Explanation of power law behavior of autoregressive conditional duration processes based on the random multiplicative process.

PubMed

Sato, Aki-Hiro

2004-04-01

Autoregressive conditional duration (ACD) processes, which have the potential to be applied to power law distributions of complex systems found in natural science, life science, and social science, are analyzed both numerically and theoretically. An ACD(1) process exhibits the singular second order moment, which suggests that its probability density function (PDF) has a power law tail. It is verified that the PDF of the ACD(1) has a power law tail with an arbitrary exponent depending on a model parameter. On the basis of theory of the random multiplicative process a relation between the model parameter and the power law exponent is theoretically derived. It is confirmed that the relation is valid from numerical simulations. An application of the ACD(1) to intervals between two successive transactions in a foreign currency market is shown.

Beyond long memory in heart rate variability: An approach based on fractionally integrated autoregressive moving average time series models with conditional heteroscedasticity

NASA Astrophysics Data System (ADS)

Leite, Argentina; Paula Rocha, Ana; Eduarda Silva, Maria

2013-06-01

Heart Rate Variability (HRV) series exhibit long memory and time-varying conditional variance. This work considers the Fractionally Integrated AutoRegressive Moving Average (ARFIMA) models with Generalized AutoRegressive Conditional Heteroscedastic (GARCH) errors. ARFIMA-GARCH models may be used to capture and remove long memory and estimate the conditional volatility in 24 h HRV recordings. The ARFIMA-GARCH approach is applied to fifteen long term HRV series available at Physionet, leading to the discrimination among normal individuals, heart failure patients, and patients with atrial fibrillation.

Water balance models in one-month-ahead streamflow forecasting

USGS Publications Warehouse

Alley, William M.

1985-01-01

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

An INAR(1) Negative Multinomial Regression Model for Longitudinal Count Data.

ERIC Educational Resources Information Center

Bockenholt, Ulf

1999-01-01

Discusses a regression model for the analysis of longitudinal count data in a panel study by adapting an integer-valued first-order autoregressive (INAR(1)) Poisson process to represent time-dependent correlation between counts. Derives a new negative multinomial distribution by combining INAR(1) representation with a random effects approach.…

Stock price forecasting based on time series analysis

NASA Astrophysics Data System (ADS)

Chi, Wan Le

2018-05-01

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

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

PubMed

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

2014-01-01

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

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

NASA Astrophysics Data System (ADS)

Gong, Maozhen

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

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)…

Volatility in GARCH Models of Business Tendency Index

NASA Astrophysics Data System (ADS)

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

2018-01-01

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

Kepler AutoRegressive Planet Search: Motivation & Methodology

NASA Astrophysics Data System (ADS)

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

2015-08-01

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

Are Public Master's Institutions Cost Efficient? A Stochastic Frontier and Spatial Analysis

ERIC Educational Resources Information Center

Titus, Marvin A.; Vamosiu, Adriana; McClure, Kevin R.

2017-01-01

The current study examines costs, measured by educational and general (E&G) spending, and cost efficiency at 252 public master's institutions in the United States over a nine-year (2004-2012) period. We use a multi-product quadratic cost function and results from a random-effects model with a first-order autoregressive (AR1) disturbance term…

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 impact of incentives on intrinsic and extrinsic motives for fitness-center attendance in college first-year students.

PubMed

Pope, Lizzy; Harvey, Jean

2015-01-01

A criticism of incentives for health behaviors is that incentives undermine intrinsic motivation. The objective of this study was to determine the impact of monetary incentive provision on participation motives for exercise in first-year college students at a northeastern public university. Randomized-controlled trial. Public university in the Northeastern United States. One hundred seventeen first-year college students. Participants were randomized to one of three conditions: a control condition receiving no incentives for meeting fitness-center attendance goals; a discontinued-incentive condition receiving weekly incentives during fall semester 2011, and no incentives during spring semester 2012; or a continued-incentive condition receiving weekly incentives during fall semester, and incentives on a variable-interval schedule during spring semester. The Exercise Motivation Inventory 2 measured exercise participation motives at baseline, end of fall semester, and end of spring semester. Fitness-center attendance was monitored by using ID-card check-in/check-out records. Repeated-measures analyses using linear mixed models with first-order autoregressive covariance structures were run to compare motive changes in the three conditions. Participation motives of Enjoyment and Revitalization associated with intrinsic motivation did not decrease significantly over time in any of the conditions, F(4, 218) = 2.25, p = .065 and F(4, 220) = 1.67, p = .16, respectively. Intrinsically associated participation motives for exercise did not decrease with incentive provision. Therefore, incentives may encourage fitness-center attendance without negatively impacting participation motives for exercise.

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

NASA Astrophysics Data System (ADS)

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

2017-04-01

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

Using simulations and data to evaluate mean sensitivity (ζ) as a useful statistic in dendrochronology

Treesearch

Andrew G. Bunn; Esther Jansma; Mikko Korpela; Robert D. Westfall; James Baldwin

2013-01-01

Mean sensitivity (ζ) continues to be used in dendrochronology despite a literature that shows it to be of questionable value in describing the properties of a time series. We simulate first-order autoregressive models with known parameters and show that ζ is a function of variance and autocorrelation of a time series. We then use 500 random tree-ring...

Forecasting Instability Indicators in the Horn of Africa

DTIC Science & Technology

2008-03-01

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

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

NASA Astrophysics Data System (ADS)

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

2016-11-01

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

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

NASA Astrophysics Data System (ADS)

Vazifedan, Turaj; Shitan, Mahendran

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

Reciprocal Associations between Negative Affect, Binge Eating, and Purging in the Natural Environment in Women with Bulimia Nervosa

PubMed Central

Lavender, Jason M.; Utzinger, Linsey M.; Cao, Li; Wonderlich, Stephen A.; Engel, Scott G.; Mitchell, James E.; Crosby, Ross D.

2016-01-01

Although negative affect (NA) has been identified as a common trigger for bulimic behaviors, findings regarding NA following such behaviors have been mixed. This study examined reciprocal associations between NA and bulimic behaviors using real-time, naturalistic data. Participants were 133 women with DSM-IV bulimia nervosa (BN) who completed a two-week ecological momentary assessment (EMA) protocol in which they recorded bulimic behaviors and provided multiple daily ratings of NA. A multilevel autoregressive cross-lagged analysis was conducted to examine concurrent, first-order autoregressive, and prospective associations between NA, binge eating, and purging across the day. Results revealed positive concurrent associations between all variables across all time points, as well as numerous autoregressive associations. For prospective associations, higher NA predicted subsequent bulimic symptoms at multiple time points; conversely, binge eating predicted lower NA at multiple time points, and purging predicted higher NA at one time point. Several autoregressive and prospective associations were also found between binge eating and purging. This study used a novel approach to examine NA in relation to bulimic symptoms, contributing to the existing literature by directly examining the magnitude of the associations, examining differences in the associations across the day, and controlling for other associations in testing each effect in the model. These findings may have relevance for understanding the etiology and/or maintenance of bulimic symptoms, as well as potentially informing psychological interventions for BN. PMID:26692122

Multifractality and autoregressive processes of dry spell lengths in Europe: an approach to their complexity and predictability

NASA Astrophysics Data System (ADS)

Lana, X.; Burgueño, A.; Serra, C.; Martínez, M. D.

2017-01-01

Dry spell lengths, DSL, defined as the number of consecutive days with daily rain amounts below a given threshold, may provide relevant information about drought regimes. Taking advantage of a daily pluviometric database covering a great extension of Europe, a detailed analysis of the multifractality of the dry spell regimes is achieved. At the same time, an autoregressive process is applied with the aim of predicting DSL. A set of parameters, namely Hurst exponent, H, estimated from multifractal spectrum, f( α), critical Hölder exponent, α 0, for which f( α) reaches its maximum value, spectral width, W, and spectral asymmetry, B, permits a first clustering of European rain gauges in terms of the complexity of their DSL series. This set of parameters also allows distinguishing between time series describing fine- or smooth-structure of the DSL regime by using the complexity index, CI. Results of previous monofractal analyses also permits establishing comparisons between smooth-structures, relatively low correlation dimensions, notable predictive instability and anti-persistence of DSL for European areas, sometimes submitted to long droughts. Relationships are also found between the CI and the mean absolute deviation, MAD, and the optimum autoregressive order, OAO, of an ARIMA( p, d,0) autoregressive process applied to the DSL series. The detailed analysis of the discrepancies between empiric and predicted DSL underlines the uncertainty over predictability of long DSL, particularly for the Mediterranean region.

Kumaraswamy autoregressive moving average models for double bounded environmental data

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

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

PubMed

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

2013-11-01

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

Clustering of financial time series

NASA Astrophysics Data System (ADS)

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

2013-05-01

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

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

Treesearch

Jeffrey P. Prestemon; David T. Butry

2005-01-01

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

A Multilevel AR(1) Model: Allowing for Inter-Individual Differences in Trait-Scores, Inertia, and Innovation Variance.

PubMed

Jongerling, Joran; Laurenceau, Jean-Philippe; Hamaker, Ellen L

2015-01-01

In this article we consider a multilevel first-order autoregressive [AR(1)] model with random intercepts, random autoregression, and random innovation variance (i.e., the level 1 residual variance). Including random innovation variance is an important extension of the multilevel AR(1) model for two reasons. First, between-person differences in innovation variance are important from a substantive point of view, in that they capture differences in sensitivity and/or exposure to unmeasured internal and external factors that influence the process. Second, using simulation methods we show that modeling the innovation variance as fixed across individuals, when it should be modeled as a random effect, leads to biased parameter estimates. Additionally, we use simulation methods to compare maximum likelihood estimation to Bayesian estimation of the multilevel AR(1) model and investigate the trade-off between the number of individuals and the number of time points. We provide an empirical illustration by applying the extended multilevel AR(1) model to daily positive affect ratings from 89 married women over the course of 42 consecutive days.

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

NASA Astrophysics Data System (ADS)

Khan, Muhammad Yousaf; Mittnik, Stefan

2018-01-01

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

Reciprocal associations between negative affect, binge eating, and purging in the natural environment in women with bulimia nervosa.

PubMed

Lavender, Jason M; Utzinger, Linsey M; Cao, Li; Wonderlich, Stephen A; Engel, Scott G; Mitchell, James E; Crosby, Ross D

2016-04-01

Although negative affect (NA) has been identified as a common trigger for bulimic behaviors, findings regarding NA following such behaviors have been mixed. This study examined reciprocal associations between NA and bulimic behaviors using real-time, naturalistic data. Participants were 133 women with bulimia nervosa (BN) according to the 4th edition of the Diagnostic and Statistical Manual of Mental Disorders who completed a 2-week ecological momentary assessment protocol in which they recorded bulimic behaviors and provided multiple daily ratings of NA. A multilevel autoregressive cross-lagged analysis was conducted to examine concurrent, first-order autoregressive, and prospective associations between NA, binge eating, and purging across the day. Results revealed positive concurrent associations between all variables across all time points, as well as numerous autoregressive associations. For prospective associations, higher NA predicted subsequent bulimic symptoms at multiple time points; conversely, binge eating predicted lower NA at multiple time points, and purging predicted higher NA at 1 time point. Several autoregressive and prospective associations were also found between binge eating and purging. This study used a novel approach to examine NA in relation to bulimic symptoms, contributing to the existing literature by directly examining the magnitude of the associations, examining differences in the associations across the day, and controlling for other associations in testing each effect in the model. These findings may have relevance for understanding the etiology and/or maintenance of bulimic symptoms, as well as potentially informing psychological interventions for BN. (c) 2016 APA, all rights reserved).

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

PubMed

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

2017-01-01

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

Adaptive spline autoregression threshold method in forecasting Mitsubishi car sales volume at PT Srikandi Diamond Motors

NASA Astrophysics Data System (ADS)

Susanti, D.; Hartini, E.; Permana, A.

2017-01-01

Sale and purchase of the growing competition between companies in Indonesian, make every company should have a proper planning in order to win the competition with other companies. One of the things that can be done to design the plan is to make car sales forecast for the next few periods, it’s required that the amount of inventory of cars that will be sold in proportion to the number of cars needed. While to get the correct forecasting, on of the methods that can be used is the method of Adaptive Spline Threshold Autoregression (ASTAR). Therefore, this time the discussion will focus on the use of Adaptive Spline Threshold Autoregression (ASTAR) method in forecasting the volume of car sales in PT.Srikandi Diamond Motors using time series data.In the discussion of this research, forecasting using the method of forecasting value Adaptive Spline Threshold Autoregression (ASTAR) produce approximately correct.

Texture classification using autoregressive filtering

NASA Technical Reports Server (NTRS)

Lawton, W. M.; Lee, M.

