter model and Heligman-Pollard model. The main objective of this paper is to investigate how accurately these two models will perform using Malaysian data. Since these models involves nonlinear equations that are explicitly difficult to solve, the Matrix Laboratory Version 8.0 (MATLAB 8.0) software will be used to estimate the parameters of the models. Autoregressive Integrated Moving Average (ARIMA) procedure is applied to acquire the forecasted parameters for both models as the forecasted mortality rates are obtained by using all the values of forecasted parameters. To investigate the accuracy of the estimation, the forecasted results will be compared against actual data of mortality rates. The results indicate that both models provide better results for male population. However, for the elderly female population, Heligman-Pollard model seems to underestimate to the mortality rates while Lee-Carter model seems to overestimate to the mortality rates.

STANFORD ARTIFICIAL INTELLIGENCE PROJECT.

DTIC Science & Technology

ARTIFICIAL INTELLIGENCE , GAME THEORY, DECISION MAKING, BIONICS, AUTOMATA, SPEECH RECOGNITION, GEOMETRIC FORMS, LEARNING MACHINES, MATHEMATICAL MODELS, PATTERN RECOGNITION, SERVOMECHANISMS, SIMULATION, BIBLIOGRAPHIES.

Possible Conflicts, ARRs, and Conflicts

DTIC Science & Technology

2002-05-04

Fourteenth European Conference on Artificial Intelligence Inteligencia Artificial , 41-53, (2001). (ECAI 2000), pp. 136-140, Berlin, Germany, (2000). [31] B...introduced), or proach to model-based diagnosis within the Artificial Intelligence backward (when a discrepancy is found, such as in CAEN [2, 21], community... Artificial Intelli- Relations (ARRs for short), for fault detection and localization [34]. gence community (usually known as DX). It is a research

Knowledge Based Simulation: An Artificial Intelligence Approach to System Modeling and Automating the Simulation Life Cycle.

DTIC Science & Technology

1988-04-13

Simulation: An Artificial Intelligence Approach to System Modeling and Automating the Simulation Life Cycle Mark S. Fox, Nizwer Husain, Malcolm...McRoberts and Y.V.Reddy CMU-RI-TR-88-5 Intelligent Systems Laboratory The Robotics Institute Carnegie Mellon University Pittsburgh, Pennsylvania D T T 13...years of research in the application of Artificial Intelligence to Simulation. Our focus has been in two areas: the use of Al knowledge representation

Development of a novel tissue-engineered nitinol frame artificial trachea with native-like physical characteristics.

PubMed

Sakaguchi, Yasuto; Sato, Toshihiko; Muranishi, Yusuke; Yutaka, Yojiro; Komatsu, Teruya; Omori, Koichi; Nakamura, Tatsuo; Date, Hiroshi

2018-04-24

Tracheal reconstruction is complicated by the short length to which a trachea can be resected. We previously developed a biocompatible polypropylene frame artificial trachea, but it lacked the strength and flexibility of the native trachea. In contrast, nitinol may provide these physical characteristics. We developed a novel nitinol frame artificial trachea and examined its biocompatibility and safety in canine models. We constructed several nitinol frame prototypes and selected the frame that most closely reproduced the strength of the native canine trachea. This frame was used to create a collagen-coated artificial trachea that was implanted into 5 adult beagle dogs. The artificial trachea was first implanted into the pedicled omentum and placed in the abdomen. Three weeks later, the omentum-wrapped artificial trachea was moved into the thoracic cavity. The thoracic trachea was then partially resected and reconstructed using the artificial trachea. Follow-up bronchoscopic evaluation was performed, and the artificial trachea was histologically examined after the dogs were sacrificed. Stenosis at the anastomosis sites was not observed in any dog. Survival for 18 months or longer was confirmed in all dogs but 1, which died after 9 months due to reasons unrelated to the artificial trachea. Histological examination confirmed respiratory epithelial regeneration on the artificial trachea's luminal surface. Severe foreign body reaction was not detected around the nitinol frame. The novel nitinol artificial trachea reproduced the physical characteristics of the native trachea. We have confirmed cell engraftment, good biocompatibility, and survival of 18 months or longer for this artificial trachea in canine models. Copyright © 2018 The American Association for Thoracic Surgery. Published by Elsevier Inc. All rights reserved.

Expanding the occupational health methodology: A concatenated artificial neural network approach to model the burnout process in Chinese nurses.

PubMed

Ladstätter, Felix; Garrosa, Eva; Moreno-Jiménez, Bernardo; Ponsoda, Vicente; Reales Aviles, José Manuel; Dai, Junming

2016-01-01

Artificial neural networks are sophisticated modelling and prediction tools capable of extracting complex, non-linear relationships between predictor (input) and predicted (output) variables. This study explores this capacity by modelling non-linearities in the hardiness-modulated burnout process with a neural network. Specifically, two multi-layer feed-forward artificial neural networks are concatenated in an attempt to model the composite non-linear burnout process. Sensitivity analysis, a Monte Carlo-based global simulation technique, is then utilised to examine the first-order effects of the predictor variables on the burnout sub-dimensions and consequences. Results show that (1) this concatenated artificial neural network approach is feasible to model the burnout process, (2) sensitivity analysis is a prolific method to study the relative importance of predictor variables and (3) the relationships among variables involved in the development of burnout and its consequences are to different degrees non-linear. Many relationships among variables (e.g., stressors and strains) are not linear, yet researchers use linear methods such as Pearson correlation or linear regression to analyse these relationships. Artificial neural network analysis is an innovative method to analyse non-linear relationships and in combination with sensitivity analysis superior to linear methods.

Evaluation of Physiologically-Based Artificial Neural Network Models to Detect Operator Workload in Remotely Piloted Aircraft Operations

DTIC Science & Technology

2016-07-13

to a computer via Bluetooth . Respiration is captured as a breathing waveform signal using a capacitive pressure sensor, sampled at 18 Hz. The...dropouts in the Bluetooth signal and artifacts caused by body movement. Workload models. Four artificial neural network models were created using

Antacid effects of Chinese herbal prescriptions assessed by a modified artificial stomach model

PubMed Central

Wu, Tsung-Hsiu; Chen, I-Chin; Chen, Lih-Chi

2010-01-01

AIM: To assess the antacid effects of the tonic Chinese herbal prescriptions, Si-Jun-Zi-Tang (SJZT) and Shen-Ling-Bai-Zhu-San (SLBZS). METHODS: Decoctions of the tonic Chinese herbal prescriptions, SJZT and SLBZS, were prepared according to Chinese original documents. The pH of the prescription decoctions and their neutralizing effects on artificial gastric acids were determined and compared with water and the active controls, sodium bicarbonate and colloidal aluminum phosphate. A modified model of Vatier’s artificial stomach was used to determine the duration of consistent neutralization effect on artificial gastric acids. The neutralization capacity in vitro was determined with the titration method of Fordtran’s model. RESULTS: The results showed that both SJZT and SLBZS have antacid effects in vitro. Compared with the water group, SJZT and SLBZS were found to possess significant gastric acid neutralizing effects. The duration for consistent neutralization of SLBZS was significantly longer than that of water. Also, SLBZS and SJZT exhibited significant antacid capacities compared to water. CONCLUSION: SJZT and SLBZS were consistently active in the artificial stomach model and are suggested to have antacid effects similar to the active control drugs. PMID:20845514

A Micro-Level Data-Calibrated Agent-Based Model: The Synergy between Microsimulation and Agent-Based Modeling.

PubMed

Singh, Karandeep; Ahn, Chang-Won; Paik, Euihyun; Bae, Jang Won; Lee, Chun-Hee

2018-01-01

Artificial life (ALife) examines systems related to natural life, its processes, and its evolution, using simulations with computer models, robotics, and biochemistry. In this article, we focus on the computer modeling, or "soft," aspects of ALife and prepare a framework for scientists and modelers to be able to support such experiments. The framework is designed and built to be a parallel as well as distributed agent-based modeling environment, and does not require end users to have expertise in parallel or distributed computing. Furthermore, we use this framework to implement a hybrid model using microsimulation and agent-based modeling techniques to generate an artificial society. We leverage this artificial society to simulate and analyze population dynamics using Korean population census data. The agents in this model derive their decisional behaviors from real data (microsimulation feature) and interact among themselves (agent-based modeling feature) to proceed in the simulation. The behaviors, interactions, and social scenarios of the agents are varied to perform an analysis of population dynamics. We also estimate the future cost of pension policies based on the future population structure of the artificial society. The proposed framework and model demonstrates how ALife techniques can be used by researchers in relation to social issues and policies.

Testing in artificial sweat - Is less more? Comparison of metal release in two different artificial sweat solutions.

PubMed

Midander, Klara; Julander, Anneli; Kettelarij, Jolinde; Lidén, Carola

2016-11-01

Metal release from materials immersed in artificial sweat can function as a measure of potential skin exposure. Several artificial sweat models exist that, to various degree, mimic realistic conditions. Study objective was to evaluate metal release from previously examined and well characterized materials in two different artificial sweat solutions; a comprehensive sweat model intended for use within research, based on the composition of human sweat; and the artificial sweat, EN1811, intended for testing compliance with the nickel restriction in REACH. The aim was to better understand whether there are advantages using either of the sweat solutions in bio-elution testing of materials. Metal release in two different artificial sweat solutions was compared for discs of a white gold alloy and two hard metals, and a rock drilling insert of tungsten carbide at 1 h, 24 h, 1 week and 1 month. The released amount of metal was analysed by means of inductively coupled plasma mass spectrometry. Similar levels of released metals were measured from test materials in the two different artificial sweat solutions. For purposes in relation to legislations, it was concluded that a metal release test using a simple artificial sweat composition may provide results that sufficiently indicate the degree of metal release at skin contact. Copyright © 2016 Elsevier Inc. All rights reserved.

Modelling and Predicting Backstroke Start Performance Using Non-Linear and Linear Models.