1984-01-01

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

Generation of Some First-Order Autoregressive Markovian Sequences of Positive Random Variables with Given Marginal Distributions,

DTIC Science & Technology

1981-03-01

Again E( XnX 1 Xn) Xn + (l-aB)/X PlXn-1 + (l-Pl)/x 2.11) and X0 E0 gives a stationary sequence. Thus the correla- tions and regressions are the...sequence, although the sample paths will tend to have runs-up. A similar analysis given in Lawrance and Lewis [5] shows that 1 1 + i a + au (3.7) E( XnX

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

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

Biyanto, Totok R.

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

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

PubMed

Zardad, Asma; Mohsin, Asma; Zaman, Khalid

2013-12-01

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

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.

[Primary branch size of Pinus koraiensis plantation: a prediction based on linear mixed effect model].

PubMed

Dong, Ling-Bo; Liu, Zhao-Gang; Li, Feng-Ri; Jiang, Li-Chun

2013-09-01

By using the branch analysis data of 955 standard branches from 60 sampled trees in 12 sampling plots of Pinus koraiensis plantation in Mengjiagang Forest Farm in Heilongjiang Province of Northeast China, and based on the linear mixed-effect model theory and methods, the models for predicting branch variables, including primary branch diameter, length, and angle, were developed. Considering tree effect, the MIXED module of SAS software was used to fit the prediction models. The results indicated that the fitting precision of the models could be improved by choosing appropriate random-effect parameters and variance-covariance structure. Then, the correlation structures including complex symmetry structure (CS), first-order autoregressive structure [AR(1)], and first-order autoregressive and moving average structure [ARMA(1,1)] were added to the optimal branch size mixed-effect model. The AR(1) improved the fitting precision of branch diameter and length mixed-effect model significantly, but all the three structures didn't improve the precision of branch angle mixed-effect model. In order to describe the heteroscedasticity during building mixed-effect model, the CF1 and CF2 functions were added to the branch mixed-effect model. CF1 function improved the fitting effect of branch angle mixed model significantly, whereas CF2 function improved the fitting effect of branch diameter and length mixed model significantly. Model validation confirmed that the mixed-effect model could improve the precision of prediction, as compare to the traditional regression model for the branch size prediction of Pinus koraiensis plantation.

Statistical Modeling and Prediction for Tourism Economy Using Dendritic Neural Network

PubMed Central

Yu, Ying; Wang, Yirui; Tang, Zheng

2017-01-01

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

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

PubMed

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

2017-01-01

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

Is First-Order Vector Autoregressive Model Optimal for fMRI Data?

PubMed

Ting, Chee-Ming; Seghouane, Abd-Krim; Khalid, Muhammad Usman; Salleh, Sh-Hussain

2015-09-01

We consider the problem of selecting the optimal orders of vector autoregressive (VAR) models for fMRI data. Many previous studies used model order of one and ignored that it may vary considerably across data sets depending on different data dimensions, subjects, tasks, and experimental designs. In addition, the classical information criteria (IC) used (e.g., the Akaike IC (AIC)) are biased and inappropriate for the high-dimensional fMRI data typically with a small sample size. We examine the mixed results on the optimal VAR orders for fMRI, especially the validity of the order-one hypothesis, by a comprehensive evaluation using different model selection criteria over three typical data types--a resting state, an event-related design, and a block design data set--with varying time series dimensions obtained from distinct functional brain networks. We use a more balanced criterion, Kullback's IC (KIC) based on Kullback's symmetric divergence combining two directed divergences. We also consider the bias-corrected versions (AICc and KICc) to improve VAR model selection in small samples. Simulation results show better small-sample selection performance of the proposed criteria over the classical ones. Both bias-corrected ICs provide more accurate and consistent model order choices than their biased counterparts, which suffer from overfitting, with KICc performing the best. Results on real data show that orders greater than one were selected by all criteria across all data sets for the small to moderate dimensions, particularly from small, specific networks such as the resting-state default mode network and the task-related motor networks, whereas low orders close to one but not necessarily one were chosen for the large dimensions of full-brain networks.

Modeling climate effects on hip fracture rate by the multivariate GARCH model in Montreal region, Canada.

PubMed

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

2014-07-01

Changes in extreme meteorological variables and the demographic shift towards an older population have made it important to investigate the association of climate variables and hip fracture by advanced methods in order to determine the climate variables that most affect hip fracture incidence. The nonlinear autoregressive moving average with exogenous variable-generalized autoregressive conditional heteroscedasticity (ARMAX-GARCH) and multivariate GARCH (MGARCH) time series approaches were applied to investigate the nonlinear association between hip fracture rate in female and male patients aged 40-74 and 75+ years and climate variables in the period of 1993-2004, in Montreal, Canada. The models describe 50-56% of daily variation in hip fracture rate and identify snow depth, air temperature, day length and air pressure as the influencing variables on the time-varying mean and variance of the hip fracture rate. The conditional covariance between climate variables and hip fracture rate is increasing exponentially, showing that the effect of climate variables on hip fracture rate is most acute when rates are high and climate conditions are at their worst. In Montreal, climate variables, particularly snow depth and air temperature, appear to be important predictors of hip fracture incidence. The association of climate variables and hip fracture does not seem to change linearly with time, but increases exponentially under harsh climate conditions. The results of this study can be used to provide an adaptive climate-related public health program and ti guide allocation of services for avoiding hip fracture risk.

Modeling climate effects on hip fracture rate by the multivariate GARCH model in Montreal region, Canada

NASA Astrophysics Data System (ADS)

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

2014-07-01

Changes in extreme meteorological variables and the demographic shift towards an older population have made it important to investigate the association of climate variables and hip fracture by advanced methods in order to determine the climate variables that most affect hip fracture incidence. The nonlinear autoregressive moving average with exogenous variable-generalized autoregressive conditional heteroscedasticity (ARMA X-GARCH) and multivariate GARCH (MGARCH) time series approaches were applied to investigate the nonlinear association between hip fracture rate in female and male patients aged 40-74 and 75+ years and climate variables in the period of 1993-2004, in Montreal, Canada. The models describe 50-56 % of daily variation in hip fracture rate and identify snow depth, air temperature, day length and air pressure as the influencing variables on the time-varying mean and variance of the hip fracture rate. The conditional covariance between climate variables and hip fracture rate is increasing exponentially, showing that the effect of climate variables on hip fracture rate is most acute when rates are high and climate conditions are at their worst. In Montreal, climate variables, particularly snow depth and air temperature, appear to be important predictors of hip fracture incidence. The association of climate variables and hip fracture does not seem to change linearly with time, but increases exponentially under harsh climate conditions. The results of this study can be used to provide an adaptive climate-related public health program and ti guide allocation of services for avoiding hip fracture risk.

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

PubMed

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

2015-01-01

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

A frequency domain global parameter estimation method for multiple reference frequency response measurements

NASA Astrophysics Data System (ADS)

Shih, C. Y.; Tsuei, Y. G.; Allemang, R. J.; Brown, D. L.

1988-10-01

A method of using the matrix Auto-Regressive Moving Average (ARMA) model in the Laplace domain for multiple-reference global parameter identification is presented. This method is particularly applicable to the area of modal analysis where high modal density exists. The method is also applicable when multiple reference frequency response functions are used to characterise linear systems. In order to facilitate the mathematical solution, the Forsythe orthogonal polynomial is used to reduce the ill-conditioning of the formulated equations and to decouple the normal matrix into two reduced matrix blocks. A Complex Mode Indicator Function (CMIF) is introduced, which can be used to determine the proper order of the rational polynomials.

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

PubMed

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

2018-04-19

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

Use of high-order spectral moments in Doppler weather radar

NASA Astrophysics Data System (ADS)

di Vito, A.; Galati, G.; Veredice, A.

Three techniques to estimate the skewness and curtosis of measured precipitation spectra are evaluated. These are: (1) an extension of the pulse-pair technique, (2) fitting the autocorrelation function with a least square polynomial and differentiating it, and (3) the autoregressive spectral estimation. The third technique provides the best results but has an exceedingly large computation burden. The first technique does not supply any useful results due to the crude approximation of the derivatives of the ACF. The second technique requires further study to reduce its variance.

The conditional power of randomization tests for single-case effect sizes in designs with randomized treatment order: A Monte Carlo simulation study.

PubMed

Michiels, Bart; Heyvaert, Mieke; Onghena, Patrick

2018-04-01

The conditional power (CP) of the randomization test (RT) was investigated in a simulation study in which three different single-case effect size (ES) measures were used as the test statistics: the mean difference (MD), the percentage of nonoverlapping data (PND), and the nonoverlap of all pairs (NAP). Furthermore, we studied the effect of the experimental design on the RT's CP for three different single-case designs with rapid treatment alternation: the completely randomized design (CRD), the randomized block design (RBD), and the restricted randomized alternation design (RRAD). As a third goal, we evaluated the CP of the RT for three types of simulated data: data generated from a standard normal distribution, data generated from a uniform distribution, and data generated from a first-order autoregressive Gaussian process. The results showed that the MD and NAP perform very similarly in terms of CP, whereas the PND performs substantially worse. Furthermore, the RRAD yielded marginally higher power in the RT, followed by the CRD and then the RBD. Finally, the power of the RT was almost unaffected by the type of the simulated data. On the basis of the results of the simulation study, we recommend at least 20 measurement occasions for single-case designs with a randomized treatment order that are to be evaluated with an RT using a 5% significance level. Furthermore, we do not recommend use of the PND, because of its low power in the RT.

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

NASA Astrophysics Data System (ADS)

Shi, Jinfei; Zhu, Songqing; Chen, Ruwen

2017-12-01

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

Weather, day length and physical activity in older adults: Cross-sectional results from the European Prospective Investigation into Cancer and Nutrition (EPIC) Norfolk Cohort.

PubMed

Wu, Yu-Tzu; Luben, Robert; Wareham, Nicholas; Griffin, Simon; Jones, Andy P

2017-01-01

A wide range of environmental factors have been related to active ageing, but few studies have explored the impact of weather and day length on physical activity in older adults. We investigate the cross-sectional association between weather conditions, day length and activity in older adults using a population-based cohort in England, the European Prospective Investigation into Cancer and Nutrition (EPIC) Norfolk study. Physical activity was measured objectively over 7 days using an accelerometer and this was used to calculate daily total physical activity (counts per minute), daily minutes of sedentary behaviour and light, moderate and vigorous physical activity (LMVPA). Day length and two types of weather conditions, precipitation and temperature, were obtained from a local weather station. The association between these variables and physical activity was examined by multilevel first-order autoregressive modelling. After adjusting for individual factors, short day length and poor weather conditions, including high precipitation and low temperatures, were associated with up to 10% lower average physical activity (p<0.01) and 8 minutes less time spent in LMVPA but 15 minutes more sedentary time, compared to the best conditions. Day length and weather conditions appear to be an important factor related to active ageing. Future work should focus on developing potential interventions to reduce their impact on physical activity behaviours in older adults.

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.

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.

An algebraic method for constructing stable and consistent autoregressive filters

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

Harlim, John, E-mail: jharlim@psu.edu; Department of Meteorology, the Pennsylvania State University, University Park, PA 16802; Hong, Hoon, E-mail: hong@ncsu.edu

2015-02-15

In this paper, we introduce an algebraic method to construct stable and consistent univariate autoregressive (AR) models of low order for filtering and predicting nonlinear turbulent signals with memory depth. By stable, we refer to the classical stability condition for the AR model. By consistent, we refer to the classical consistency constraints of Adams–Bashforth methods of order-two. One attractive feature of this algebraic method is that the model parameters can be obtained without directly knowing any training data set as opposed to many standard, regression-based parameterization methods. It takes only long-time average statistics as inputs. The proposed method provides amore » discretization time step interval which guarantees the existence of stable and consistent AR model and simultaneously produces the parameters for the AR models. In our numerical examples with two chaotic time series with different characteristics of decaying time scales, we find that the proposed AR models produce significantly more accurate short-term predictive skill and comparable filtering skill relative to the linear regression-based AR models. These encouraging results are robust across wide ranges of discretization times, observation times, and observation noise variances. Finally, we also find that the proposed model produces an improved short-time prediction relative to the linear regression-based AR-models in forecasting a data set that characterizes the variability of the Madden–Julian Oscillation, a dominant tropical atmospheric wave pattern.« less

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

NASA Astrophysics Data System (ADS)

Nurhaida, Subanar, Abdurakhman, Abadi, Agus Maman

2017-08-01

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

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

PubMed

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

2017-01-01

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

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

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

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

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

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

PubMed

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

2017-01-01

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

Computational problems in autoregressive moving average (ARMA) models

NASA Technical Reports Server (NTRS)

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

1981-01-01

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

Processing on weak electric signals by the autoregressive model

NASA Astrophysics Data System (ADS)

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

2008-10-01

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

On the Empirical Importance of the Conditional Skewness Assumption in Modelling the Relationship between Risk and Return

NASA Astrophysics Data System (ADS)

Pipień, M.

2008-09-01

We present the results of an application of Bayesian inference in testing the relation between risk and return on the financial instruments. On the basis of the Intertemporal Capital Asset Pricing Model, proposed by Merton we built a general sampling distribution suitable in analysing this relationship. The most important feature of our assumptions is that the skewness of the conditional distribution of returns is used as an alternative source of relation between risk and return. This general specification relates to Skewed Generalized Autoregressive Conditionally Heteroscedastic-in-Mean model. In order to make conditional distribution of financial returns skewed we considered the unified approach based on the inverse probability integral transformation. In particular, we applied hidden truncation mechanism, inverse scale factors, order statistics concept, Beta and Bernstein distribution transformations and also a constructive method. Based on the daily excess returns on the Warsaw Stock Exchange Index we checked the empirical importance of the conditional skewness assumption on the relation between risk and return on the Warsaw Stock Market. We present posterior probabilities of all competing specifications as well as the posterior analysis of the positive sign of the tested relationship.

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

NASA Astrophysics Data System (ADS)

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

2007-11-01

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

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

NASA Astrophysics Data System (ADS)

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

2017-04-01

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

Optimization of the time series NDVI-rainfall relationship using linear mixed-effects modeling for the anti-desertification area in the Beijing and Tianjin sandstorm source region

NASA Astrophysics Data System (ADS)

Wang, Jin; Sun, Tao; Fu, Anmin; Xu, Hao; Wang, Xinjie

2018-05-01

Degradation in drylands is a critically important global issue that threatens ecosystem and environmental in many ways. Researchers have tried to use remote sensing data and meteorological data to perform residual trend analysis and identify human-induced vegetation changes. However, complex interactions between vegetation and climate, soil units and topography have not yet been considered. Data used in the study included annual accumulated Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m normalized difference vegetation index (NDVI) from 2002 to 2013, accumulated rainfall from September to August, digital elevation model (DEM) and soil units. This paper presents linear mixed-effect (LME) modeling methods for the NDVI-rainfall relationship. We developed linear mixed-effects models that considered the random effects of sample points nested in soil units for nested two-level modeling and single-level modeling of soil units and sample points, respectively. Additionally, three functions, including the exponential function (exp), the power function (power), and the constant plus power function (CPP), were tested to remove heterogeneity, and an additional three correlation structures, including the first-order autoregressive structure [AR(1)], a combination of first-order autoregressive and moving average structures [ARMA(1,1)] and the compound symmetry structure (CS), were used to address the spatiotemporal correlations. It was concluded that the nested two-level model considering both heteroscedasticity with (CPP) and spatiotemporal correlation with [ARMA(1,1)] showed the best performance (AMR = 0.1881, RMSE = 0.2576, adj- R 2 = 0.9593). Variations between soil units and sample points that may have an effect on the NDVI-rainfall relationship should be included in model structures, and linear mixed-effects modeling achieves this in an effective and accurate way.

Weather, day length and physical activity in older adults: Cross-sectional results from the European Prospective Investigation into Cancer and Nutrition (EPIC) Norfolk Cohort

PubMed Central

Wu, Yu-Tzu; Luben, Robert; Wareham, Nicholas; Griffin, Simon; Jones, Andy P.

2017-01-01

Background A wide range of environmental factors have been related to active ageing, but few studies have explored the impact of weather and day length on physical activity in older adults. We investigate the cross-sectional association between weather conditions, day length and activity in older adults using a population-based cohort in England, the European Prospective Investigation into Cancer and Nutrition (EPIC) Norfolk study. Methods Physical activity was measured objectively over 7 days using an accelerometer and this was used to calculate daily total physical activity (counts per minute), daily minutes of sedentary behaviour and light, moderate and vigorous physical activity (LMVPA). Day length and two types of weather conditions, precipitation and temperature, were obtained from a local weather station. The association between these variables and physical activity was examined by multilevel first-order autoregressive modelling. Results After adjusting for individual factors, short day length and poor weather conditions, including high precipitation and low temperatures, were associated with up to 10% lower average physical activity (p<0.01) and 8 minutes less time spent in LMVPA but 15 minutes more sedentary time, compared to the best conditions. Conclusion Day length and weather conditions appear to be an important factor related to active ageing. Future work should focus on developing potential interventions to reduce their impact on physical activity behaviours in older adults. PMID:28562613

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

NASA Astrophysics Data System (ADS)

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

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

Instantaneous nonlinear assessment of complex cardiovascular dynamics by Laguerre-Volterra point process models.

PubMed

Valenza, Gaetano; Citi, Luca; Barbieri, Riccardo

2013-01-01

We report an exemplary study of instantaneous assessment of cardiovascular dynamics performed using point-process nonlinear models based on Laguerre expansion of the linear and nonlinear Wiener-Volterra kernels. As quantifiers, instantaneous measures such as high order spectral features and Lyapunov exponents can be estimated from a quadratic and cubic autoregressive formulation of the model first order moment, respectively. Here, these measures are evaluated on heartbeat series coming from 16 healthy subjects and 14 patients with Congestive Hearth Failure (CHF). Data were gathered from the on-line repository PhysioBank, which has been taken as landmark for testing nonlinear indices. Results show that the proposed nonlinear Laguerre-Volterra point-process methods are able to track the nonlinear and complex cardiovascular dynamics, distinguishing significantly between CHF and healthy heartbeat series.

Sleep analysis for wearable devices applying autoregressive parametric models.

PubMed

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

2005-01-01

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

Failure monitoring in dynamic systems: Model construction without fault training data

NASA Technical Reports Server (NTRS)

Smyth, P.; Mellstrom, J.

1993-01-01

Advances in the use of autoregressive models, pattern recognition methods, and hidden Markov models for on-line health monitoring of dynamic systems (such as DSN antennas) have recently been reported. However, the algorithms described in previous work have the significant drawback that data acquired under fault conditions are assumed to be available in order to train the model used for monitoring the system under observation. This article reports that this assumption can be relaxed and that hidden Markov monitoring models can be constructed using only data acquired under normal conditions and prior knowledge of the system characteristics being measured. The method is described and evaluated on data from the DSS 13 34-m beam wave guide antenna. The primary conclusion from the experimental results is that the method is indeed practical and holds considerable promise for application at the 70-m antenna sites where acquisition of fault data under controlled conditions is not realistic.

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

NASA Astrophysics Data System (ADS)

Scharnagl, Benedikt; Durner, Wolfgang

2013-04-01

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

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

NASA Astrophysics Data System (ADS)

Ağaç, Kübra; Koçak, Kasım; Deniz, Ali

2015-04-01

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

Maximum likelihood estimation for periodic autoregressive moving average models

USGS Publications Warehouse

Vecchia, A.V.

1985-01-01

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

Autoregressive linear least square single scanning electron microscope image signal-to-noise ratio estimation.

PubMed

Sim, Kok Swee; NorHisham, Syafiq

2016-11-01

A technique based on linear Least Squares Regression (LSR) model is applied to estimate signal-to-noise ratio (SNR) of scanning electron microscope (SEM) images. In order to test the accuracy of this technique on SNR estimation, a number of SEM images are initially corrupted with white noise. The autocorrelation function (ACF) of the original and the corrupted SEM images are formed to serve as the reference point to estimate the SNR value of the corrupted image. The LSR technique is then compared with the previous three existing techniques known as nearest neighbourhood, first-order interpolation, and the combination of both nearest neighborhood and first-order interpolation. The actual and the estimated SNR values of all these techniques are then calculated for comparison purpose. It is shown that the LSR technique is able to attain the highest accuracy compared to the other three existing techniques as the absolute difference between the actual and the estimated SNR value is relatively small. SCANNING 38:771-782, 2016. © 2016 Wiley Periodicals, Inc. © Wiley Periodicals, Inc.

Damage localization of marine risers using time series of vibration signals

NASA Astrophysics Data System (ADS)

Liu, Hao; Yang, Hezhen; Liu, Fushun

2014-10-01

Based on dynamic response signals a damage detection algorithm is developed for marine risers. Damage detection methods based on numerous modal properties have encountered issues in the researches in offshore oil community. For example, significant increase in structure mass due to marine plant/animal growth and changes in modal properties by equipment noise are not the result of damage for riser structures. In an attempt to eliminate the need to determine modal parameters, a data-based method is developed. The implementation of the method requires that vibration data are first standardized to remove the influence of different loading conditions and the autoregressive moving average (ARMA) model is used to fit vibration response signals. In addition, a damage feature factor is introduced based on the autoregressive (AR) parameters. After that, the Euclidean distance between ARMA models is subtracted as a damage indicator for damage detection and localization and a top tensioned riser simulation model with different damage scenarios is analyzed using the proposed method with dynamic acceleration responses of a marine riser as sensor data. Finally, the influence of measured noise is analyzed. According to the damage localization results, the proposed method provides accurate damage locations of risers and is robust to overcome noise effect.

Prediction of global ionospheric VTEC maps using an adaptive autoregressive model

NASA Astrophysics Data System (ADS)

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

2018-02-01

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

Predicting long-term catchment nutrient export: the use of nonlinear time series models

NASA Astrophysics Data System (ADS)

Valent, Peter; Howden, Nicholas J. K.; Szolgay, Jan; Komornikova, Magda

2010-05-01

After the Second World War the nitrate concentrations in European water bodies changed significantly as the result of increased nitrogen fertilizer use and changes in land use. However, in the last decades, as a consequence of the implementation of nitrate-reducing measures in Europe, the nitrate concentrations in water bodies slowly decrease. This causes that the mean and variance of the observed time series also changes with time (nonstationarity and heteroscedascity). In order to detect changes and properly describe the behaviour of such time series by time series analysis, linear models (such as autoregressive (AR), moving average (MA) and autoregressive moving average models (ARMA)), are no more suitable. Time series with sudden changes in statistical characteristics can cause various problems in the calibration of traditional water quality models and thus give biased predictions. Proper statistical analysis of these non-stationary and heteroscedastic time series with the aim of detecting and subsequently explaining the variations in their statistical characteristics requires the use of nonlinear time series models. This information can be then used to improve the model building and calibration of conceptual water quality model or to select right calibration periods in order to produce reliable predictions. The objective of this contribution is to analyze two long time series of nitrate concentrations of the rivers Ouse and Stour with advanced nonlinear statistical modelling techniques and compare their performance with traditional linear models of the ARMA class in order to identify changes in the time series characteristics. The time series were analysed with nonlinear models with multiple regimes represented by self-exciting threshold autoregressive (SETAR) and Markov-switching models (MSW). The analysis showed that, based on the value of residual sum of squares (RSS) in both datasets, SETAR and MSW models described the time-series better than models of the ARMA class. In most cases the relative improvement of SETAR models against AR models of first order was low ranging between 1% and 4% with the exception of the three-regime model for the River Stour time-series where the improvement was 48.9%. In comparison, the relative improvement of MSW models was between 44.6% and 52.5 for two-regime and from 60.4% to 75% for three-regime models. However, the visual assessment of models plotted against original datasets showed that despite a high value of RSS, some ARMA models could describe the analyzed time-series better than AR, MA and SETAR models with lower values of RSS. In both datasets MSW models provided a very good visual fit describing most of the extreme values.

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

PubMed Central

Atyabi, Adham; Shic, Frederick; Naples, Adam

2016-01-01

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

Time-series analysis of delta13C from tree rings. I. Time trends and autocorrelation.