PubMed

de Jesus, Karla; Ayala, Helon V H; de Jesus, Kelly; Coelho, Leandro Dos S; Medeiros, Alexandre I A; Abraldes, José A; Vaz, Mário A P; Fernandes, Ricardo J; Vilas-Boas, João Paulo

2018-03-01

Our aim was to compare non-linear and linear mathematical model responses for backstroke start performance prediction. Ten swimmers randomly completed eight 15 m backstroke starts with feet over the wedge, four with hands on the highest horizontal and four on the vertical handgrip. Swimmers were videotaped using a dual media camera set-up, with the starts being performed over an instrumented block with four force plates. Artificial neural networks were applied to predict 5 m start time using kinematic and kinetic variables and to determine the accuracy of the mean absolute percentage error. Artificial neural networks predicted start time more robustly than the linear model with respect to changing training to the validation dataset for the vertical handgrip (3.95 ± 1.67 vs. 5.92 ± 3.27%). Artificial neural networks obtained a smaller mean absolute percentage error than the linear model in the horizontal (0.43 ± 0.19 vs. 0.98 ± 0.19%) and vertical handgrip (0.45 ± 0.19 vs. 1.38 ± 0.30%) using all input data. The best artificial neural network validation revealed a smaller mean absolute error than the linear model for the horizontal (0.007 vs. 0.04 s) and vertical handgrip (0.01 vs. 0.03 s). Artificial neural networks should be used for backstroke 5 m start time prediction due to the quite small differences among the elite level performances.

Computational properties of networks of synchronous groups of spiking neurons.

PubMed

Dayhoff, Judith E

2007-09-01

We demonstrate a model in which synchronously firing ensembles of neurons are networked to produce computational results. Each ensemble is a group of biological integrate-and-fire spiking neurons, with probabilistic interconnections between groups. An analogy is drawn in which each individual processing unit of an artificial neural network corresponds to a neuronal group in a biological model. The activation value of a unit in the artificial neural network corresponds to the fraction of active neurons, synchronously firing, in a biological neuronal group. Weights of the artificial neural network correspond to the product of the interconnection density between groups, the group size of the presynaptic group, and the postsynaptic potential heights in the synchronous group model. All three of these parameters can modulate connection strengths between neuronal groups in the synchronous group models. We give an example of nonlinear classification (XOR) and a function approximation example in which the capability of the artificial neural network can be captured by a neural network model with biological integrate-and-fire neurons configured as a network of synchronously firing ensembles of such neurons. We point out that the general function approximation capability proven for feedforward artificial neural networks appears to be approximated by networks of neuronal groups that fire in synchrony, where the groups comprise integrate-and-fire neurons. We discuss the advantages of this type of model for biological systems, its possible learning mechanisms, and the associated timing relationships.

Bayesian Model Averaging of Artificial Intelligence Models for Hydraulic Conductivity Estimation

NASA Astrophysics Data System (ADS)

Nadiri, A.; Chitsazan, N.; Tsai, F. T.; Asghari Moghaddam, A.

2012-12-01

This research presents a Bayesian artificial intelligence model averaging (BAIMA) method that incorporates multiple artificial intelligence (AI) models to estimate hydraulic conductivity and evaluate estimation uncertainties. Uncertainty in the AI model outputs stems from error in model input as well as non-uniqueness in selecting different AI methods. Using one single AI model tends to bias the estimation and underestimate uncertainty. BAIMA employs Bayesian model averaging (BMA) technique to address the issue of using one single AI model for estimation. BAIMA estimates hydraulic conductivity by averaging the outputs of AI models according to their model weights. In this study, the model weights were determined using the Bayesian information criterion (BIC) that follows the parsimony principle. BAIMA calculates the within-model variances to account for uncertainty propagation from input data to AI model output. Between-model variances are evaluated to account for uncertainty due to model non-uniqueness. We employed Takagi-Sugeno fuzzy logic (TS-FL), artificial neural network (ANN) and neurofuzzy (NF) to estimate hydraulic conductivity for the Tasuj plain aquifer, Iran. BAIMA combined three AI models and produced better fitting than individual models. While NF was expected to be the best AI model owing to its utilization of both TS-FL and ANN models, the NF model is nearly discarded by the parsimony principle. The TS-FL model and the ANN model showed equal importance although their hydraulic conductivity estimates were quite different. This resulted in significant between-model variances that are normally ignored by using one AI model.

Examining spatial-temporal variability and prediction of rainfall in North-eastern Nigeria

NASA Astrophysics Data System (ADS)

Muhammed, B. U.; Kaduk, J.; Balzter, H.

2012-12-01

In the last 50 years rainfall in North-eastern Nigeria under the influence of the West African Monsoon (WAM) has been characterised by large annual variations with severe droughts recorded in 1967-1973, and 1983-1987. This variability in rainfall has a large impact on the regions agricultural output, economy and security where the majority of the people depend on subsistence agriculture. In the 1990s there was a sign of recovery with higher annual rainfall totals compared to the 1961-1990 period but annual totals were slightly above the long term mean for the century. In this study we examine how significant this recovery is by analysing medium-term (1980-2006) rainfall of the region using the Climate Research Unit (CRU) and National Centre for Environment Prediction (NCEP) precipitation ½ degree, 6 hourly reanalysis data set. Percentage coefficient of variation increases northwards for annual rainfall (10%-35%) and the number of rainy days (10%-50%). The standardized precipitation index (SPI) of the area shows 7 years during the period as very wet (1996, 1999, 2003 and 2004) with SPI≥1.5 and moderately wet (1993, 1998, and 2006) with values of 1.0≥SPI≤1.49. Annual rainfall indicates a recovery from the 1990s and onwards but significant increases (in the amount of rainfall and number of days recorded with rainfall) is only during the peak of the monsoon season in the months of August and September (p<0.05) with no significant increases in the months following the onset of rainfall. Forecasting of monthly rainfall was made using the Auto Regressive Integrated Moving Average (ARIMA) model. The model is further evaluated using 24 months rainfall data yielding r=0.79 (regression slope=0.8; p<0.0001) in the sub-humid part of the study area and r=0.65 (regression slope=0.59, and p<0.0001) in the northern semi-arid part. The results suggest that despite the positive changes in rainfall (without significant increases in the months following the onset of the monsoon), the area has not fully recovered from the drought years of the 1960s, 70s, and 80s. These findings also highlight the implications of the current recovery on rain fed agriculture and water resources in the study area. The strong correlation and a root mean square error of 64.8 mm between the ARIMA model and the rainfall data used for this study indicates that the model can be satisfactorily used in forecasting rainfall in the in the sub-humid part of North-eastern Nigeria over a 24 months period.

Applications of artificial neural nets in clinical biomechanics.

PubMed

Schöllhorn, W I

2004-11-01

The purpose of this article is to provide an overview of current applications of artificial neural networks in the area of clinical biomechanics. The body of literature on artificial neural networks grew intractably vast during the last 15 years. Conventional statistical models may present certain limitations that can be overcome by neural networks. Artificial neural networks in general are introduced, some limitations, and some proven benefits are discussed.

Trimaran Resistance Artificial Neural Network

DTIC Science & Technology

2011-01-01

11th International Conference on Fast Sea Transportation FAST 2011, Honolulu, Hawaii, USA, September 2011 Trimaran Resistance Artificial Neural Network Richard...Trimaran Resistance Artificial Neural Network 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER 5e... Artificial Neural Network and is restricted to the center and side-hull configurations tested. The value in the parametric model is that it is able to

Wet tropospheric delays forecast based on Vienna Mapping Function time series analysis

NASA Astrophysics Data System (ADS)

Rzepecka, Zofia; Kalita, Jakub

2016-04-01

It is well known that the dry part of the zenith tropospheric delay (ZTD) is much easier to model than the wet part (ZTW). The aim of the research is applying stochastic modeling and prediction of ZTW using time series analysis tools. Application of time series analysis enables closer understanding of ZTW behavior as well as short-term prediction of future ZTW values. The ZTW data used for the studies were obtained from the GGOS service hold by Vienna technical University. The resolution of the data is six hours. ZTW for the years 2010 -2013 were adopted for the study. The International GNSS Service (IGS) permanent stations LAMA and GOPE, located in mid-latitudes, were admitted for the investigations. Initially the seasonal part was separated and modeled using periodic signals and frequency analysis. The prominent annual and semi-annual signals were removed using sines and consines functions. The autocorrelation of the resulting signal is significant for several days (20-30 samples). The residuals of this fitting were further analyzed and modeled with ARIMA processes. For both the stations optimal ARMA processes based on several criterions were obtained. On this basis predicted ZTW values were computed for one day ahead, leaving the white process residuals. Accuracy of the prediction can be estimated at about 3 cm.

Presentations to Emergency Departments for COPD: A Time Series Analysis.

PubMed

Rosychuk, Rhonda J; Youngson, Erik; Rowe, Brian H

2016-01-01

Background. Chronic obstructive pulmonary disease (COPD) is a common respiratory condition characterized by progressive dyspnea and acute exacerbations which may result in emergency department (ED) presentations. This study examines monthly rates of presentations to EDs in one Canadian province. Methods. Presentations for COPD made by individuals aged ≥55 years during April 1999 to March 2011 were extracted from provincial databases. Data included age, sex, and health zone of residence (North, Central, South, and urban). Crude rates were calculated. Seasonal autoregressive integrated moving average (SARIMA) time series models were developed. Results. ED presentations for COPD totalled 188,824 and the monthly rate of presentation remained relatively stable (from 197.7 to 232.6 per 100,000). Males and seniors (≥65 years) comprised 52.2% and 73.7% of presentations, respectively. The ARIMA(1,0, 0) × (1,0, 1)12 model was appropriate for the overall rate of presentations and for each sex and seniors. Zone specific models showed relatively stable or decreasing rates; the North zone had an increasing trend. Conclusions. ED presentation rates for COPD have been relatively stable in Alberta during the past decade. However, their increases in northern regions deserve further exploration. The SARIMA models quantified the temporal patterns and can help planning future health care service needs.

Effects of heat conduction on artificial viscosity methods for shock capturing

DOE PAGES

Cook, Andrew W.

2013-12-01

Here we investigate the efficacy of artificial thermal conductivity for shock capturing. The conductivity model is derived from artificial bulk and shear viscosities, such that stagnation enthalpy remains constant across shocks. By thus fixing the Prandtl number, more physical shock profiles are obtained, only on a larger scale. The conductivity model does not contain any empirical constants. It increases the net dissipation of a computational algorithm but is found to better preserve symmetry and produce more robust solutions for strong-shock problems.

Expertise, Task Complexity, and Artificial Intelligence: A Conceptual Framework.

ERIC Educational Resources Information Center

Buckland, Michael K.; Florian, Doris

1991-01-01

Examines the relationship between users' expertise, task complexity of information system use, and artificial intelligence to provide the basis for a conceptual framework for considering the role that artificial intelligence might play in information systems. Cognitive and conceptual models are discussed, and cost effectiveness is considered. (27…

The accuracy comparison between ARFIMA and singular spectrum analysis for forecasting the sales volume of motorcycle in Indonesia

NASA Astrophysics Data System (ADS)

Sitohang, Yosep Oktavianus; Darmawan, Gumgum

2017-08-01

This research attempts to compare between two forecasting models in time series analysis for predicting the sales volume of motorcycle in Indonesia. The first forecasting model used in this paper is Autoregressive Fractionally Integrated Moving Average (ARFIMA). ARFIMA can handle non-stationary data and has a better performance than ARIMA in forecasting accuracy on long memory data. This is because the fractional difference parameter can explain correlation structure in data that has short memory, long memory, and even both structures simultaneously. The second forecasting model is Singular spectrum analysis (SSA). The advantage of the technique is that it is able to decompose time series data into the classic components i.e. trend, cyclical, seasonal and noise components. This makes the forecasting accuracy of this technique significantly better. Furthermore, SSA is a model-free technique, so it is likely to have a very wide range in its application. Selection of the best model is based on the value of the lowest MAPE. Based on the calculation, it is obtained the best model for ARFIMA is ARFIMA (3, d = 0, 63, 0) with MAPE value of 22.95 percent. For SSA with a window length of 53 and 4 group of reconstructed data, resulting MAPE value of 13.57 percent. Based on these results it is concluded that SSA produces better forecasting accuracy.

Long-term forecasting of internet backbone traffic.

PubMed

Papagiannaki, Konstantina; Taft, Nina; Zhang, Zhi-Li; Diot, Christophe

2005-09-01

We introduce a methodology to predict when and where link additions/upgrades have to take place in an Internet protocol (IP) backbone network. Using simple network management protocol (SNMP) statistics, collected continuously since 1999, we compute aggregate demand between any two adjacent points of presence (PoPs) and look at its evolution at time scales larger than 1 h. We show that IP backbone traffic exhibits visible long term trends, strong periodicities, and variability at multiple time scales. Our methodology relies on the wavelet multiresolution analysis (MRA) and linear time series models. Using wavelet MRA, we smooth the collected measurements until we identify the overall long-term trend. The fluctuations around the obtained trend are further analyzed at multiple time scales. We show that the largest amount of variability in the original signal is due to its fluctuations at the 12-h time scale. We model inter-PoP aggregate demand as a multiple linear regression model, consisting of the two identified components. We show that this model accounts for 98% of the total energy in the original signal, while explaining 90% of its variance. Weekly approximations of those components can be accurately modeled with low-order autoregressive integrated moving average (ARIMA) models. We show that forecasting the long term trend and the fluctuations of the traffic at the 12-h time scale yields accurate estimates for at least 6 months in the future.

Development of seasonal flow outlook model for Ganges-Brahmaputra Basins in Bangladesh

NASA Astrophysics Data System (ADS)

Hossain, Sazzad; Haque Khan, Raihanul; Gautum, Dilip Kumar; Karmaker, Ripon; Hossain, Amirul

2016-10-01

Bangladesh is crisscrossed by the branches and tributaries of three main river systems, the Ganges, Bramaputra and Meghna (GBM). The temporal variation of water availability of those rivers has an impact on the different water usages such as irrigation, urban water supply, hydropower generation, navigation etc. Thus, seasonal flow outlook can play important role in various aspects of water management. The Flood Forecasting and Warning Center (FFWC) in Bangladesh provides short term and medium term flood forecast, and there is a wide demand from end-users about seasonal flow outlook for agricultural purposes. The objective of this study is to develop a seasonal flow outlook model in Bangladesh based on rainfall forecast. It uses European Centre for Medium-Range Weather Forecasts (ECMWF) seasonal precipitation, temperature forecast to simulate HYDROMAD hydrological model. Present study is limited for Ganges and Brahmaputra River Basins. ARIMA correction is applied to correct the model error. The performance of the model is evaluated using coefficient of determination (R2) and Nash-Sutcliffe Efficiency (NSE). The model result shows good performance with R2 value of 0.78 and NSE of 0.61 for the Brahmaputra River Basin, and R2 value of 0.72 and NSE of 0.59 for the Ganges River Basin for the period of May to July 2015. The result of the study indicates strong potential to make seasonal outlook to be operationalized.

Combined Use of Tissue Morphology, Neural Network Analysis of Chromatin Texture and Clinical Variables to Predict Prostate Cancer Agressiveness from Biopsy Water

DTIC Science & Technology

2000-10-01

Purpose: To combine clinical, serum, pathologic and computer derived information into an artificial neural network to develop/validate a model to...Development of an artificial neural network (year 02). Prospective validation of this model (projected year 03). All models will be tested and

Combined Use of Tissue Morphology, Neural Network Analysis of Chromatin Texture & Clinical Variables to Predict Prostate Cancer Agressiveness from Biopsy Material

DTIC Science & Technology

1999-10-01

THE PURPOSE OF THIS REPORT IS TO COMBINE CLINICAL, SERUM, PATHOLOGICAL AND COMPUTER DERIVED INFORMATION INTO AN ARTIFICIAL NEURAL NETWORK TO DEVELOP...01). Development of a artificial neural network model (year 02). Prospective validation of this model (projected year 03). All models will be tested

Log-linear model based behavior selection method for artificial fish swarm algorithm.

PubMed

Huang, Zhehuang; Chen, Yidong

2015-01-01

Artificial fish swarm algorithm (AFSA) is a population based optimization technique inspired by social behavior of fishes. In past several years, AFSA has been successfully applied in many research and application areas. The behavior of fishes has a crucial impact on the performance of AFSA, such as global exploration ability and convergence speed. How to construct and select behaviors of fishes are an important task. To solve these problems, an improved artificial fish swarm algorithm based on log-linear model is proposed and implemented in this paper. There are three main works. Firstly, we proposed a new behavior selection algorithm based on log-linear model which can enhance decision making ability of behavior selection. Secondly, adaptive movement behavior based on adaptive weight is presented, which can dynamically adjust according to the diversity of fishes. Finally, some new behaviors are defined and introduced into artificial fish swarm algorithm at the first time to improve global optimization capability. The experiments on high dimensional function optimization showed that the improved algorithm has more powerful global exploration ability and reasonable convergence speed compared with the standard artificial fish swarm algorithm.

Magnetically-actuated artificial cilia for microfluidic propulsion.

PubMed

Khaderi, S N; Craus, C B; Hussong, J; Schorr, N; Belardi, J; Westerweel, J; Prucker, O; Rühe, J; den Toonder, J M J; Onck, P R

2011-06-21

In this paper we quantitatively analyse the performance of magnetically-driven artificial cilia for lab-on-a-chip applications. The artificial cilia are fabricated using thin polymer films with embedded magnetic nano-particles and their deformation is studied under different external magnetic fields and flows. A coupled magneto-mechanical solid-fluid model that accurately captures the interaction between the magnetic field, cilia and fluid is used to simulate the cilia motion. The elastic and magnetic properties of the cilia are obtained by fitting the results of the computational model to the experimental data. The performance of the artificial cilia with a non-uniform cross-section is characterised using the numerical model for two channel configurations that are of practical importance: an open-loop and a closed-loop channel. We predict that the flow and pressure head generated by the artificial cilia can be as high as 18 microlitres per minute and 3 mm of water, respectively. We also study the effect of metachronal waves on the flow generated and show that the fluid propelled increases drastically compared to synchronously beating cilia, and is unidirectional. This increase is significant even when the phase difference between adjacent cilia is small. The obtained results provide guidelines for the optimal design of magnetically-driven artificial cilia for microfluidic propulsion.

Effects of artificial-recharge experiments at Ship Creek alluvial fan on water levels at Spring Acres Subdivision, Anchorage, Alaska

USGS Publications Warehouse

Meyer, William; Patrick, Leslie

1980-01-01

The effect of the artificial recharge experiments on water levels at Spring Acres subdivision, Anchorage, Alaska, was evaluated using two digital models constructed to simulate groundwater movement and water-level rises induced by the artificial recharge. The models predicted that the artificial recharge would have caused water levels in the aquifer immediately underlying Spring Acres subdivision to rise 0.2 foot from May 20 to August 7, 1975. The models also predicted a total rise in groundwater levels of 1.1 feet at this location from July 16, 1973 to August 7, 1975, as a result of the artificial-recharge experiments. Water-level data collected from auger holes in March 1975 by a consulting firm for the contractor indicated a depth to water of 6-7 feet below land surface at Spring Acres subdivision at this time. Water levels measured in and near Spring Acres subdivision several years before and after the 1973-75 artificial-recharge experiments showed seasonal rises of 2 to 12.4 feet. A depth to water below land surface of 2.6 feet was measured 600 feet from the subdivision in 1971 and in the subdivision in 1977. Average measured depth to water in the area was 7.0 feet from early 1976 to September 1979. (USGS)

A Symbolic Model of the Nonconscious Acquisition of Information.

ERIC Educational Resources Information Center

Ling, Charles X.; Marinov, Marin

1994-01-01

Challenges Smolensky's theory that human intuitive/nonconscious cognitive processes can only be accurately explained in terms of subsymbolic computations in artificial neural networks. Symbolic learning models of two cognitive tasks involving nonconscious acquisition of information are presented: learning production rules and artificial finite…

A Hermite-based lattice Boltzmann model with artificial viscosity for compressible viscous flows

NASA Astrophysics Data System (ADS)

Qiu, Ruofan; Chen, Rongqian; Zhu, Chenxiang; You, Yancheng

2018-05-01

A lattice Boltzmann model on Hermite basis for compressible viscous flows is presented in this paper. The model is developed in the framework of double-distribution-function approach, which has adjustable specific-heat ratio and Prandtl number. It contains a density distribution function for the flow field and a total energy distribution function for the temperature field. The equilibrium distribution function is determined by Hermite expansion, and the D3Q27 and D3Q39 three-dimensional (3D) discrete velocity models are used, in which the discrete velocity model can be replaced easily. Moreover, an artificial viscosity is introduced to enhance the model for capturing shock waves. The model is tested through several cases of compressible flows, including 3D supersonic viscous flows with boundary layer. The effect of artificial viscosity is estimated. Besides, D3Q27 and D3Q39 models are further compared in the present platform.

Application of Artificial Thunderstorm Cells for the Investigation of Lightning Initiation Problems between a Thundercloud and the Ground

NASA Astrophysics Data System (ADS)

Temnikov, A. G.; Chernensky, L. L.; Orlov, A. V.; Lysov, N. Y.; Zhuravkova, D. S.; Belova, O. S.; Gerastenok, T. K.