PubMed

Monserud, R A; Marshall, J D

2001-09-01

Univariate time-series analyses were conducted on stable carbon isotope ratios obtained from tree-ring cellulose. We looked for the presence and structure of autocorrelation. Significant autocorrelation violates the statistical independence assumption and biases hypothesis tests. Its presence would indicate the existence of lagged physiological effects that persist for longer than the current year. We analyzed data from 28 trees (60-85 years old; mean = 73 years) of western white pine (Pinus monticola Dougl.), ponderosa pine (Pinus ponderosa Laws.), and Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco var. glauca) growing in northern Idaho. Material was obtained by the stem analysis method from rings laid down in the upper portion of the crown throughout each tree's life. The sampling protocol minimized variation caused by changing light regimes within each tree. Autoregressive moving average (ARMA) models were used to describe the autocorrelation structure over time. Three time series were analyzed for each tree: the stable carbon isotope ratio (delta(13)C); discrimination (delta); and the difference between ambient and internal CO(2) concentrations (c(a) - c(i)). The effect of converting from ring cellulose to whole-leaf tissue did not affect the analysis because it was almost completely removed by the detrending that precedes time-series analysis. A simple linear or quadratic model adequately described the time trend. The residuals from the trend had a constant mean and variance, thus ensuring stationarity, a requirement for autocorrelation analysis. The trend over time for c(a) - c(i) was particularly strong (R(2) = 0.29-0.84). Autoregressive moving average analyses of the residuals from these trends indicated that two-thirds of the individual tree series contained significant autocorrelation, whereas the remaining third were random (white noise) over time. We were unable to distinguish between individuals with and without significant autocorrelation beforehand. Significant ARMA models were all of low order, with either first- or second-order (i.e., lagged 1 or 2 years, respectively) models performing well. A simple autoregressive (AR(1)), model was the most common. The most useful generalization was that the same ARMA model holds for each of the three series (delta(13)C, delta, c(a) - c(i)) for an individual tree, if the time trend has been properly removed for each series. The mean series for the two pine species were described by first-order ARMA models (1-year lags), whereas the Douglas-fir mean series were described by second-order models (2-year lags) with negligible first-order effects. Apparently, the process of constructing a mean time series for a species preserves an underlying signal related to delta(13)C while canceling some of the random individual tree variation. Furthermore, the best model for the overall mean series (e.g., for a species) cannot be inferred from a consensus of the individual tree model forms, nor can its parameters be estimated reliably from the mean of the individual tree parameters. Because two-thirds of the individual tree time series contained significant autocorrelation, the normal assumption of a random structure over time is unwarranted, even after accounting for the time trend. The residuals of an appropriate ARMA model satisfy the independence assumption, and can be used to make hypothesis tests.

Macro-level safety analysis of pedestrian crashes in Shanghai, China.

PubMed

Wang, Xuesong; Yang, Junguang; Lee, Chris; Ji, Zhuoran; You, Shikai

2016-11-01

Pedestrian safety has become one of the most important issues in the field of traffic safety. This study aims at investigating the association between pedestrian crash frequency and various predictor variables including roadway, socio-economic, and land-use features. The relationships were modeled using the data from 263 Traffic Analysis Zones (TAZs) within the urban area of Shanghai - the largest city in China. Since spatial correlation exists among the zonal-level data, Bayesian Conditional Autoregressive (CAR) models with seven different spatial weight features (i.e. (a) 0-1 first order, adjacency-based, (b) common boundary-length-based, (c) geometric centroid-distance-based, (d) crash-weighted centroid-distance-based, (e) land use type, adjacency-based, (f) land use intensity, adjacency-based, and (g) geometric centroid-distance-order) were developed to characterize the spatial correlations among TAZs. Model results indicated that the geometric centroid-distance-order spatial weight feature, which was introduced in macro-level safety analysis for the first time, outperformed all the other spatial weight features. Population was used as the surrogate for pedestrian exposure, and had a positive effect on pedestrian crashes. Other significant factors included length of major arterials, length of minor arterials, road density, average intersection spacing, percentage of 3-legged intersections, and area of TAZ. Pedestrian crashes were higher in TAZs with medium land use intensity than in TAZs with low and high land use intensity. Thus, higher priority should be given to TAZs with medium land use intensity to improve pedestrian safety. Overall, these findings can help transportation planners and managers understand the characteristics of pedestrian crashes and improve pedestrian safety. Copyright © 2016 Elsevier Ltd. All rights reserved.

Three essays on price dynamics and causations among energy markets and macroeconomic information

NASA Astrophysics Data System (ADS)

Hong, Sung Wook

This dissertation examines three important issues in energy markets: price dynamics, information flow, and structural change. We discuss each issue in detail, building empirical time series models, analyzing the results, and interpreting the findings. First, we examine the contemporaneous interdependencies and information flows among crude oil, natural gas, and electricity prices in the United States (US) through the multivariate generalized autoregressive conditional heteroscedasticity (MGARCH) model, Directed Acyclic Graph (DAG) for contemporaneous causal structures and Bernanke factorization for price dynamic processes. Test results show that the DAG from residuals of out-of-sample-forecast is consistent with the DAG from residuals of within-sample-fit. The result supports innovation accounting analysis based on DAGs using residuals of out-of-sample-forecast. Second, we look at the effects of the federal fund rate and/or WTI crude oil price shock on US macroeconomic and financial indicators by using a Factor Augmented Vector Autoregression (FAVAR) model and a graphical model without any deductive assumption. The results show that, in contemporaneous time, the federal fund rate shock is exogenous as the identifying assumption in the Vector Autoregression (VAR) framework of the monetary shock transmission mechanism, whereas the WTI crude oil price return is not exogenous. Third, we examine price dynamics and contemporaneous causality among the price returns of WTI crude oil, gasoline, corn, and the S&P 500. We look for structural break points and then build an econometric model to find the consistent sub-periods having stable parameters in a given VAR framework and to explain recent movements and interdependency among returns. We found strong evidence of two structural breaks and contemporaneous causal relationships among the residuals, but also significant differences between contemporaneous causal structures for each sub-period.

Kepler AutoRegressive Planet Search (KARPS)

NASA Astrophysics Data System (ADS)

Caceres, Gabriel

2018-01-01

One of the main obstacles in detecting faint planetary transits is the intrinsic stellar variability of the host star. The Kepler AutoRegressive Planet Search (KARPS) project implements statistical methodology associated with autoregressive processes (in particular, ARIMA and ARFIMA) to model stellar lightcurves in order to improve exoplanet transit detection. We also develop a novel Transit Comb Filter (TCF) applied to the AR residuals which provides a periodogram analogous to the standard Box-fitting Least Squares (BLS) periodogram. We train a random forest classifier on known Kepler Objects of Interest (KOIs) using select features from different stages of this analysis, and then use ROC curves to define and calibrate the criteria to recover the KOI planet candidates with high fidelity. These statistical methods are detailed in a contributed poster (Feigelson et al., this meeting).These procedures are applied to the full DR25 dataset of NASA’s Kepler mission. Using the classification criteria, a vast majority of known KOIs are recovered and dozens of new KARPS Candidate Planets (KCPs) discovered, including ultra-short period exoplanets. The KCPs will be briefly presented and discussed.

Assessment and prediction of air quality using fuzzy logic and autoregressive models

NASA Astrophysics Data System (ADS)

Carbajal-Hernández, José Juan; Sánchez-Fernández, Luis P.; Carrasco-Ochoa, Jesús A.; Martínez-Trinidad, José Fco.

2012-12-01

In recent years, artificial intelligence methods have been used for the treatment of environmental problems. This work, presents two models for assessment and prediction of air quality. First, we develop a new computational model for air quality assessment in order to evaluate toxic compounds that can harm sensitive people in urban areas, affecting their normal activities. In this model we propose to use a Sigma operator to statistically asses air quality parameters using their historical data information and determining their negative impact in air quality based on toxicity limits, frequency average and deviations of toxicological tests. We also introduce a fuzzy inference system to perform parameter classification using a reasoning process and integrating them in an air quality index describing the pollution levels in five stages: excellent, good, regular, bad and danger, respectively. The second model proposed in this work predicts air quality concentrations using an autoregressive model, providing a predicted air quality index based on the fuzzy inference system previously developed. Using data from Mexico City Atmospheric Monitoring System, we perform a comparison among air quality indices developed for environmental agencies and similar models. Our results show that our models are an appropriate tool for assessing site pollution and for providing guidance to improve contingency actions in urban areas.

Small Sample Properties of Bayesian Multivariate Autoregressive Time Series Models

ERIC Educational Resources Information Center

Price, Larry R.

2012-01-01

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

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

NASA Astrophysics Data System (ADS)

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

2014-09-01

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

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

PubMed Central

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

2014-01-01

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

Acceleration and Velocity Sensing from Measured Strain

NASA Technical Reports Server (NTRS)

Pak, Chan-Gi; Truax, Roger

2015-01-01

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

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

PubMed

Wang, Jinjia; Zhang, Yanna

2015-02-01

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

Reservoir optimisation using El Niño information. Case study of Daule Peripa (Ecuador)

NASA Astrophysics Data System (ADS)

Gelati, Emiliano; Madsen, Henrik; Rosbjerg, Dan

2010-05-01

The optimisation of water resources systems requires the ability to produce runoff scenarios that are consistent with available climatic information. We approach stochastic runoff modelling with a Markov-modulated autoregressive model with exogenous input, which belongs to the class of Markov-switching models. The model assumes runoff parameterisation to be conditioned on a hidden climatic state following a Markov chain, whose state transition probabilities depend on climatic information. This approach allows stochastic modeling of non-stationary runoff, as runoff anomalies are described by a mixture of autoregressive models with exogenous input, each one corresponding to a climate state. We calibrate the model on the inflows of the Daule Peripa reservoir located in western Ecuador, where the occurrence of El Niño leads to anomalously heavy rainfall caused by positive sea surface temperature anomalies along the coast. El Niño - Southern Oscillation (ENSO) information is used to condition the runoff parameterisation. Inflow predictions are realistic, especially at the occurrence of El Niño events. The Daule Peripa reservoir serves a hydropower plant and a downstream water supply facility. Using historical ENSO records, synthetic monthly inflow scenarios are generated for the period 1950-2007. These scenarios are used as input to perform stochastic optimisation of the reservoir rule curves with a multi-objective Genetic Algorithm (MOGA). The optimised rule curves are assumed to be the reservoir base policy. ENSO standard indices are currently forecasted at monthly time scale with nine-month lead time. These forecasts are used to perform stochastic optimisation of reservoir releases at each monthly time step according to the following procedure: (i) nine-month inflow forecast scenarios are generated using ENSO forecasts; (ii) a MOGA is set up to optimise the upcoming nine monthly releases; (iii) the optimisation is carried out by simulating the releases on the inflow forecasts, and by applying the base policy on a subsequent synthetic inflow scenario in order to account for long-term costs; (iv) the optimised release for the first month is implemented; (v) the state of the system is updated and (i), (ii), (iii), and (iv) are iterated for the following time step. The results highlight the advantages of using a climate-driven stochastic model to produce inflow scenarios and forecasts for reservoir optimisation, showing potential improvements with respect to the current management. Dynamic programming was used to find the best possible release time series given the inflow observations, in order to benchmark any possible operational improvement.

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

PubMed

Bildirici, Melike; Ersin, Özgür Ömer

2018-01-01

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

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

PubMed

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

2012-07-15

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

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

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

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

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

Influence of weather on the synchrony of gypsy moth (Lepidoptera: Lymantriidae) outbreaks in New England

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

Williams, D.W.; Liebhold, A.M.

1995-10-01

Outbreaks of the gypsy moth, Lymantria dispar (L.), were partially synchronous across New England states (Massachusetts, Maine, New Hampshire, and Vermont) from 1938 to 1992. To explain this synchrony, we investigated the Moran effect, a hypothesis that local population oscillations, which result form similar density-dependent mechanisms operating at time lags, may be synchronized over wide areas by exposure to common weather patterns. We also investigated the theory of climatic release, which ostulates that outbreaks are triggered by climatic factors favorable for population growth. Time series analysis revealed defoliation series in 2 states as 1st-order autoregressive processes and the other 2more » as periodic 2nd-order autoregressive processes. Defoliation residuals series computed using the autoregressive models for each state were cross correlated with series of weather variables recorded in the respective states. The weather variables significantly correlated with defoliation residuals in all 4 states were minimum temperature and precipitation in mid-December in the same gypsy moth generation and minimum temperature in mid- to late July of the previous generation. These weather variables also were correlated strongly among the 4 states. The analyses supported the predictions of the Moran effect and suggest the common weather may synchronize local populations so as to produce pest outbreaks over wide areas. We did not find convincing evidence to support the theory of climatic release. 41 refs., 7 figs., 4 tabs.« less

Asymptotically stable phase synchronization revealed by autoregressive circle maps

NASA Astrophysics Data System (ADS)

Drepper, F. R.

2000-11-01

A specially designed of nonlinear time series analysis is introduced based on phases, which are defined as polar angles in spaces spanned by a finite number of delayed coordinates. A canonical choice of the polar axis and a related implicit estimation scheme for the potentially underlying autoregressive circle map (next phase map) guarantee the invertibility of reconstructed phase space trajectories to the original coordinates. The resulting Fourier approximated, invertibility enforcing phase space map allows us to detect conditional asymptotic stability of coupled phases. This comparatively general synchronization criterion unites two existing generalizations of the old concept and can successfully be applied, e.g., to phases obtained from electrocardiogram and airflow recordings characterizing cardiorespiratory interaction.

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

PubMed

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

1981-06-01

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

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

NASA Astrophysics Data System (ADS)

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

2018-03-01

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

Assessing the effects of pharmacological agents on respiratory dynamics using time-series modeling.