2017-12-01

The results of the experimental application of artificial thunderstorm cells of negative and positive polarities for the investigation of the lightning initiation problems between the thundercloud and the ground using model hydrometeor arrays are presented. Possible options of the initiation and development of a discharge between the charged cloud and the ground in the presence of model hydrometeors are established. It is experimentally shown that groups of large hydrometeors of various shapes significantly increase the probability of channel discharge initiation between the artificial thunderstorm cell and the ground, especially in the case of positive polarity of the cloud. The authors assume that large hail arrays in the thundercloud can initiate the preliminary breakdown stage in the lower part of the thundercloud or initiate and stimulate the propagation of positive lightning from its upper part. A significant effect of the shape of model hydrometeors and the way they are grouped on the processes of initiation and stimulation of the channel discharge propagation in the artificial thunderstorm cell of negative or positive polarity-ground gap is experimentally established. It is found that, in the case of negative polarity of a charged cloud, the group of conductive cylindrical hydrometeors connected by a dielectric string more effectively initiates the channel discharge between the artificial thunderstorm cell and the ground. In the case of positive polarity of the artificial thunderstorm cell, the best effect of the channel discharge initiation is achieved for model hydrometeors grouped together by the dielectric tape. The obtained results can be used in the development of the method for the directed artificial lightning initiation between the thundercloud and the ground.

Training for laparoscopic Nissen fundoplication with a newly designed model: a replacement for animal tissue models?

PubMed Central

Christie, Lorna; Goossens, Richard; Jakimowicz, Jack J.

2010-01-01

Background To bridge the early learning curve for laparoscopic Nissen fundoplication from the clinical setting to a safe environment, training models can be used. This study aimed to develop a reusable, low-cost model to be used for training in laparoscopic Nissen fundoplication procedure as an alternative to the use of animal tissue models. Methods From artificial organs and tissue, an anatomic model of the human upper abdomen was developed for training in performing laparoscopic Nissen fundoplication. The 20 participants and tutors in the European Association for Endoscopic Surgery (EAES) upper gastrointestinal surgery course completed four complementary tasks of laparoscopic Nissen fundoplication with the artificial model, then compared the realism, haptic feedback, and training properties of the model with those of animal tissue models. Results The main difference between the two training models was seen in the properties of the stomach. The wrapping of the stomach in the artificial model was rated significantly lower than that in the animal tissue model (mean, 3.6 vs. 4.2; p = 0.010). The main criticism of the stomach of the artificial model was that it was too rigid for making a proper wrap. The suturing of the stomach wall, however, was regarded as fairly realistic (mean, 3.6). The crura on the artificial model were rated better (mean, 4.3) than those on the animal tissue (mean, 4.0), although the difference was not significant. The participants regarded the model as a good to excellent (mean, 4.3) training tool. Conclusion The newly developed model is regarded as a good tool for training in laparoscopic Nissen fundoplication procedure. It is cheaper, more durable, and more readily available for training and can therefore be used in every training center. The stomach of this model, however, still needs improvement because it is too rigid for making the wrap. PMID:20526629

Applying artificial neural networks to predict communication risks in the emergency department.

PubMed

Bagnasco, Annamaria; Siri, Anna; Aleo, Giuseppe; Rocco, Gennaro; Sasso, Loredana

2015-10-01

To describe the utility of artificial neural networks in predicting communication risks. In health care, effective communication reduces the risk of error. Therefore, it is important to identify the predictive factors of effective communication. Non-technical skills are needed to achieve effective communication. This study explores how artificial neural networks can be applied to predict the risk of communication failures in emergency departments. A multicentre observational study. Data were collected between March-May 2011 by observing the communication interactions of 840 nurses with their patients during their routine activities in emergency departments. The tools used for our observation were a questionnaire to collect personal and descriptive data, level of training and experience and Guilbert's observation grid, applying the Situation-Background-Assessment-Recommendation technique to communication in emergency departments. A total of 840 observations were made on the nurses working in the emergency departments. Based on Guilbert's observation grid, the output variables is likely to influence the risk of communication failure were 'terminology'; 'listening'; 'attention' and 'clarity', whereas nurses' personal characteristics were used as input variables in the artificial neural network model. A model based on the multilayer perceptron topology was developed and trained. The receiver operator characteristic analysis confirmed that the artificial neural network model correctly predicted the performance of more than 80% of the communication failures. The application of the artificial neural network model could offer a valid tool to forecast and prevent harmful communication errors in the emergency department. © 2015 John Wiley & Sons Ltd.

Modeling the thermotaxis behavior of C.elegans based on the artificial neural network.

PubMed

Li, Mingxu; Deng, Xin; Wang, Jin; Chen, Qiaosong; Tang, Yun

2016-07-03

ASBTRACT This research aims at modeling the thermotaxis behavior of C.elegans which is a kind of nematode with full clarified neuronal connections. Firstly, this work establishes the motion model which can perform the undulatory locomotion with turning behavior. Secondly, the thermotaxis behavior is modeled by nonlinear functions and the nonlinear functions are learned by artificial neural network. Once the artificial neural networks have been well trained, they can perform the desired thermotaxis behavior. Last, several testing simulations are carried out to verify the effectiveness of the model for thermotaxis behavior. This work also analyzes the different performances of the model under different environments. The testing results reveal the essence of the thermotaxis of C.elegans to some extent, and theoretically support the research on the navigation of the crawling robots.

Daily water level forecasting using wavelet decomposition and artificial intelligence techniques

NASA Astrophysics Data System (ADS)

Seo, Youngmin; Kim, Sungwon; Kisi, Ozgur; Singh, Vijay P.

2015-01-01

Reliable water level forecasting for reservoir inflow is essential for reservoir operation. The objective of this paper is to develop and apply two hybrid models for daily water level forecasting and investigate their accuracy. These two hybrid models are wavelet-based artificial neural network (WANN) and wavelet-based adaptive neuro-fuzzy inference system (WANFIS). Wavelet decomposition is employed to decompose an input time series into approximation and detail components. The decomposed time series are used as inputs to artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) for WANN and WANFIS models, respectively. Based on statistical performance indexes, the WANN and WANFIS models are found to produce better efficiency than the ANN and ANFIS models. WANFIS7-sym10 yields the best performance among all other models. It is found that wavelet decomposition improves the accuracy of ANN and ANFIS. This study evaluates the accuracy of the WANN and WANFIS models for different mother wavelets, including Daubechies, Symmlet and Coiflet wavelets. It is found that the model performance is dependent on input sets and mother wavelets, and the wavelet decomposition using mother wavelet, db10, can further improve the efficiency of ANN and ANFIS models. Results obtained from this study indicate that the conjunction of wavelet decomposition and artificial intelligence models can be a useful tool for accurate forecasting daily water level and can yield better efficiency than the conventional forecasting models.

An algebraic interpretation of PSP composition.

PubMed

Vaucher, G

1998-01-01

The introduction of time in artificial neurons is a delicate problem on which many groups are working. Our approach combines some properties of biological models and the algebraic properties of McCulloch and Pitts artificial neuron (AN) (McCulloch and Pitts, 1943) to produce a new model which links both characteristics. In this extended artificial neuron, postsynaptic potentials (PSPs) are considered as numerical elements, having two degrees of freedom, on which the neuron computes operations. Modelled in this manner, a group of neurons can be seen as a computer with an asynchronous architecture. To formalize the functioning of this computer, we propose an algebra of impulses. This approach might also be interesting in the modelling of the passive electrical properties in some biological neurons.

Artificial Boundary Conditions for Finite Element Model Update and Damage Detection

DTIC Science & Technology

2017-03-01

BOUNDARY CONDITIONS FOR FINITE ELEMENT MODEL UPDATE AND DAMAGE DETECTION by Emmanouil Damanakis March 2017 Thesis Advisor: Joshua H. Gordis...REPORT TYPE AND DATES COVERED Master’s thesis 4. TITLE AND SUBTITLE ARTIFICIAL BOUNDARY CONDITIONS FOR FINITE ELEMENT MODEL UPDATE AND DAMAGE DETECTION...release. Distribution is unlimited. 12b. DISTRIBUTION CODE 13. ABSTRACT (maximum 200 words) In structural engineering, a finite element model is often

Modelling and Predicting Backstroke Start Performance Using Non-Linear and Linear Models

PubMed Central

de Jesus, Karla; Ayala, Helon V. H.; de Jesus, Kelly; Coelho, Leandro dos S.; Medeiros, Alexandre I.A.; Abraldes, José A.; Vaz, Mário A.P.; Fernandes, Ricardo J.; Vilas-Boas, João Paulo

2018-01-01

Abstract Our aim was to compare non-linear and linear mathematical model responses for backstroke start performance prediction. Ten swimmers randomly completed eight 15 m backstroke starts with feet over the wedge, four with hands on the highest horizontal and four on the vertical handgrip. Swimmers were videotaped using a dual media camera set-up, with the starts being performed over an instrumented block with four force plates. Artificial neural networks were applied to predict 5 m start time using kinematic and kinetic variables and to determine the accuracy of the mean absolute percentage error. Artificial neural networks predicted start time more robustly than the linear model with respect to changing training to the validation dataset for the vertical handgrip (3.95 ± 1.67 vs. 5.92 ± 3.27%). Artificial neural networks obtained a smaller mean absolute percentage error than the linear model in the horizontal (0.43 ± 0.19 vs. 0.98 ± 0.19%) and vertical handgrip (0.45 ± 0.19 vs. 1.38 ± 0.30%) using all input data. The best artificial neural network validation revealed a smaller mean absolute error than the linear model for the horizontal (0.007 vs. 0.04 s) and vertical handgrip (0.01 vs. 0.03 s). Artificial neural networks should be used for backstroke 5 m start time prediction due to the quite small differences among the elite level performances. PMID:29599857

Gender Differences in Sexual Attraction and Moral Judgment: Research With Artificial Face Models.

PubMed

González-Álvarez, Julio; Cervera-Crespo, Teresa

2018-01-01

Sexual attraction in humans is influenced by cultural or moral factors, and some gender differences can emerge in this complex interaction. A previous study found that men dissociate sexual attraction from moral judgment more than women do. Two experiments consisting of giving attractiveness ratings to photos of real opposite-sex individuals showed that men, compared to women, were significantly less influenced by the moral valence of a description about the person shown in each photo. There is evidence of some processing differences between real and artificial computer-generated faces. The present study tests the robustness of González-Álvarez's findings and extends the research to an experimental design using artificial face models as stimuli. A sample of 88 young adults (61 females and 27 males, average age 19.32, SD = 2.38) rated the attractiveness of 80 3D artificial face models generated with the FaceGen Modeller 3.5 software. Each face model was paired with a "good" and a "bad" (from a moral point of view) sentence depicting a quality or activity of the person represented in the model (e.g., she/he is an altruistic nurse in Africa vs. she/he is a prominent drug dealer). Results were in line with the previous findings and showed that, with artificial faces as well, sexual attraction is less influenced by morality in men than in women. This gender difference is consistent with an evolutionary perspective on human sexuality.

Neurolinguistically constrained simulation of sentence comprehension: integrating artificial intelligence and brain theory

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

Gigley, H.M.

1982-01-01

An artificial intelligence approach to the simulation of neurolinguistically constrained processes in sentence comprehension is developed using control strategies for simulation of cooperative computation in associative networks. The desirability of this control strategy in contrast to ATN and production system strategies is explained. A first pass implementation of HOPE, an artificial intelligence simulation model of sentence comprehension, constrained by studies of aphasic performance, psycholinguistics, neurolinguistics, and linguistic theory is described. Claims that the model could serve as a basis for sentence production simulation and for a model of language acquisition as associative learning are discussed. HOPE is a model thatmore » performs in a normal state and includes a lesion simulation facility. HOPE is also a research tool. Its modifiability and use as a tool to investigate hypothesized causes of degradation in comprehension performance by aphasic patients are described. Issues of using behavioral constraints in modelling and obtaining appropriate data for simulated process modelling are discussed. Finally, problems of validation of the simulation results are raised; and issues of how to interpret clinical results to define the evolution of the model are discussed. Conclusions with respect to the feasibility of artificial intelligence simulation process modelling are discussed based on the current state of research.« less

Electrical Stimulation of Broca's Area Enhances Implicit Learning of an Artificial Grammar

ERIC Educational Resources Information Center

de Vries, Meinou H.; Barth, Andre C. R.; Maiworm, Sandra; Knecht, Stefan; Zwitserlood, Pienie; Floel, Agnes

2010-01-01

Artificial grammar learning constitutes a well-established model for the acquisition of grammatical knowledge in a natural setting. Previous neuroimaging studies demonstrated that Broca's area (left BA 44/45) is similarly activated by natural syntactic processing and artificial grammar learning. The current study was conducted to investigate the…

Describing temporal variability of the mean Estonian precipitation series in climate time scale

NASA Astrophysics Data System (ADS)

Post, P.; Kärner, O.

2009-04-01

Applicability of the random walk type models to represent the temporal variability of various atmospheric temperature series has been successfully demonstrated recently (e.g. Kärner, 2002). Main problem in the temperature modeling is connected to the scale break in the generally self similar air temperature anomaly series (Kärner, 2005). The break separates short-range strong non-stationarity from nearly stationary longer range variability region. This is an indication of the fact that several geophysical time series show a short-range non-stationary behaviour and a stationary behaviour in longer range (Davis et al., 1996). In order to model series like that the choice of time step appears to be crucial. To characterize the long-range variability we can neglect the short-range non-stationary fluctuations, provided that we are able to model properly the long-range tendencies. The structure function (Monin and Yaglom, 1975) was used to determine an approximate segregation line between the short and the long scale in terms of modeling. The longer scale can be called climate one, because such models are applicable in scales over some decades. In order to get rid of the short-range fluctuations in daily series the variability can be examined using sufficiently long time step. In the present paper, we show that the same philosophy is useful to find a model to represent a climate-scale temporal variability of the Estonian daily mean precipitation amount series over 45 years (1961-2005). Temporal variability of the obtained daily time series is examined by means of an autoregressive and integrated moving average (ARIMA) family model of the type (0,1,1). This model is applicable for daily precipitation simulating if to select an appropriate time step that enables us to neglet the short-range non-stationary fluctuations. A considerably longer time step than one day (30 days) is used in the current paper to model the precipitation time series variability. Each ARIMA (0,1,1) model can be interpreted to be consisting of random walk in a noisy environment (Box and Jenkins, 1976). The fitted model appears to be weakly non-stationary, that gives us the possibility to use stationary approximation if only the noise component from that sum of white noise and random walk is exploited. We get a convenient routine to generate a stationary precipitation climatology with a reasonable accuracy, since the noise component variance is much larger than the dispersion of the random walk generator. This interpretation emphasizes dominating role of a random component in the precipitation series. The result is understandable due to a small territory of Estonia that is situated in the mid-latitude cyclone track. References Box, J.E.P. and G. Jenkins 1976: Time Series Analysis, Forecasting and Control (revised edn.), Holden Day San Francisco, CA, 575 pp. Davis, A., Marshak, A., Wiscombe, W. and R. Cahalan 1996: Multifractal characterizations of intermittency in nonstationary geophysical signals and fields.in G. Trevino et al. (eds) Current Topics in Nonsstationarity Analysis. World-Scientific, Singapore, 97-158. Kärner, O. 2002: On nonstationarity and antipersistency in global temperature series. J. Geophys. Res. D107; doi:10.1029/2001JD002024. Kärner, O. 2005: Some examples on negative feedback in the Earth climate system. Centr. European J. Phys. 3; 190-208. Monin, A.S. and A.M. Yaglom 1975: Statistical Fluid Mechanics, Vol 2. Mechanics of Turbulence , MIT Press Boston Mass, 886 pp.

An artificial EMG generation model based on signal-dependent noise and related application to motion classification

PubMed Central

Hayashi, Hideaki; Nakamura, Go; Chin, Takaaki; Tsuji, Toshio

2017-01-01

This paper proposes an artificial electromyogram (EMG) signal generation model based on signal-dependent noise, which has been ignored in existing methods, by introducing the stochastic construction of the EMG signals. In the proposed model, an EMG signal variance value is first generated from a probability distribution with a shape determined by a commanded muscle force and signal-dependent noise. Artificial EMG signals are then generated from the associated Gaussian distribution with a zero mean and the generated variance. This facilitates representation of artificial EMG signals with signal-dependent noise superimposed according to the muscle activation levels. The frequency characteristics of the EMG signals are also simulated via a shaping filter with parameters determined by an autoregressive model. An estimation method to determine EMG variance distribution using rectified and smoothed EMG signals, thereby allowing model parameter estimation with a small number of samples, is also incorporated in the proposed model. Moreover, the prediction of variance distribution with strong muscle contraction from EMG signals with low muscle contraction and related artificial EMG generation are also described. The results of experiments conducted, in which the reproduction capability of the proposed model was evaluated through comparison with measured EMG signals in terms of amplitude, frequency content, and EMG distribution demonstrate that the proposed model can reproduce the features of measured EMG signals. Further, utilizing the generated EMG signals as training data for a neural network resulted in the classification of upper limb motion with a higher precision than by learning from only measured EMG signals. This indicates that the proposed model is also applicable to motion classification. PMID:28640883

Artificialized land characteristics and sediment connectivity explain muddy flood hazard in Wallonia

NASA Astrophysics Data System (ADS)

de Walque, Baptiste; Bielders, Charles; Degré, Aurore; Maugnard, Alexandre

2017-04-01

Muddy flood occurrence is an off-site erosion problem of growing interest in Europe and in particular in the loess belt and Condroz regions of Wallonia (Belgium). In order to assess the probability of occurrence of muddy floods in specific places, a muddy flood hazard prediction model has been built. It was used to test 11 different explanatory variables in simple and multiple logistic regressions approaches. A database of 442 muddy flood-affected sites and an equal number of homologous non flooded sites was used. For each site, relief, land use, sediment production and sediment connectivity of the contributing area were extracted. To assess the prediction quality of the model, we proceeded to a validation using 48 new pairs of homologous sites. Based on Akaïke Information Criterion (AIC), we determined that the best muddy flood hazard assessment model requires a total of 6 explanatory variable as inputs: the spatial aggregation of the artificialized land, the sediment connectivity, the artificialized land proximity to the outlet, the proportion of artificialized land, the mean slope and the Gravelius index of compactness of the contributive area. The artificialized land properties listed above showed to improve substantially the model quality (p-values from 10e-10 to 10e-4). All of the 3 properties showed negative correlation with the muddy flood hazard. These results highlight the importance of considering the artificialized land characteristics in the sediment transport assessment models. Indeed, artificialized land such as roads may dramatically deviate flows and influence the connectivity in the landscape. Besides the artificialized land properties, the sediment connectivity showed significant explanatory power (p-value of 10e-11). A positive correlation between the sediment connectivity and the muddy flood hazard was found, ranging from 0.3 to 0.45 depending on the sediment connectivity index. Several studies already have highlighted the importance of this parameter in the sediment transport characterization in the landscape. Using the best muddy flood probability of occurrence threshold value of 0.49, the validation of the best multiple logistic regression resulted in a prediction quality of 75.6% (original dataset) and 81.2% (secondary dataset). The developed statistical model could be used as a reliable tool to target muddy floods mitigation measures in sites resulting with the highest muddy floods hazard.

[An experimental study on the therapeutic effects of eustachian tube surfactant in barotitis media].

PubMed

Feng, Lining; Chen, Wenxian; Cong, Rui; Zheng, Guoxi; Gou, Lin; Guo, Qun

2002-11-01

To observe the effect of surfactant on eustachian tube (ET) on the opening of ET as well as it's therapeutic role in barotitis media (BM). 50 guinea pigs were successfully established as BM models by stimulated ascending in altitude chamber. Parts of the models were treated with by middle ear flushing with nature ETS, artificial ETS, artificial phospholipid and saline, after which the eustachian tube pressure opening level (POL) of each group was tested. Others were injected with 1 ml artificial ETS in on side of the middle ear, and 1 ml of saline in the other served as control. Natural ETS decreased the POL from 11.98 to 6.11 kPa (P < 0.01); Artificial ETS reduced the POL from 11.91 to 6.67 kPa (P < 0.01), there were no significant differences between the two groups. Artificial phospholipid decreased the POL from 11.86 to 8.61 kPa (P < 0.05), which was not as effective as natural ETS. While the POL of saline group remained unchanged. After one week of artificial ETS treatment, the congestion in drum membrane alleviated, the hearing threshold of ETS group improved and the effusion in tympanic cavity lessened. The results suggest that artificial ETS is as effective as nature ETS to facilitates the opening of eustachian tube. Artificial ETS may exert therapeutic effects on BM.

Introducing Artificial Neural Networks through a Spreadsheet Model

ERIC Educational Resources Information Center

Rienzo, Thomas F.; Athappilly, Kuriakose K.

2012-01-01

Business students taking data mining classes are often introduced to artificial neural networks (ANN) through point and click navigation exercises in application software. Even if correct outcomes are obtained, students frequently do not obtain a thorough understanding of ANN processes. This spreadsheet model was created to illuminate the roles of…

Artificial Intelligence and the High School Computer Curriculum.

ERIC Educational Resources Information Center

Dillon, Richard W.