PubMed

Wong, Kin Foon Kevin; Gong, Jen J; Cotten, Joseph F; Solt, Ken; Brown, Emery N

2013-04-01

Developing quantitative descriptions of how stimulant and depressant drugs affect the respiratory system is an important focus in medical research. Respiratory variables-respiratory rate, tidal volume, and end tidal carbon dioxide-have prominent temporal dynamics that make it inappropriate to use standard hypothesis-testing methods that assume independent observations to assess the effects of these pharmacological agents. We present a polynomial signal plus autoregressive noise model for analysis of continuously recorded respiratory variables. We use a cyclic descent algorithm to maximize the conditional log likelihood of the parameters and the corrected Akaike's information criterion to choose simultaneously the orders of the polynomial and the autoregressive models. In an analysis of respiratory rates recorded from anesthetized rats before and after administration of the respiratory stimulant methylphenidate, we use the model to construct within-animal z-tests of the drug effect that take account of the time-varying nature of the mean respiratory rate and the serial dependence in rate measurements. We correct for the effect of model lack-of-fit on our inferences by also computing bootstrap confidence intervals for the average difference in respiratory rate pre- and postmethylphenidate treatment. Our time-series modeling quantifies within each animal the substantial increase in mean respiratory rate and respiratory dynamics following methylphenidate administration. This paradigm can be readily adapted to analyze the dynamics of other respiratory variables before and after pharmacologic treatments.

Spatio-temporal wildland arson crime functions

Treesearch

David T. Butry; Jeffrey P. Prestemon

2005-01-01

Wildland arson creates damages to structures and timber and affects the health and safety of people living in rural and wildland urban interface areas. We develop a model that incorporates temporal autocorrelations and spatial correlations in wildland arson ignitions in Florida. A Poisson autoregressive model of order p, or PAR(p)...

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.…

Getting It Right Matters: Climate Spectra and Their Estimation

NASA Astrophysics Data System (ADS)

Privalsky, Victor; Yushkov, Vladislav

2018-06-01

In many recent publications, climate spectra estimated with different methods from observed, GCM-simulated, and reconstructed time series contain many peaks at time scales from a few years to many decades and even centuries. However, respective spectral estimates obtained with the autoregressive (AR) and multitapering (MTM) methods showed that spectra of climate time series are smooth and contain no evidence of periodic or quasi-periodic behavior. Four order selection criteria for the autoregressive models were studied and proven sufficiently reliable for 25 time series of climate observations at individual locations or spatially averaged at local-to-global scales. As time series of climate observations are short, an alternative reliable nonparametric approach is Thomson's MTM. These results agree with both the earlier climate spectral analyses and the Markovian stochastic model of climate.

On The Value at Risk Using Bayesian Mixture Laplace Autoregressive Approach for Modelling the Islamic Stock Risk Investment

NASA Astrophysics Data System (ADS)

Miftahurrohmah, Brina; Iriawan, Nur; Fithriasari, Kartika

2017-06-01

Stocks are known as the financial instruments traded in the capital market which have a high level of risk. Their risks are indicated by their uncertainty of their return which have to be accepted by investors in the future. The higher the risk to be faced, the higher the return would be gained. Therefore, the measurements need to be made against the risk. Value at Risk (VaR) as the most popular risk measurement method, is frequently ignore when the pattern of return is not uni-modal Normal. The calculation of the risks using VaR method with the Normal Mixture Autoregressive (MNAR) approach has been considered. This paper proposes VaR method couple with the Mixture Laplace Autoregressive (MLAR) that would be implemented for analysing the first three biggest capitalization Islamic stock return in JII, namely PT. Astra International Tbk (ASII), PT. Telekomunikasi Indonesia Tbk (TLMK), and PT. Unilever Indonesia Tbk (UNVR). Parameter estimation is performed by employing Bayesian Markov Chain Monte Carlo (MCMC) approaches.

GIS-based analysis and modelling with empirical and remotely-sensed data on coastline advance and retreat

NASA Astrophysics Data System (ADS)

Ahmad, Sajid Rashid

With the understanding that far more research remains to be done on the development and use of innovative and functional geospatial techniques and procedures to investigate coastline changes this thesis focussed on the integration of remote sensing, geographical information systems (GIS) and modelling techniques to provide meaningful insights on the spatial and temporal dynamics of coastline changes. One of the unique strengths of this research was the parameterization of the GIS with long-term empirical and remote sensing data. Annual empirical data from 1941--2007 were analyzed by the GIS, and then modelled with statistical techniques. Data were also extracted from Landsat TM and ETM+ images. The band ratio method was used to extract the coastlines. Topographic maps were also used to extract digital map data. All data incorporated into ArcGIS 9.2 were analyzed with various modules, including Spatial Analyst, 3D Analyst, and Triangulated Irregular Networks. The Digital Shoreline Analysis System was used to analyze and predict rates of coastline change. GIS results showed the spatial locations along the coast that will either advance or retreat over time. The linear regression results highlighted temporal changes which are likely to occur along the coastline. Box-Jenkins modelling procedures were utilized to determine statistical models which best described the time series (1941--2007) of coastline change data. After several iterations and goodness-of-fit tests, second-order spatial cyclic autoregressive models, first-order autoregressive models and autoregressive moving average models were identified as being appropriate for describing the deterministic and random processes operating in Guyana's coastal system. The models highlighted not only cyclical patterns in advance and retreat of the coastline, but also the existence of short and long-term memory processes. Long-term memory processes could be associated with mudshoal propagation and stabilization while short-term memory processes were indicative of transitory hydrodynamic and other processes. An innovative framework for a spatio-temporal information-based system (STIBS) was developed. STIBS incorporated diverse datasets within a GIS, dynamic computer-based simulation models, and a spatial information query and graphical subsystem. Tests of the STIBS proved that it could be used to simulate and visualize temporal variability in shifting morphological states of the coastline.

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.

Measuring Treasury Bond Portfolio Risk and Portfolio Optimization with a Non-Gaussian Multivariate Model

NASA Astrophysics Data System (ADS)

Dong, Yijun

The research about measuring the risk of a bond portfolio and the portfolio optimization was relatively rare previously, because the risk factors of bond portfolios are not very volatile. However, this condition has changed recently. The 2008 financial crisis brought high volatility to the risk factors and the related bond securities, even if the highly rated U.S. treasury bonds. Moreover, the risk factors of bond portfolios show properties of fat-tailness and asymmetry like risk factors of equity portfolios. Therefore, we need to use advanced techniques to measure and manage risk of bond portfolios. In our paper, we first apply autoregressive moving average generalized autoregressive conditional heteroscedasticity (ARMA-GARCH) model with multivariate normal tempered stable (MNTS) distribution innovations to predict risk factors of U.S. treasury bonds and statistically demonstrate that MNTS distribution has the ability to capture the properties of risk factors based on the goodness-of-fit tests. Then based on empirical evidence, we find that the VaR and AVaR estimated by assuming normal tempered stable distribution are more realistic and reliable than those estimated by assuming normal distribution, especially for the financial crisis period. Finally, we use the mean-risk portfolio optimization to minimize portfolios' potential risks. The empirical study indicates that the optimized bond portfolios have better risk-adjusted performances than the benchmark portfolios for some periods. Moreover, the optimized bond portfolios obtained by assuming normal tempered stable distribution have improved performances in comparison to the optimized bond portfolios obtained by assuming normal distribution.

Two dynamic regimes in the human gut microbiome

PubMed Central

Smillie, Chris S.; Alm, Eric J.

2017-01-01

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

Two dynamic regimes in the human gut microbiome.

PubMed

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

2017-02-01

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

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

NASA Astrophysics Data System (ADS)

Tastan, Hüseyin

2006-02-01

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

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

NASA Astrophysics Data System (ADS)

Schlechtingen, Meik; Ferreira Santos, Ilmar

2011-07-01

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

Impact of global financial crisis on precious metals returns: An application of ARCH and GARCH methods

NASA Astrophysics Data System (ADS)

Ismail, Mohd Tahir; Abdullah, Nurul Ain; Abdul Karim, Samsul Ariffin

2013-04-01

This paper is focusing on seeing the resilient of precious metals returns in facing the global financial crisis and provides a new guide for the investors before making investment decisions on precious metals. Four types of precious metals returns which are the variables selected in this study. The precious metals are gold, silver, bronze and platinum. All the variables are transferred to natural logarithm (ln). Daily data over the period 2 January 1995 to 30 December 2011 is used. Unit root tests that involve Augmented Dickey-Fuller (ADF) and Kwiatkowski-Phillips-Schmidt-Shin (KPSS) tests have been employed in determining the stationarity of the variables. Autoregressive Conditional Heteroscedasticity (ARCH) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) methods have been applied in measuring the impact of global financial crisis on precious metals returns. The result shows that investing in platinum is less risky compared to the other precious metals because it is not influence by the crisis period.

On the Feed-back Mechanism of Chinese Stock Markets

NASA Astrophysics Data System (ADS)

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

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

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

PubMed Central

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

2013-01-01

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

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

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

A novel framework to simulating non-stationary, non-linear, non-Normal hydrological time series using Markov Switching Autoregressive Models

NASA Astrophysics Data System (ADS)

Birkel, C.; Paroli, R.; Spezia, L.; Tetzlaff, D.; Soulsby, C.

2012-12-01

In this paper we present a novel model framework using the class of Markov Switching Autoregressive Models (MSARMs) to examine catchments as complex stochastic systems that exhibit non-stationary, non-linear and non-Normal rainfall-runoff and solute dynamics. Hereby, MSARMs are pairs of stochastic processes, one observed and one unobserved, or hidden. We model the unobserved process as a finite state Markov chain and assume that the observed process, given the hidden Markov chain, is conditionally autoregressive, which means that the current observation depends on its recent past (system memory). The model is fully embedded in a Bayesian analysis based on Markov Chain Monte Carlo (MCMC) algorithms for model selection and uncertainty assessment. Hereby, the autoregressive order and the dimension of the hidden Markov chain state-space are essentially self-selected. The hidden states of the Markov chain represent unobserved levels of variability in the observed process that may result from complex interactions of hydroclimatic variability on the one hand and catchment characteristics affecting water and solute storage on the other. To deal with non-stationarity, additional meteorological and hydrological time series along with a periodic component can be included in the MSARMs as covariates. This extension allows identification of potential underlying drivers of temporal rainfall-runoff and solute dynamics. We applied the MSAR model framework to streamflow and conservative tracer (deuterium and oxygen-18) time series from an intensively monitored 2.3 km2 experimental catchment in eastern Scotland. Statistical time series analysis, in the form of MSARMs, suggested that the streamflow and isotope tracer time series are not controlled by simple linear rules. MSARMs showed that the dependence of current observations on past inputs observed by transport models often in form of the long-tailing of travel time and residence time distributions can be efficiently explained by non-stationarity either of the system input (climatic variability) and/or the complexity of catchment storage characteristics. The statistical model is also capable of reproducing short (event) and longer-term (inter-event) and wet and dry dynamical "hydrological states". These reflect the non-linear transport mechanisms of flow pathways induced by transient climatic and hydrological variables and modified by catchment characteristics. We conclude that MSARMs are a powerful tool to analyze the temporal dynamics of hydrological data, allowing for explicit integration of non-stationary, non-linear and non-Normal characteristics.

A conditional Granger causality model approach for group analysis in functional MRI

PubMed Central

Zhou, Zhenyu; Wang, Xunheng; Klahr, Nelson J.; Liu, Wei; Arias, Diana; Liu, Hongzhi; von Deneen, Karen M.; Wen, Ying; Lu, Zuhong; Xu, Dongrong; Liu, Yijun

2011-01-01

Granger causality model (GCM) derived from multivariate vector autoregressive models of data has been employed for identifying effective connectivity in the human brain with functional MR imaging (fMRI) and to reveal complex temporal and spatial dynamics underlying a variety of cognitive processes. In the most recent fMRI effective connectivity measures, pairwise GCM has commonly been applied based on single voxel values or average values from special brain areas at the group level. Although a few novel conditional GCM methods have been proposed to quantify the connections between brain areas, our study is the first to propose a viable standardized approach for group analysis of an fMRI data with GCM. To compare the effectiveness of our approach with traditional pairwise GCM models, we applied a well-established conditional GCM to pre-selected time series of brain regions resulting from general linear model (GLM) and group spatial kernel independent component analysis (ICA) of an fMRI dataset in the temporal domain. Datasets consisting of one task-related and one resting-state fMRI were used to investigate connections among brain areas with the conditional GCM method. With the GLM detected brain activation regions in the emotion related cortex during the block design paradigm, the conditional GCM method was proposed to study the causality of the habituation between the left amygdala and pregenual cingulate cortex during emotion processing. For the resting-state dataset, it is possible to calculate not only the effective connectivity between networks but also the heterogeneity within a single network. Our results have further shown a particular interacting pattern of default mode network (DMN) that can be characterized as both afferent and efferent influences on the medial prefrontal cortex (mPFC) and posterior cingulate cortex (PCC). These results suggest that the conditional GCM approach based on a linear multivariate vector autoregressive (MVAR) model can achieve greater accuracy in detecting network connectivity than the widely used pairwise GCM, and this group analysis methodology can be quite useful to extend the information obtainable in fMRI. PMID:21232892

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.

Influence of psychotherapist density and antidepressant sales on suicide rates.

PubMed

Kapusta, N D; Niederkrotenthaler, T; Etzersdorfer, E; Voracek, M; Dervic, K; Jandl-Jager, E; Sonneck, G

2009-03-01

Antidepressant sales and suicide rates have been shown to be correlated in industrialized countries. The aim was to study the possible effects of psychotherapy utilization on suicide rates. We assessed the impact of antidepressant sales and psychotherapist density on suicide rates between 1991 and 2005. To adjust for serial correlation in time series, three first-order autoregressive models adjusted for per capita alcohol consumption and unemployment rates were employed. Antidepressant sales and the density of psychotherapists in the population were negatively associated with suicide rates. This study provides evidence that decreasing suicide rates were associated with both increasing antidepressant sales and an increasing density of psychotherapists. The decrease of suicide rates could reflect a general improvement in mental health care rather than being caused by antidepressant sales or psychotherapist density alone.

Zero-crossing statistics for non-Markovian time series.

PubMed

Nyberg, Markus; Lizana, Ludvig; Ambjörnsson, Tobias

2018-03-01

In applications spanning from image analysis and speech recognition to energy dissipation in turbulence and time-to failure of fatigued materials, researchers and engineers want to calculate how often a stochastic observable crosses a specific level, such as zero. At first glance this problem looks simple, but it is in fact theoretically very challenging, and therefore few exact results exist. One exception is the celebrated Rice formula that gives the mean number of zero crossings in a fixed time interval of a zero-mean Gaussian stationary process. In this study we use the so-called independent interval approximation to go beyond Rice's result and derive analytic expressions for all higher-order zero-crossing cumulants and moments. Our results agree well with simulations for the non-Markovian autoregressive model.

On the continuity of the stationary state distribution of DPCM

NASA Astrophysics Data System (ADS)

Naraghi-Pour, Morteza; Neuhoff, David L.

1990-03-01

Continuity and singularity properties of the stationary state distribution of differential pulse code modulation (DPCM) are explored. Two-level DPCM (i.e., delta modulation) operating on a first-order autoregressive source is considered, and it is shown that, when the magnitude of the DPCM prediciton coefficient is between zero and one-half, the stationary state distribution is singularly continuous; i.e., it is not discrete but concentrates on an uncountable set with a Lebesgue measure of zero. Consequently, it cannot be represented with a probability density function. For prediction coefficients with magnitude greater than or equal to one-half, the distribution is pure, i.e., either absolutely continuous and representable with a density function, or singular. This problem is compared to the well-known and still substantially unsolved problem of symmetric Bernoulli convolutions.

Zero-crossing statistics for non-Markovian time series

NASA Astrophysics Data System (ADS)

Nyberg, Markus; Lizana, Ludvig; Ambjörnsson, Tobias

2018-03-01

In applications spanning from image analysis and speech recognition to energy dissipation in turbulence and time-to failure of fatigued materials, researchers and engineers want to calculate how often a stochastic observable crosses a specific level, such as zero. At first glance this problem looks simple, but it is in fact theoretically very challenging, and therefore few exact results exist. One exception is the celebrated Rice formula that gives the mean number of zero crossings in a fixed time interval of a zero-mean Gaussian stationary process. In this study we use the so-called independent interval approximation to go beyond Rice's result and derive analytic expressions for all higher-order zero-crossing cumulants and moments. Our results agree well with simulations for the non-Markovian autoregressive model.

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

PubMed

Asumadu-Sarkodie, Samuel; Owusu, Phebe Asantewaa

2016-06-01

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

A self-adaption compensation control for hysteresis nonlinearity in piezo-actuated stages based on Pi-sigma fuzzy neural network

NASA Astrophysics Data System (ADS)

Xu, Rui; Zhou, Miaolei

2018-04-01

Piezo-actuated stages are widely applied in the high-precision positioning field nowadays. However, the inherent hysteresis nonlinearity in piezo-actuated stages greatly deteriorates the positioning accuracy of piezo-actuated stages. This paper first utilizes a nonlinear autoregressive moving average with exogenous inputs (NARMAX) model based on the Pi-sigma fuzzy neural network (PSFNN) to construct an online rate-dependent hysteresis model for describing the hysteresis nonlinearity in piezo-actuated stages. In order to improve the convergence rate of PSFNN and modeling precision, we adopt the gradient descent algorithm featuring three different learning factors to update the model parameters. The convergence of the NARMAX model based on the PSFNN is analyzed effectively. To ensure that the parameters can converge to the true values, the persistent excitation condition is considered. Then, a self-adaption compensation controller is designed for eliminating the hysteresis nonlinearity in piezo-actuated stages. A merit of the proposed controller is that it can directly eliminate the complex hysteresis nonlinearity in piezo-actuated stages without any inverse dynamic models. To demonstrate the effectiveness of the proposed model and control methods, a set of comparative experiments are performed on piezo-actuated stages. Experimental results show that the proposed modeling and control methods have excellent performance.

Incorporating measurement error in n = 1 psychological autoregressive modeling.

PubMed

Schuurman, Noémi K; Houtveen, Jan H; Hamaker, Ellen L

2015-01-01

Measurement error is omnipresent in psychological data. However, the vast majority of applications of autoregressive time series analyses in psychology do not take measurement error into account. Disregarding measurement error when it is present in the data results in a bias of the autoregressive parameters. We discuss two models that take measurement error into account: An autoregressive model with a white noise term (AR+WN), and an autoregressive moving average (ARMA) model. In a simulation study we compare the parameter recovery performance of these models, and compare this performance for both a Bayesian and frequentist approach. We find that overall, the AR+WN model performs better. Furthermore, we find that for realistic (i.e., small) sample sizes, psychological research would benefit from a Bayesian approach in fitting these models. Finally, we illustrate the effect of disregarding measurement error in an AR(1) model by means of an empirical application on mood data in women. We find that, depending on the person, approximately 30-50% of the total variance was due to measurement error, and that disregarding this measurement error results in a substantial underestimation of the autoregressive parameters.

Local Linear Regression for Data with AR Errors.

PubMed

Li, Runze; Li, Yan

2009-07-01

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

Multivariate Autoregressive Modeling and Granger Causality Analysis of Multiple Spike Trains

PubMed Central

Krumin, Michael; Shoham, Shy

2010-01-01

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

Get Over It! A Multilevel Threshold Autoregressive Model for State-Dependent Affect Regulation.

PubMed

De Haan-Rietdijk, Silvia; Gottman, John M; Bergeman, Cindy S; Hamaker, Ellen L

2016-03-01

Intensive longitudinal data provide rich information, which is best captured when specialized models are used in the analysis. One of these models is the multilevel autoregressive model, which psychologists have applied successfully to study affect regulation as well as alcohol use. A limitation of this model is that the autoregressive parameter is treated as a fixed, trait-like property of a person. We argue that the autoregressive parameter may be state-dependent, for example, if the strength of affect regulation depends on the intensity of affect experienced. To allow such intra-individual variation, we propose a multilevel threshold autoregressive model. Using simulations, we show that this model can be used to detect state-dependent regulation with adequate power and Type I error. The potential of the new modeling approach is illustrated with two empirical applications that extend the basic model to address additional substantive research questions.

Robust Semi-Active Ride Control under Stochastic Excitation

DTIC Science & Technology

2014-01-01

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

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

NASA Technical Reports Server (NTRS)

Larsen, Curtis E.

1988-01-01

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

Incorporating measurement error in n = 1 psychological autoregressive modeling

PubMed Central

Schuurman, Noémi K.; Houtveen, Jan H.; Hamaker, Ellen L.

2015-01-01

Measurement error is omnipresent in psychological data. However, the vast majority of applications of autoregressive time series analyses in psychology do not take measurement error into account. Disregarding measurement error when it is present in the data results in a bias of the autoregressive parameters. We discuss two models that take measurement error into account: An autoregressive model with a white noise term (AR+WN), and an autoregressive moving average (ARMA) model. In a simulation study we compare the parameter recovery performance of these models, and compare this performance for both a Bayesian and frequentist approach. We find that overall, the AR+WN model performs better. Furthermore, we find that for realistic (i.e., small) sample sizes, psychological research would benefit from a Bayesian approach in fitting these models. Finally, we illustrate the effect of disregarding measurement error in an AR(1) model by means of an empirical application on mood data in women. We find that, depending on the person, approximately 30–50% of the total variance was due to measurement error, and that disregarding this measurement error results in a substantial underestimation of the autoregressive parameters. PMID:26283988

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

NASA Astrophysics Data System (ADS)

Zou, Yiqun; Tang, Xiafei

2014-06-01

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

Dynamic RSA: Examining parasympathetic regulatory dynamics via vector-autoregressive modeling of time-varying RSA and heart period.

PubMed

Fisher, Aaron J; Reeves, Jonathan W; Chi, Cyrus

2016-07-01

Expanding on recently published methods, the current study presents an approach to estimating the dynamic, regulatory effect of the parasympathetic nervous system on heart period on a moment-to-moment basis. We estimated second-to-second variation in respiratory sinus arrhythmia (RSA) in order to estimate the contemporaneous and time-lagged relationships among RSA, interbeat interval (IBI), and respiration rate via vector autoregression. Moreover, we modeled these relationships at lags of 1 s to 10 s, in order to evaluate the optimal latency for estimating dynamic RSA effects. The IBI (t) on RSA (t-n) regression parameter was extracted from individual models as an operationalization of the regulatory effect of RSA on IBI-referred to as dynamic RSA (dRSA). Dynamic RSA positively correlated with standard averages of heart rate and negatively correlated with standard averages of RSA. We propose that dRSA reflects the active downregulation of heart period by the parasympathetic nervous system and thus represents a novel metric that provides incremental validity in the measurement of autonomic cardiac control-specifically, a method by which parasympathetic regulatory effects can be measured in process. © 2016 Society for Psychophysiological Research.

The Autoregressive Method: A Method of Approximating and Estimating Positive Functions

DTIC Science & Technology

1976-08-01

in drawing the curves, thanks to computer graphics. A few people ha’ very imaginatively pro- posed - td developed new ways of visualizing the data...k= -= it turns out that , , 0ki < 0 is a sufficient condition for all our k= -cc ( operations to be valid. Ii_ _ _ _ _ _ __ __ _ _ _ _ -106- We will

Circular Conditional Autoregressive Modeling of Vector Fields.

PubMed

Modlin, Danny; Fuentes, Montse; Reich, Brian

2012-02-01

As hurricanes approach landfall, there are several hazards for which coastal populations must be prepared. Damaging winds, torrential rains, and tornadoes play havoc with both the coast and inland areas; but, the biggest seaside menace to life and property is the storm surge. Wind fields are used as the primary forcing for the numerical forecasts of the coastal ocean response to hurricane force winds, such as the height of the storm surge and the degree of coastal flooding. Unfortunately, developments in deterministic modeling of these forcings have been hindered by computational expenses. In this paper, we present a multivariate spatial model for vector fields, that we apply to hurricane winds. We parameterize the wind vector at each site in polar coordinates and specify a circular conditional autoregressive (CCAR) model for the vector direction, and a spatial CAR model for speed. We apply our framework for vector fields to hurricane surface wind fields for Hurricane Floyd of 1999 and compare our CCAR model to prior methods that decompose wind speed and direction into its N-S and W-E cardinal components.

Circular Conditional Autoregressive Modeling of Vector Fields*

PubMed Central

Modlin, Danny; Fuentes, Montse; Reich, Brian

2013-01-01

As hurricanes approach landfall, there are several hazards for which coastal populations must be prepared. Damaging winds, torrential rains, and tornadoes play havoc with both the coast and inland areas; but, the biggest seaside menace to life and property is the storm surge. Wind fields are used as the primary forcing for the numerical forecasts of the coastal ocean response to hurricane force winds, such as the height of the storm surge and the degree of coastal flooding. Unfortunately, developments in deterministic modeling of these forcings have been hindered by computational expenses. In this paper, we present a multivariate spatial model for vector fields, that we apply to hurricane winds. We parameterize the wind vector at each site in polar coordinates and specify a circular conditional autoregressive (CCAR) model for the vector direction, and a spatial CAR model for speed. We apply our framework for vector fields to hurricane surface wind fields for Hurricane Floyd of 1999 and compare our CCAR model to prior methods that decompose wind speed and direction into its N-S and W-E cardinal components. PMID:24353452

Time Series Modeling of Nano-Gold Immunochromatographic Assay via Expectation Maximization Algorithm.