1993-01-01

Describes a four-part curriculum that can serve as a model for incorporating artificial intelligence (AI) into the high school computer curriculum. The model includes examining questions fundamental to AI, creating and designing an expert system, language processing, and creating programs that integrate machine vision with robotics and…

Psychometric Measurement Models and Artificial Neural Networks

ERIC Educational Resources Information Center

Sese, Albert; Palmer, Alfonso L.; Montano, Juan J.

2004-01-01

The study of measurement models in psychometrics by means of dimensionality reduction techniques such as Principal Components Analysis (PCA) is a very common practice. In recent times, an upsurge of interest in the study of artificial neural networks apt to computing a principal component extraction has been observed. Despite this interest, the…

Using chaotic artificial neural networks to model memory in the brain

NASA Astrophysics Data System (ADS)

Aram, Zainab; Jafari, Sajad; Ma, Jun; Sprott, Julien C.; Zendehrouh, Sareh; Pham, Viet-Thanh

2017-03-01

In the current study, a novel model for human memory is proposed based on the chaotic dynamics of artificial neural networks. This new model explains a biological fact about memory which is not yet explained by any other model: There are theories that the brain normally works in a chaotic mode, while during attention it shows ordered behavior. This model uses the periodic windows observed in a previously proposed model for the brain to store and then recollect the information.

Predicting the number of emergency department presentations in Western Australia: a population-based time series analysis.

PubMed

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

2015-02-01

To predict the number of ED presentations in Western Australia (WA) in the next 5 years, stratified by place of treatment, age, triage and disposition. We conducted a population-based time series analysis of 7 year monthly WA statewide ED presentation data from the financial years 2006/07 to 2012/13 using univariate autoregressive integrated moving average (ARIMA) and multivariate vector-ARIMA techniques. ED presentations in WA were predicted to increase from 990,342 in 2012/13 to 1,250,991 (95% CI: 982,265-1,519,718) in 2017/18, an increase of 260,649 (or 26.3%). The majority of this increase would occur in metropolitan WA (84.2%). The compound annual growth rate (CAGR) in metropolitan WA in the next 5 years was predicted to be 6.5% compared with 2.0% in the non-metropolitan area. The greatest growth in metropolitan WA would be in ages 65 and over (CAGR, 6.9%), triage categories 2 and 3 (8.3% and 7.7%, respectively) and admitted (9.8%) cohorts. The only predicted decrease was triage category 5 (-5.3%). ED demand in WA will exceed population growth. The highest growth will be in patients with complex care needs. An integrated system-wide strategy is urgently required to ensure access, quality and sustainability of the health system. © 2015 Australasian College for Emergency Medicine and Australasian Society for Emergency Medicine.

Effects of Menthol-Containing Artificial Tears on Tear Stimulation and Ocular Surface Integrity in Normal and Dry Eye Rat Models.

PubMed

Ahn, Somin; Eom, Youngsub; Kang, Boram; Park, Jungboung; Lee, Hyung Keun; Kim, Hyo Myung; Song, Jong Suk

2018-05-01

To evaluate the effects of menthol-containing artificial tears on tear stimulation and ocular surface integrity in normal and dry eye rat models. A total of 54 male Lewis rats were used. The levels of tear secretion and tear MUC5AC concentrations were compared between the menthol-containing artificial tear-treated group (menthol group) and the vehicle-treated group (vehicle group). The groups were compared after a single instillation to evaluate the immediate effects, and after repeated instillation (five times a day for 5 days) to evaluate the longer-term effects. Tear lactate dehydrogenase (LDH) activity was measured to evaluate eye drop instillation-induced ocular surface damage. The effects of menthol-containing artificial tears were also evaluated in a dry eye rat model. After a single instillation of menthol-containing artificial tears, tear secretion increased from 4.37 (±0.75) mm at baseline to 7.37 (±1.60) mm. However, after repeated instillations, the effects of tear stimulation decreased. The tear MUC5AC concentration was significantly lower in the menthol group than in the vehicle group after a single instillation, but not after repeated instillation. However, the tear LDH concentration was significantly increased in the menthol group after repeated instillation. In the dry eye rat model, the extent of menthol-induced tear stimulation was reduced. Menthol-containing artificial tears increased tear secretion, but lowered the tear MUC5AC concentration. Menthol-induced tear stimulation was reduced after repeated instillation for 5 days and in the dry eye rat model. Conversely, repeated instillation of menthol-induced ocular surface damage, resulting in increased tear LDH activity.

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.

Analysis of forecasting malaria case with climatic factors as predictor in Mandailing Natal Regency: a time series study

NASA Astrophysics Data System (ADS)

Aulia, D.; Ayu, S. F.; Matondang, A.

2018-01-01

Malaria is the most contagious global concern. As a public health problem with outbreaks, affect the quality of life and economy, also could lead to death. Therefore, this research is to forecast malaria cases with climatic factors as predictors in Mandailing Natal Regency. The total number of positive malaria cases on January 2008 to December 2016 were taken from health department of Mandailing Natal Regency. Climates data such as rainfall, humidity, and temperature were taken from Center of Statistic Department of Mandailing Natal Regency. E-views ver. 9 is used to analyze this study. Autoregressive integrated average, ARIMA (0,1,1) (1,0,0)12 is the best model to explain the 67,2% variability data in time series study. Rainfall (P value = 0.0005), temperature (P value = 0,0029) and humidity (P value = 0.0001) are significant predictors for malaria transmission. Seasonal adjusted factor (SAF) in November and March shows peak for malaria cases.

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.

Methodologic Guide for Evaluating Clinical Performance and Effect of Artificial Intelligence Technology for Medical Diagnosis and Prediction.

PubMed

Park, Seong Ho; Han, Kyunghwa

2018-03-01

The use of artificial intelligence in medicine is currently an issue of great interest, especially with regard to the diagnostic or predictive analysis of medical images. Adoption of an artificial intelligence tool in clinical practice requires careful confirmation of its clinical utility. Herein, the authors explain key methodology points involved in a clinical evaluation of artificial intelligence technology for use in medicine, especially high-dimensional or overparameterized diagnostic or predictive models in which artificial deep neural networks are used, mainly from the standpoints of clinical epidemiology and biostatistics. First, statistical methods for assessing the discrimination and calibration performances of a diagnostic or predictive model are summarized. Next, the effects of disease manifestation spectrum and disease prevalence on the performance results are explained, followed by a discussion of the difference between evaluating the performance with use of internal and external datasets, the importance of using an adequate external dataset obtained from a well-defined clinical cohort to avoid overestimating the clinical performance as a result of overfitting in high-dimensional or overparameterized classification model and spectrum bias, and the essentials for achieving a more robust clinical evaluation. Finally, the authors review the role of clinical trials and observational outcome studies for ultimate clinical verification of diagnostic or predictive artificial intelligence tools through patient outcomes, beyond performance metrics, and how to design such studies. © RSNA, 2018.

Deep Neural Networks: A New Framework for Modeling Biological Vision and Brain Information Processing.

PubMed

Kriegeskorte, Nikolaus

2015-11-24

Recent advances in neural network modeling have enabled major strides in computer vision and other artificial intelligence applications. Human-level visual recognition abilities are coming within reach of artificial systems. Artificial neural networks are inspired by the brain, and their computations could be implemented in biological neurons. Convolutional feedforward networks, which now dominate computer vision, take further inspiration from the architecture of the primate visual hierarchy. However, the current models are designed with engineering goals, not to model brain computations. Nevertheless, initial studies comparing internal representations between these models and primate brains find surprisingly similar representational spaces. With human-level performance no longer out of reach, we are entering an exciting new era, in which we will be able to build biologically faithful feedforward and recurrent computational models of how biological brains perform high-level feats of intelligence, including vision.

Artificial intelligence may help in predicting the need for additional surgery after endoscopic resection of T1 colorectal cancer.

PubMed

Ichimasa, Katsuro; Kudo, Shin-Ei; Mori, Yuichi; Misawa, Masashi; Matsudaira, Shingo; Kouyama, Yuta; Baba, Toshiyuki; Hidaka, Eiji; Wakamura, Kunihiko; Hayashi, Takemasa; Kudo, Toyoki; Ishigaki, Tomoyuki; Yagawa, Yusuke; Nakamura, Hiroki; Takeda, Kenichi; Haji, Amyn; Hamatani, Shigeharu; Mori, Kensaku; Ishida, Fumio; Miyachi, Hideyuki

2018-03-01

 Decisions concerning additional surgery after endoscopic resection of T1 colorectal cancer (CRC) are difficult because preoperative prediction of lymph node metastasis (LNM) is problematic. We investigated whether artificial intelligence can predict LNM presence, thus minimizing the need for additional surgery.  Data on 690 consecutive patients with T1 CRCs that were surgically resected in 2001 - 2016 were retrospectively analyzed. We divided patients into two groups according to date: data from 590 patients were used for machine learning for the artificial intelligence model, and the remaining 100 patients were included for model validation. The artificial intelligence model analyzed 45 clinicopathological factors and then predicted positivity or negativity for LNM. Operative specimens were used as the gold standard for the presence of LNM. The artificial intelligence model was validated by calculating the sensitivity, specificity, and accuracy for predicting LNM, and comparing these data with those of the American, European, and Japanese guidelines.  Sensitivity was 100 % (95 % confidence interval [CI] 72 % to 100 %) in all models. Specificity of the artificial intelligence model and the American, European, and Japanese guidelines was 66 % (95 %CI 56 % to 76 %), 44 % (95 %CI 34 % to 55 %), 0 % (95 %CI 0 % to 3 %), and 0 % (95 %CI 0 % to 3 %), respectively; and accuracy was 69 % (95 %CI 59 % to 78 %), 49 % (95 %CI 39 % to 59 %), 9 % (95 %CI 4 % to 16 %), and 9 % (95 %CI 4 % - 16 %), respectively. The rates of unnecessary additional surgery attributable to misdiagnosing LNM-negative patients as having LNM were: 77 % (95 %CI 62 % to 89 %) for the artificial intelligence model, and 85 % (95 %CI 73 % to 93 %; P  < 0.001), 91 % (95 %CI 84 % to 96 %; P  < 0.001), and 91 % (95 %CI 84 % to 96 %; P  < 0.001) for the American, European, and Japanese guidelines, respectively.  Compared with current guidelines, artificial intelligence significantly reduced unnecessary additional surgery after endoscopic resection of T1 CRC without missing LNM positivity. © Georg Thieme Verlag KG Stuttgart · New York.

Modeling and Evaluating Emotions Impact on Cognition

DTIC Science & Technology

2013-07-01

Causality and Responsibility Judgment in Multi-Agent Interactions: Extended abstract. 23rd International Joint Conference on Artificial Inteligence ...responsibility judgment in multi-agent interactions." Journal of Artificial Intelligence Research v44(1), 223- 273. • Morteza Dehghani, Jonathan Gratch... Artificial Intelligence (AAAI’11). Grant related invited talks: • Keynote speaker, Workshop on Empathic and Emotional Agents at the International

Case-Based Planning: An Integrated Theory of Planning, Learning and Memory

DTIC Science & Technology

1986-10-01

rtvoeoo oldo II nocomtmry and Idonltly by block numbor) planning Case-based reasoning learning Artificial Intelligence 20. ABSTRACT (Conllnum...Computational Model of Analogical Prob- lem Solving, Proceedings of the Seventh International Joint Conference on Artificial Intelligence ...Understanding and Generalizing Plans., Proceedings of the Eight Interna- tional Joint Conference on Artificial Intelligence , IJCAI, Karlsrhue, Germany

Cultural Modelling: Literature review

DTIC Science & Technology

2006-09-01

of mood and/or emotions. Our review did show some evidence that artificial intelligence research has tended to depict human decision making as...pp. 72-79). The Society for the Study of Artificial Intelligence and the Simulation of Behaviour (AISB). Halfill, T., Sundstrom, E., Nielsen, T. M...M. & Thagard, P. (2005). Changing personalities: Towards realistic virtual characters. Journal of Experimental & Theoretical Artificial Intelligence

Organization-based Model-driven Development of High-assurance Multiagent Systems

DTIC Science & Technology

2009-02-27

based Model -driven Development of High-assurance Multiagent Systems " performed by Dr. Scott A . DeLoach and Dr Robby at Kansas State University... A Capabilities Based Model for Artificial Organizations. Journal of Autonomous Agents and Multiagent Systems . Volume 16, no. 1, February 2008, pp...Matson, E . T. (2007). A capabilities based theory of artificial organizations. Journal of Autonomous Agents and Multiagent Systems

A CFBPN Artificial Neural Network Model for Educational Qualitative Data Analyses: Example of Students' Attitudes Based on Kellerts' Typologies

ERIC Educational Resources Information Center

Yorek, Nurettin; Ugulu, Ilker

2015-01-01

In this study, artificial neural networks are suggested as a model that can be "trained" to yield qualitative results out of a huge amount of categorical data. It can be said that this is a new approach applied in educational qualitative data analysis. In this direction, a cascade-forward back-propagation neural network (CFBPN) model was…

Building a Decision Support System for Inpatient Admission Prediction With the Manchester Triage System and Administrative Check-in Variables.

PubMed

Zlotnik, Alexander; Alfaro, Miguel Cuchí; Pérez, María Carmen Pérez; Gallardo-Antolín, Ascensión; Martínez, Juan Manuel Montero

2016-05-01

The usage of decision support tools in emergency departments, based on predictive models, capable of estimating the probability of admission for patients in the emergency department may give nursing staff the possibility of allocating resources in advance. We present a methodology for developing and building one such system for a large specialized care hospital using a logistic regression and an artificial neural network model using nine routinely collected variables available right at the end of the triage process.A database of 255.668 triaged nonobstetric emergency department presentations from the Ramon y Cajal University Hospital of Madrid, from January 2011 to December 2012, was used to develop and test the models, with 66% of the data used for derivation and 34% for validation, with an ordered nonrandom partition. On the validation dataset areas under the receiver operating characteristic curve were 0.8568 (95% confidence interval, 0.8508-0.8583) for the logistic regression model and 0.8575 (95% confidence interval, 0.8540-0. 8610) for the artificial neural network model. χ Values for Hosmer-Lemeshow fixed "deciles of risk" were 65.32 for the logistic regression model and 17.28 for the artificial neural network model. A nomogram was generated upon the logistic regression model and an automated software decision support system with a Web interface was built based on the artificial neural network model.

Log-Linear Model Based Behavior Selection Method for Artificial Fish Swarm Algorithm

PubMed Central

Huang, Zhehuang; Chen, Yidong

2015-01-01

Artificial fish swarm algorithm (AFSA) is a population based optimization technique inspired by social behavior of fishes. In past several years, AFSA has been successfully applied in many research and application areas. The behavior of fishes has a crucial impact on the performance of AFSA, such as global exploration ability and convergence speed. How to construct and select behaviors of fishes are an important task. To solve these problems, an improved artificial fish swarm algorithm based on log-linear model is proposed and implemented in this paper. There are three main works. Firstly, we proposed a new behavior selection algorithm based on log-linear model which can enhance decision making ability of behavior selection. Secondly, adaptive movement behavior based on adaptive weight is presented, which can dynamically adjust according to the diversity of fishes. Finally, some new behaviors are defined and introduced into artificial fish swarm algorithm at the first time to improve global optimization capability. The experiments on high dimensional function optimization showed that the improved algorithm has more powerful global exploration ability and reasonable convergence speed compared with the standard artificial fish swarm algorithm. PMID:25691895

A self-taught artificial agent for multi-physics computational model personalization.

PubMed

Neumann, Dominik; Mansi, Tommaso; Itu, Lucian; Georgescu, Bogdan; Kayvanpour, Elham; Sedaghat-Hamedani, Farbod; Amr, Ali; Haas, Jan; Katus, Hugo; Meder, Benjamin; Steidl, Stefan; Hornegger, Joachim; Comaniciu, Dorin

2016-12-01

Personalization is the process of fitting a model to patient data, a critical step towards application of multi-physics computational models in clinical practice. Designing robust personalization algorithms is often a tedious, time-consuming, model- and data-specific process. We propose to use artificial intelligence concepts to learn this task, inspired by how human experts manually perform it. The problem is reformulated in terms of reinforcement learning. In an off-line phase, Vito, our self-taught artificial agent, learns a representative decision process model through exploration of the computational model: it learns how the model behaves under change of parameters. The agent then automatically learns an optimal strategy for on-line personalization. The algorithm is model-independent; applying it to a new model requires only adjusting few hyper-parameters of the agent and defining the observations to match. The full knowledge of the model itself is not required. Vito was tested in a synthetic scenario, showing that it could learn how to optimize cost functions generically. Then Vito was applied to the inverse problem of cardiac electrophysiology and the personalization of a whole-body circulation model. The obtained results suggested that Vito could achieve equivalent, if not better goodness of fit than standard methods, while being more robust (up to 11% higher success rates) and with faster (up to seven times) convergence rate. Our artificial intelligence approach could thus make personalization algorithms generalizable and self-adaptable to any patient and any model. Copyright © 2016. Published by Elsevier B.V.

Optimization of an artificial-recharge-pumping system for water supply in the Maghaway Valley, Cebu, Philippines

NASA Astrophysics Data System (ADS)

Kawo, Nafyad Serre; Zhou, Yangxiao; Magalso, Ronnell; Salvacion, Lasaro

2018-05-01

A coupled simulation-optimization approach to optimize an artificial-recharge-pumping system for the water supply in the Maghaway Valley, Cebu, Philippines, is presented. The objective is to maximize the total pumping rate through a system of artificial recharge and pumping while meeting constraints such as groundwater-level drawdown and bounds on pumping rates at each well. The simulation models were coupled with groundwater management optimization to maximize production rates. Under steady-state natural conditions, the significant inflow to the aquifer comes from river leakage, whereas the natural discharge is mainly the subsurface outflow to the downstream area. Results from the steady artificial-recharge-pumping simulation model show that artificial recharge is about 20,587 m3/day and accounts for 77% of total inflow. Under transient artificial-recharge-pumping conditions, artificial recharge varies between 14,000 and 20,000 m3/day depending on the wet and dry seasons, respectively. The steady-state optimisation results show that the total optimal abstraction rate is 37,545 m3/day and artificial recharge is increased to 29,313 m3/day. The transient optimization results show that the average total optimal pumping rate is 36,969 m3/day for the current weir height. The transient optimization results for an increase in weir height by 1 and 2 m show that the average total optimal pumping rates are increased to 38,768 and 40,463 m3/day, respectively. It is concluded that the increase in the height of the weir can significantly increase the artificial recharge rate and production rate in Maghaway Valley.

Application of artificial neural networks in hydrological modeling: A case study of runoff simulation of a Himalayan glacier basin

NASA Technical Reports Server (NTRS)

Buch, A. M.; Narain, A.; Pandey, P. C.

1994-01-01

The simulation of runoff from a Himalayan Glacier basin using an Artificial Neural Network (ANN) is presented. The performance of the ANN model is found to be superior to the Energy Balance Model and the Multiple Regression model. The RMS Error is used as the figure of merit for judging the performance of the three models, and the RMS Error for the ANN model is the latest of the three models. The ANN is faster in learning and exhibits excellent system generalization characteristics.

A comparative study between nonlinear regression and artificial neural network approaches for modelling wild oat (Avena fatua) field emergence

USDA-ARS?s Scientific Manuscript database

Non-linear regression techniques are used widely to fit weed field emergence patterns to soil microclimatic indices using S-type functions. Artificial neural networks present interesting and alternative features for such modeling purposes. In this work, a univariate hydrothermal-time based Weibull m...

Numerical simulation of artificial hip joint motion based on human age factor

NASA Astrophysics Data System (ADS)

Ramdhani, Safarudin; Saputra, Eko; Jamari, J.

2018-05-01

Artificial hip joint is a prosthesis (synthetic body part) which usually consists of two or more components. Replacement of the hip joint due to the occurrence of arthritis, ordinarily patients aged or older. Numerical simulation models are used to observe the range of motion in the artificial hip joint, the range of motion of joints used as the basis of human age. Finite- element analysis (FEA) is used to calculate stress von mises in motion and observes a probability of prosthetic impingement. FEA uses a three-dimensional nonlinear model and considers the position variation of acetabular liner cups. The result of numerical simulation shows that FEA method can be used to analyze the performance calculation of the artificial hip joint at this time more accurate than conventional method.