PubMed

Zeng, Nianyin; Wang, Zidong; Li, Yurong; Du, Min; Cao, Jie; Liu, Xiaohui

2013-12-01

In this paper, the expectation maximization (EM) algorithm is applied to the modeling of the nano-gold immunochromatographic assay (nano-GICA) via available time series of the measured signal intensities of the test and control lines. The model for the nano-GICA is developed as the stochastic dynamic model that consists of a first-order autoregressive stochastic dynamic process and a noisy measurement. By using the EM algorithm, the model parameters, the actual signal intensities of the test and control lines, as well as the noise intensity can be identified simultaneously. Three different time series data sets concerning the target concentrations are employed to demonstrate the effectiveness of the introduced algorithm. Several indices are also proposed to evaluate the inferred models. It is shown that the model fits the data very well.

Principal Dynamic Mode Analysis of the Hodgkin–Huxley Equations

PubMed Central

Eikenberry, Steffen E.; Marmarelis, Vasilis Z.

2015-01-01

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

A generalized conditional heteroscedastic model for temperature downscaling

NASA Astrophysics Data System (ADS)

Modarres, R.; Ouarda, T. B. M. J.

2014-11-01

This study describes a method for deriving the time varying second order moment, or heteroscedasticity, of local daily temperature and its association to large Coupled Canadian General Circulation Models predictors. This is carried out by applying a multivariate generalized autoregressive conditional heteroscedasticity (MGARCH) approach to construct the conditional variance-covariance structure between General Circulation Models (GCMs) predictors and maximum and minimum temperature time series during 1980-2000. Two MGARCH specifications namely diagonal VECH and dynamic conditional correlation (DCC) are applied and 25 GCM predictors were selected for a bivariate temperature heteroscedastic modeling. It is observed that the conditional covariance between predictors and temperature is not very strong and mostly depends on the interaction between the random process governing temporal variation of predictors and predictants. The DCC model reveals a time varying conditional correlation between GCM predictors and temperature time series. No remarkable increasing or decreasing change is observed for correlation coefficients between GCM predictors and observed temperature during 1980-2000 while weak winter-summer seasonality is clear for both conditional covariance and correlation. Furthermore, the stationarity and nonlinearity Kwiatkowski-Phillips-Schmidt-Shin (KPSS) and Brock-Dechert-Scheinkman (BDS) tests showed that GCM predictors, temperature and their conditional correlation time series are nonlinear but stationary during 1980-2000 according to BDS and KPSS test results. However, the degree of nonlinearity of temperature time series is higher than most of the GCM predictors.

Asymmetric conditional volatility in international stock markets

NASA Astrophysics Data System (ADS)

Ferreira, Nuno B.; Menezes, Rui; Mendes, Diana A.

2007-08-01

Recent studies show that a negative shock in stock prices will generate more volatility than a positive shock of similar magnitude. The aim of this paper is to appraise the hypothesis under which the conditional mean and the conditional variance of stock returns are asymmetric functions of past information. We compare the results for the Portuguese Stock Market Index PSI 20 with six other Stock Market Indices, namely the SP 500, FTSE 100, DAX 30, CAC 40, ASE 20, and IBEX 35. In order to assess asymmetric volatility we use autoregressive conditional heteroskedasticity specifications known as TARCH and EGARCH. We also test for asymmetry after controlling for the effect of macroeconomic factors on stock market returns using TAR and M-TAR specifications within a VAR framework. Our results show that the conditional variance is an asymmetric function of past innovations raising proportionately more during market declines, a phenomenon known as the leverage effect. However, when we control for the effect of changes in macroeconomic variables, we find no significant evidence of asymmetric behaviour of the stock market returns. There are some signs that the Portuguese Stock Market tends to show somewhat less market efficiency than other markets since the effect of the shocks appear to take a longer time to dissipate.

Kepler AutoRegressive Planet Search

NASA Astrophysics Data System (ADS)

Feigelson, Eric

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

Development of First-Graders' Word Reading Skills: For Whom Can Dynamic Assessment Tell Us More?

PubMed

Cho, Eunsoo; Compton, Donald L; Gilbert, Jennifer K; Steacy, Laura M; Collins, Alyson A; Lindström, Esther R

2017-01-01

Dynamic assessment (DA) of word reading measures learning potential for early reading development by documenting the amount of assistance needed to learn how to read words with unfamiliar orthography. We examined the additive value of DA for predicting first-grade decoding and word recognition development while controlling for autoregressive effects. Additionally, we examined whether predictive validity of DA would be higher for students who have poor phonological awareness skills. First-grade students (n = 105) were assessed on measures of word reading, phonological awareness, rapid automatized naming, and DA in the fall and again assessed on word reading measures in the spring. A series of planned, moderated multiple regression analyses indicated that DA made a significant and unique contribution in predicting word recognition development above and beyond the autoregressor, particularly for students with poor phonological awareness skills. For these students, DA explained 3.5% of the unique variance in end-of-first-grade word recognition that was not attributable to autoregressive effect. Results suggest that DA provides an important source of individual differences in the development of word recognition skills that cannot be fully captured by merely assessing the present level of reading skills through traditional static assessment, particularly for students at risk for developing reading disabilities. © Hammill Institute on Disabilities 2015.

The role of sleep disturbances in the longitudinal relationship between psychosocial working conditions, measured by work demands and support, and depression.

PubMed

Magnusson Hanson, Linda L; Chungkham, Holendro Singh; Åkerstedt, Torbjörn; Westerlund, Hugo

2014-12-01

Because work demands and lack of social support seem to be prospectively linked to sleep problems, and sleep problems are linked to depression, sleep problems may play a role in the relationship between these work characteristics and depressive symptoms. In order to shed more light on this relationship, the current study investigated whether disturbed sleep is a mediator in the longitudinal relationships between work demands, social support, and depression. Longitudinal cohort study with repeated survey measures on four occasions. Swedish workforce. 2,017 working participants from the Swedish Longitudinal Occupational Survey of Health in 2006, 2008, 2010, and 2012. Work demands (four items) and social support (six items) were assessed with the Demand Control Questionnaire, disturbed sleep (four items) with the Karolinska Sleep Questionnaire, and depressive symptoms with a brief subscale (six items) from the Symptom Checklist. Autoregressive longitudinal mediation models using structural equation modeling were tested. The work characteristics, and disturbed sleep, were found to be separately associated with depressive symptoms in subsequent waves. However, only demands were found to be longitudinally related to subsequent disturbed sleep. The longitudinal autoregressive models supported a weak mediating role of disturbed sleep in the relationship between demands and depressive symptoms (standardized beta 0.008, P < 0.001), but not between support and depressive symptoms. These results indicate that higher demands at work might cause an increase in depressive symptoms, in part, by increasing disturbed sleep, although the mediated effect was relatively small compared to the total effect. © 2014 Associated Professional Sleep Societies, LLC.

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

PubMed

Hamaker, Ellen L; Grasman, Raoul P P P

2014-01-01

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

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

PubMed Central

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

2015-01-01

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

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

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

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

Hoon Sohn; Charles Farrar; Norman Hunter

2001-01-01

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

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

NASA Astrophysics Data System (ADS)

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

2008-02-01

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

Forecasting intentional wildfires using temporal and spatiotemporal autocorrelations

Treesearch

Jeffrey P. Prestemon; María L. Chas-Amil; Julia M. Touza; Scott L. Goodrick

2012-01-01

We report daily time series models containing both temporal and spatiotemporal lags, which are applied to forecasting intentional wildfires in Galicia, Spain. Models are estimated independently for each of the 19 forest districts in Galicia using a 1999–2003 training dataset and evaluated out-of-sample with a 2004–06 dataset. Poisson autoregressive models of order P –...

The Longitudinal Interplay of Adolescents' Self-Esteem and Body Image: A Conditional Autoregressive Latent Trajectory Analysis

ERIC Educational Resources Information Center

Morin, Alexandre J. S.; Maiano, Christophe; Marsh, Herbert W.; Janosz, Michel; Nagengast, Benjamin

2011-01-01

Self-esteem and body image are central to coping successfully with the developmental challenges of adolescence. However, the current knowledge surrounding self-esteem and body image is fraught with controversy. This study attempts to clarify some of them by addressing three questions: (1) Are the intraindividual developmental trajectories of…

Mean-variance portfolio optimization by using time series approaches based on logarithmic utility function

NASA Astrophysics Data System (ADS)

Soeryana, E.; Fadhlina, N.; Sukono; Rusyaman, E.; Supian, S.

2017-01-01

Investments in stocks investors are also faced with the issue of risk, due to daily price of stock also fluctuate. For minimize the level of risk, investors usually forming an investment portfolio. Establishment of a portfolio consisting of several stocks are intended to get the optimal composition of the investment portfolio. This paper discussed about optimizing investment portfolio of Mean-Variance to stocks by using mean and volatility is not constant based on logarithmic utility function. Non constant mean analysed using models Autoregressive Moving Average (ARMA), while non constant volatility models are analysed using the Generalized Autoregressive Conditional heteroscedastic (GARCH). Optimization process is performed by using the Lagrangian multiplier technique. As a numerical illustration, the method is used to analyse some Islamic stocks in Indonesia. The expected result is to get the proportion of investment in each Islamic stock analysed.

Mean-Variance portfolio optimization by using non constant mean and volatility based on the negative exponential utility function

NASA Astrophysics Data System (ADS)

Soeryana, Endang; Halim, Nurfadhlina Bt Abdul; Sukono, Rusyaman, Endang; Supian, Sudradjat

2017-03-01

Investments in stocks investors are also faced with the issue of risk, due to daily price of stock also fluctuate. For minimize the level of risk, investors usually forming an investment portfolio. Establishment of a portfolio consisting of several stocks are intended to get the optimal composition of the investment portfolio. This paper discussed about optimizing investment portfolio of Mean-Variance to stocks by using mean and volatility is not constant based on the Negative Exponential Utility Function. Non constant mean analyzed using models Autoregressive Moving Average (ARMA), while non constant volatility models are analyzed using the Generalized Autoregressive Conditional heteroscedastic (GARCH). Optimization process is performed by using the Lagrangian multiplier technique. As a numerical illustration, the method is used to analyze some stocks in Indonesia. The expected result is to get the proportion of investment in each stock analyzed

Directionality volatility in electroencephalogram time series

NASA Astrophysics Data System (ADS)

Mansor, Mahayaudin M.; Green, David A.; Metcalfe, Andrew V.

2016-06-01

We compare time series of electroencephalograms (EEGs) from healthy volunteers with EEGs from subjects diagnosed with epilepsy. The EEG time series from the healthy group are recorded during awake state with their eyes open and eyes closed, and the records from subjects with epilepsy are taken from three different recording regions of pre-surgical diagnosis: hippocampal, epileptogenic and seizure zone. The comparisons for these 5 categories are in terms of deviations from linear time series models with constant variance Gaussian white noise error inputs. One feature investigated is directionality, and how this can be modelled by either non-linear threshold autoregressive models or non-Gaussian errors. A second feature is volatility, which is modelled by Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) processes. Other features include the proportion of variability accounted for by time series models, and the skewness and the kurtosis of the residuals. The results suggest these comparisons may have diagnostic potential for epilepsy and provide early warning of seizures.

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.

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

PubMed

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

2014-10-01

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

Comparisons of Four Methods for Estimating a Dynamic Factor Model

ERIC Educational Resources Information Center

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

2008-01-01

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

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%.

Model-Based Clustering of Regression Time Series Data via APECM -- An AECM Algorithm Sung to an Even Faster Beat

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

Chen, Wei-Chen; Maitra, Ranjan

2011-01-01

We propose a model-based approach for clustering time series regression data in an unsupervised machine learning framework to identify groups under the assumption that each mixture component follows a Gaussian autoregressive regression model of order p. Given the number of groups, the traditional maximum likelihood approach of estimating the parameters using the expectation-maximization (EM) algorithm can be employed, although it is computationally demanding. The somewhat fast tune to the EM folk song provided by the Alternating Expectation Conditional Maximization (AECM) algorithm can alleviate the problem to some extent. In this article, we develop an alternative partial expectation conditional maximization algorithmmore » (APECM) that uses an additional data augmentation storage step to efficiently implement AECM for finite mixture models. Results on our simulation experiments show improved performance in both fewer numbers of iterations and computation time. The methodology is applied to the problem of clustering mutual funds data on the basis of their average annual per cent returns and in the presence of economic indicators.« less

An empirical comparison of SPM preprocessing parameters to the analysis of fMRI data.