Collapse susceptibility mapping in karstified gypsum terrain (Sivas basin - Turkey) by conditional probability, logistic regression, artificial neural network models

NASA Astrophysics Data System (ADS)

Yilmaz, Isik; Keskin, Inan; Marschalko, Marian; Bednarik, Martin

2010-05-01

This study compares the GIS based collapse susceptibility mapping methods such as; conditional probability (CP), logistic regression (LR) and artificial neural networks (ANN) applied in gypsum rock masses in Sivas basin (Turkey). Digital Elevation Model (DEM) was first constructed using GIS software. Collapse-related factors, directly or indirectly related to the causes of collapse occurrence, such as distance from faults, slope angle and aspect, topographical elevation, distance from drainage, topographic wetness index- TWI, stream power index- SPI, Normalized Difference Vegetation Index (NDVI) by means of vegetation cover, distance from roads and settlements were used in the collapse susceptibility analyses. In the last stage of the analyses, collapse susceptibility maps were produced from CP, LR and ANN models, and they were then compared by means of their validations. Area Under Curve (AUC) values obtained from all three methodologies showed that the map obtained from ANN model looks like more accurate than the other models, and the results also showed that the artificial neural networks is a usefull tool in preparation of collapse susceptibility map and highly compatible with GIS operating features. Key words: Collapse; doline; susceptibility map; gypsum; GIS; conditional probability; logistic regression; artificial neural networks.

Artificial neural network modelling of a large-scale wastewater treatment plant operation.

PubMed

Güçlü, Dünyamin; Dursun, Sükrü

2010-11-01

Artificial Neural Networks (ANNs), a method of artificial intelligence method, provide effective predictive models for complex processes. Three independent ANN models trained with back-propagation algorithm were developed to predict effluent chemical oxygen demand (COD), suspended solids (SS) and aeration tank mixed liquor suspended solids (MLSS) concentrations of the Ankara central wastewater treatment plant. The appropriate architecture of ANN models was determined through several steps of training and testing of the models. ANN models yielded satisfactory predictions. Results of the root mean square error, mean absolute error and mean absolute percentage error were 3.23, 2.41 mg/L and 5.03% for COD; 1.59, 1.21 mg/L and 17.10% for SS; 52.51, 44.91 mg/L and 3.77% for MLSS, respectively, indicating that the developed model could be efficiently used. The results overall also confirm that ANN modelling approach may have a great implementation potential for simulation, precise performance prediction and process control of wastewater treatment plants.

Modeling rainfall-runoff process using soft computing techniques

NASA Astrophysics Data System (ADS)

Kisi, Ozgur; Shiri, Jalal; Tombul, Mustafa

2013-02-01

Rainfall-runoff process was modeled for a small catchment in Turkey, using 4 years (1987-1991) of measurements of independent variables of rainfall and runoff values. The models used in the study were Artificial Neural Networks (ANNs), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Gene Expression Programming (GEP) which are Artificial Intelligence (AI) approaches. The applied models were trained and tested using various combinations of the independent variables. The goodness of fit for the model was evaluated in terms of the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), coefficient of efficiency (CE) and scatter index (SI). A comparison was also made between these models and traditional Multi Linear Regression (MLR) model. The study provides evidence that GEP (with RMSE=17.82 l/s, MAE=6.61 l/s, CE=0.72 and R2=0.978) is capable of modeling rainfall-runoff process and is a viable alternative to other applied artificial intelligence and MLR time-series methods.

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.

Forecasting the number of zoonotic cutaneous leishmaniasis cases in south of Fars province, Iran using seasonal ARIMA time series method.

PubMed

Sharafi, Mehdi; Ghaem, Haleh; Tabatabaee, Hamid Reza; Faramarzi, Hossein

2017-01-01

To predict the trend of cutaneous leishmaniasis and assess the relationship between the disease trend and weather variables in south of Fars province using Seasonal Autoregressive Integrated Moving Average (SARIMA) model. The trend of cutaneous leishmaniasis was predicted using Mini tab software and SARIMA model. Besides, information about the disease and weather conditions was collected monthly based on time series design during January 2010 to March 2016. Moreover, various SARIMA models were assessed and the best one was selected. Then, the model's fitness was evaluated based on normality of the residuals' distribution, correspondence between the fitted and real amounts, and calculation of Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC). The study results indicated that SARIMA model (4,1,4)(0,1,0) (12) in general and SARIMA model (4,1,4)(0,1,1) (12) in below and above 15 years age groups could appropriately predict the disease trend in the study area. Moreover, temperature with a three-month delay (lag3) increased the disease trend, rainfall with a four-month delay (lag4) decreased the disease trend, and rainfall with a nine-month delay (lag9) increased the disease trend. Based on the results, leishmaniasis follows a descending trend in the study area in case drought condition continues, SARIMA models can suitably measure the disease trend, and the disease follows a seasonal trend. Copyright © 2017 Hainan Medical University. Production and hosting by Elsevier B.V. All rights reserved.

Artificial neural network cardiopulmonary modeling and diagnosis

DOEpatents

Kangas, L.J.; Keller, P.E.

1997-10-28

The present invention is a method of diagnosing a cardiopulmonary condition in an individual by comparing data from a progressive multi-stage test for the individual to a non-linear multi-variate model, preferably a recurrent artificial neural network having sensor fusion. The present invention relies on a cardiovascular model developed from physiological measurements of an individual. Any differences between the modeled parameters and the parameters of an individual at a given time are used for diagnosis. 12 figs.

Artificial neural network cardiopulmonary modeling and diagnosis

DOEpatents

Kangas, Lars J.; Keller, Paul E.

1997-01-01

The present invention is a method of diagnosing a cardiopulmonary condition in an individual by comparing data from a progressive multi-stage test for the individual to a non-linear multi-variate model, preferably a recurrent artificial neural network having sensor fusion. The present invention relies on a cardiovascular model developed from physiological measurements of an individual. Any differences between the modeled parameters and the parameters of an individual at a given time are used for diagnosis.

Artificial organisms as tools for the development of psychological theory: Tolman's lesson.

PubMed

Miglino, Orazio; Gigliotta, Onofrio; Cardaci, Maurizio; Ponticorvo, Michela

2007-12-01

In the 1930s and 1940s, Edward Tolman developed a psychological theory of spatial orientation in rats and humans. He expressed his theory as an automaton (the "schematic sowbug") or what today we would call an "artificial organism." With the technology of the day, he could not implement his model. Nonetheless, he used it to develop empirical predictions which tested with animals in the laboratory. This way of proceeding was in line with scientific practice dating back to Galileo. The way psychologists use artificial organisms in their work today breaks with this tradition. Modern "artificial organisms" are constructed a posteriori, working from experimental or ethological observations. As a result, researchers can use them to confirm a theoretical model or to simulate its operation. But they make no contribution to the actual building of models. In this paper, we try to return to Tolman's original strategy: implementing his theory of "vicarious trial and error" in a simulated robot, forecasting the robot's behavior and conducting experiments that verify or falsify these predictions.

Modeling of dielectric elastomer oscillators for soft biomimetic applications.

PubMed

Henke, E-F M; Wilson, Katherine E; Anderson, I A

2018-06-26

Biomimetic, entirely soft robots with animal-like behavior and integrated artificial nervous systems will open up totally new perspectives and applications. However, until now, most presented studies on soft robots were limited to only partly soft designs, since all solutions at least needed conventional, stiff electronics to sense, process signals and activate actuators. We present a novel approach for a set up and the experimental validation of an artificial pace maker that is able to drive basic robotic structures and act as artificial central pattern generator. The structure is based on multi-functional dielectric elastomers (DEs). DE actuators, DE switches and DE resistors are combined to create complex DE oscillators (DEOs). Supplied with only one external DC voltage, the DEO autonomously generates oscillating signals that can be used to clock a robotic structure, control the cyclic motion of artificial muscles in bionic robots or make a whole robotic structure move. We present the basic functionality, derive a mathematical model for predicting the generated signal waveform and verify the model experimentally.

Permeability study of cancellous bone and its idealised structures.

PubMed

Syahrom, Ardiyansyah; Abdul Kadir, Mohammed Rafiq; Harun, Muhamad Nor; Öchsner, Andreas

2015-01-01

Artificial bone is a suitable alternative to autografts and allografts, however their use is still limited. Though there were numerous reports on their structural properties, permeability studies of artificial bones were comparably scarce. This study focused on the development of idealised, structured models of artificial cancellous bone and compared their permeability values with bone surface area and porosity. Cancellous bones from fresh bovine femur were extracted and cleaned following an established protocol. The samples were scanned using micro-computed tomography (μCT) and three-dimensional models of the cancellous bones were reconstructed for morphology study. Seven idealised and structured cancellous bone models were then developed and fabricated via rapid prototyping technique. A test-rig was developed and permeability tests were performed on the artificial and real cancellous bones. The results showed a linear correlation between the permeability and the porosity as well as the bone surface area. The plate-like idealised structure showed a similar value of permeability to the real cancellous bones. Copyright © 2014 IPEM. Published by Elsevier Ltd. All rights reserved.

Could Intelligent Tutors Anticipate Successfully User Reactions?

NASA Astrophysics Data System (ADS)

Kalisz, Eugenia; Florea, Adina Magda

2006-06-01

Emotions have been shown to have an important impact on several human processes such as decision-making, planning, cognition, and learning. In an e-learning system, an artificial tutor capable of effectively understanding and anticipating the student emotions during learning will have a significantly enhanced role. The paper presents a model of an artificial tutor endowed with synthesized emotions according to the BDE model, previously developed by the authors. It also analyzes possible student reactions while interacting with the learning material and the way the artificial tutor could anticipate and should respond to these reactions, with adequate actions.

Trend and seasonality of community-acquired Escherichia coli antimicrobial resistance and its dynamic relationship with antimicrobial use assessed by ARIMA models.

PubMed

Asencio Egea, María Ángeles; Huertas Vaquero, María; Carranza González, Rafael; Herráez Carrera, Óscar; Redondo González, Olga; Arias Arias, Ángel

2017-12-04

We studied the trend and seasonality of community-acquired Escherichia coli resistance and quantified its correlation with the previous use of certain antibiotics. A time series study of resistant community-acquired E. coli isolates and their association with antibiotic use was conducted in a Primary Health Care Area from 2008 to 2012. A Poisson regression model was constructed to estimate the trend and seasonality of E. coli resistance. A significant increasing trend in mean E. coli resistance to cephalosporins, aminoglycosides and nitrofurantoin was observed. Seasonal resistance to ciprofloxacin and amoxicillin-clavulanic acid was significantly higher in autumn-winter. There was a delay of 7, 10 and 12 months between the use of cotrimoxazole (P<0.038), fosfomycin (P<0.024) and amoxicillin-clavulanic acid (P<0.015), respectively, and the occurrence of E. coli resistance. An average delay of 10 months between the previous use of amoxicillin-clavulanic acid, cotrimoxazole and fosfomycin and the appearance of resistant community-acquired E. coli strains was detected. Copyright © 2017 Elsevier España, S.L.U. and Sociedad Española de Enfermedades Infecciosas y Microbiología Clínica. All rights reserved.