PubMed

Della-Maggiore, Valeria; Chau, Wilkin; Peres-Neto, Pedro R; McIntosh, Anthony R

2002-09-01

We present the results from two sets of Monte Carlo simulations aimed at evaluating the robustness of some preprocessing parameters of SPM99 for the analysis of functional magnetic resonance imaging (fMRI). Statistical robustness was estimated by implementing parametric and nonparametric simulation approaches based on the images obtained from an event-related fMRI experiment. Simulated datasets were tested for combinations of the following parameters: basis function, global scaling, low-pass filter, high-pass filter and autoregressive modeling of serial autocorrelation. Based on single-subject SPM analysis, we derived the following conclusions that may serve as a guide for initial analysis of fMRI data using SPM99: (1) The canonical hemodynamic response function is a more reliable basis function to model the fMRI time series than HRF with time derivative. (2) Global scaling should be avoided since it may significantly decrease the power depending on the experimental design. (3) The use of a high-pass filter may be beneficial for event-related designs with fixed interstimulus intervals. (4) When dealing with fMRI time series with short interstimulus intervals (<8 s), the use of first-order autoregressive model is recommended over a low-pass filter (HRF) because it reduces the risk of inferential bias while providing a relatively good power. For datasets with interstimulus intervals longer than 8 seconds, temporal smoothing is not recommended since it decreases power. While the generalizability of our results may be limited, the methods we employed can be easily implemented by other scientists to determine the best parameter combination to analyze their data.

Modeling rainfall-runoff relationship using multivariate GARCH model

NASA Astrophysics Data System (ADS)

Modarres, R.; Ouarda, T. B. M. J.

2013-08-01

The traditional hydrologic time series approaches are used for modeling, simulating and forecasting conditional mean of hydrologic variables but neglect their time varying variance or the second order moment. This paper introduces the multivariate Generalized Autoregressive Conditional Heteroscedasticity (MGARCH) modeling approach to show how the variance-covariance relationship between hydrologic variables varies in time. These approaches are also useful to estimate the dynamic conditional correlation between hydrologic variables. To illustrate the novelty and usefulness of MGARCH models in hydrology, two major types of MGARCH models, the bivariate diagonal VECH and constant conditional correlation (CCC) models are applied to show the variance-covariance structure and cdynamic correlation in a rainfall-runoff process. The bivariate diagonal VECH-GARCH(1,1) and CCC-GARCH(1,1) models indicated both short-run and long-run persistency in the conditional variance-covariance matrix of the rainfall-runoff process. The conditional variance of rainfall appears to have a stronger persistency, especially long-run persistency, than the conditional variance of streamflow which shows a short-lived drastic increasing pattern and a stronger short-run persistency. The conditional covariance and conditional correlation coefficients have different features for each bivariate rainfall-runoff process with different degrees of stationarity and dynamic nonlinearity. The spatial and temporal pattern of variance-covariance features may reflect the signature of different physical and hydrological variables such as drainage area, topography, soil moisture and ground water fluctuations on the strength, stationarity and nonlinearity of the conditional variance-covariance for a rainfall-runoff process.

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.

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

PubMed

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

2016-01-01

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

Image interpolation by adaptive 2-D autoregressive modeling and soft-decision estimation.

PubMed

Zhang, Xiangjun; Wu, Xiaolin

2008-06-01

The challenge of image interpolation is to preserve spatial details. We propose a soft-decision interpolation technique that estimates missing pixels in groups rather than one at a time. The new technique learns and adapts to varying scene structures using a 2-D piecewise autoregressive model. The model parameters are estimated in a moving window in the input low-resolution image. The pixel structure dictated by the learnt model is enforced by the soft-decision estimation process onto a block of pixels, including both observed and estimated. The result is equivalent to that of a high-order adaptive nonseparable 2-D interpolation filter. This new image interpolation approach preserves spatial coherence of interpolated images better than the existing methods, and it produces the best results so far over a wide range of scenes in both PSNR measure and subjective visual quality. Edges and textures are well preserved, and common interpolation artifacts (blurring, ringing, jaggies, zippering, etc.) are greatly reduced.

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.

Automatic load forecasting. Final report

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

Nelson, D.J.; Vemuri, S.

A method which lends itself to on-line forecasting of hourly electric loads is presented and the results of its use are compared to models developed using the Box-Jenkins method. The method consists of processing the historical hourly loads with a sequential least-squares estimator to identify a finite order autoregressive model which in turn is used to obtain a parsimonious autoregressive-moving average model. A procedure is also defined for incorporating temperature as a variable to improve forecasts where loads are temperature dependent. The method presented has several advantages in comparison to the Box-Jenkins method including much less human intervention and improvedmore » model identification. The method has been tested using three-hourly data from the Lincoln Electric System, Lincoln, Nebraska. In the exhaustive analyses performed on this data base this method produced significantly better results than the Box-Jenkins method. The method also proved to be more robust in that greater confidence could be placed in the accuracy of models based upon the various measures available at the identification stage.« less

Comparing lagged linear correlation, lagged regression, Granger causality, and vector autoregression for uncovering associations in EHR data.

PubMed

Levine, Matthew E; Albers, David J; Hripcsak, George

2016-01-01

Time series analysis methods have been shown to reveal clinical and biological associations in data collected in the electronic health record. We wish to develop reliable high-throughput methods for identifying adverse drug effects that are easy to implement and produce readily interpretable results. To move toward this goal, we used univariate and multivariate lagged regression models to investigate associations between twenty pairs of drug orders and laboratory measurements. Multivariate lagged regression models exhibited higher sensitivity and specificity than univariate lagged regression in the 20 examples, and incorporating autoregressive terms for labs and drugs produced more robust signals in cases of known associations among the 20 example pairings. Moreover, including inpatient admission terms in the model attenuated the signals for some cases of unlikely associations, demonstrating how multivariate lagged regression models' explicit handling of context-based variables can provide a simple way to probe for health-care processes that confound analyses of EHR data.

Nonparametric Bayesian Segmentation of a Multivariate Inhomogeneous Space-Time Poisson Process.

PubMed

Ding, Mingtao; He, Lihan; Dunson, David; Carin, Lawrence

2012-12-01

A nonparametric Bayesian model is proposed for segmenting time-evolving multivariate spatial point process data. An inhomogeneous Poisson process is assumed, with a logistic stick-breaking process (LSBP) used to encourage piecewise-constant spatial Poisson intensities. The LSBP explicitly favors spatially contiguous segments, and infers the number of segments based on the observed data. The temporal dynamics of the segmentation and of the Poisson intensities are modeled with exponential correlation in time, implemented in the form of a first-order autoregressive model for uniformly sampled discrete data, and via a Gaussian process with an exponential kernel for general temporal sampling. We consider and compare two different inference techniques: a Markov chain Monte Carlo sampler, which has relatively high computational complexity; and an approximate and efficient variational Bayesian analysis. The model is demonstrated with a simulated example and a real example of space-time crime events in Cincinnati, Ohio, USA.

Combined non-parametric and parametric approach for identification of time-variant systems

NASA Astrophysics Data System (ADS)

Dziedziech, Kajetan; Czop, Piotr; Staszewski, Wieslaw J.; Uhl, Tadeusz

2018-03-01

Identification of systems, structures and machines with variable physical parameters is a challenging task especially when time-varying vibration modes are involved. The paper proposes a new combined, two-step - i.e. non-parametric and parametric - modelling approach in order to determine time-varying vibration modes based on input-output measurements. Single-degree-of-freedom (SDOF) vibration modes from multi-degree-of-freedom (MDOF) non-parametric system representation are extracted in the first step with the use of time-frequency wavelet-based filters. The second step involves time-varying parametric representation of extracted modes with the use of recursive linear autoregressive-moving-average with exogenous inputs (ARMAX) models. The combined approach is demonstrated using system identification analysis based on the experimental mass-varying MDOF frame-like structure subjected to random excitation. The results show that the proposed combined method correctly captures the dynamics of the analysed structure, using minimum a priori information on the model.

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.

Recurrence plots of discrete-time Gaussian stochastic processes

NASA Astrophysics Data System (ADS)

Ramdani, Sofiane; Bouchara, Frédéric; Lagarde, Julien; Lesne, Annick

2016-09-01

We investigate the statistical properties of recurrence plots (RPs) of data generated by discrete-time stationary Gaussian random processes. We analytically derive the theoretical values of the probabilities of occurrence of recurrence points and consecutive recurrence points forming diagonals in the RP, with an embedding dimension equal to 1. These results allow us to obtain theoretical values of three measures: (i) the recurrence rate (REC) (ii) the percent determinism (DET) and (iii) RP-based estimation of the ε-entropy κ(ε) in the sense of correlation entropy. We apply these results to two Gaussian processes, namely first order autoregressive processes and fractional Gaussian noise. For these processes, we simulate a number of realizations and compare the RP-based estimations of the three selected measures to their theoretical values. These comparisons provide useful information on the quality of the estimations, such as the minimum required data length and threshold radius used to construct the RP.

A low free-parameter stochastic model of daily Forbush decrease indices

NASA Astrophysics Data System (ADS)

Patra, Sankar Narayan; Bhattacharya, Gautam; Panja, Subhash Chandra; Ghosh, Koushik

2014-01-01

Forbush decrease is a rapid decrease in the observed galactic cosmic ray intensity pattern occurring after a coronal mass ejection. In the present paper we have analyzed the daily Forbush decrease indices from January, 1967 to December, 2003 generated in IZMIRAN, Russia. First the entire indices have been smoothened and next we have made an attempt to fit a suitable stochastic model for the present time series by means of a necessary number of process parameters. The study reveals that the present time series is governed by a stationary autoregressive process of order 2 with a trace of white noise. Under the consideration of the present model we have shown that chaos is not expected in the present time series which opens up the possibility of validation of its forecasting (both short-term and long-term) as well as its multi-periodic behavior.

Singular Spectrum Analysis for Astronomical Time Series: Constructing a Parsimonious Hypothesis Test

NASA Astrophysics Data System (ADS)

Greco, G.; Kondrashov, D.; Kobayashi, S.; Ghil, M.; Branchesi, M.; Guidorzi, C.; Stratta, G.; Ciszak, M.; Marino, F.; Ortolan, A.

We present a data-adaptive spectral method - Monte Carlo Singular Spectrum Analysis (MC-SSA) - and its modification to tackle astrophysical problems. Through numerical simulations we show the ability of the MC-SSA in dealing with 1/f β power-law noise affected by photon counting statistics. Such noise process is simulated by a first-order autoregressive, AR(1) process corrupted by intrinsic Poisson noise. In doing so, we statistically estimate a basic stochastic variation of the source and the corresponding fluctuations due to the quantum nature of light. In addition, MC-SSA test retains its effectiveness even when a significant percentage of the signal falls below a certain level of detection, e.g., caused by the instrument sensitivity. The parsimonious approach presented here may be broadly applied, from the search for extrasolar planets to the extraction of low-intensity coherent phenomena probably hidden in high energy transients.

GSTARI model of BPR assets in West Java, Central Java, and East Java

NASA Astrophysics Data System (ADS)

Susanti, Susi; Sulistijowati Handajani, Sri; Indriati, Diari

2018-05-01

Bank Perkreditan Rakyat (BPR) is a financial institution in Indonesia dealing with Micro, Small, and Medium Enterprises (MSMEs). Though limited to MSMEs, the development of the BPR industry continues to increase. West Java, Central Java, and East Java have high BPR asset development are suspected to be interconnected because of their economic activities as a neighboring provincies. BPR assets are nonstationary time series data that follow the uptrend pattern. Therefore, the suitable model with the data is generalized space time autoregressive integrated (GSTARI) which considers the spatial and time interrelationships. GSTARI model used spatial order 1 and the autoregressive order is obtained of optimal lag which has the smallest value of Akaike information criterion corrected. The correlation test results showed that the location used in this study had a close relationship. Based on the results of model identification, the best model obtained is GSTAR(31)-I(1). The parameter estimation used the ordinary least squares with the selection of significant variables used the stepwise method and the normalization cross correlation weighting. The residual model fulfilled the assumption of white noise and normal multivariate, so the model was appropriate. The average RMSE and MAPE values of the model were 498.75 and 2.48%.

Sensor network based solar forecasting using a local vector autoregressive ridge framework

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

Xu, J.; Yoo, S.; Heiser, J.

2016-04-04

The significant improvements and falling costs of photovoltaic (PV) technology make solar energy a promising resource, yet the cloud induced variability of surface solar irradiance inhibits its effective use in grid-tied PV generation. Short-term irradiance forecasting, especially on the minute scale, is critically important for grid system stability and auxiliary power source management. Compared to the trending sky imaging devices, irradiance sensors are inexpensive and easy to deploy but related forecasting methods have not been well researched. The prominent challenge of applying classic time series models on a network of irradiance sensors is to address their varying spatio-temporal correlations duemore » to local changes in cloud conditions. We propose a local vector autoregressive framework with ridge regularization to forecast irradiance without explicitly determining the wind field or cloud movement. By using local training data, our learned forecast model is adaptive to local cloud conditions and by using regularization, we overcome the risk of overfitting from the limited training data. Our systematic experimental results showed an average of 19.7% RMSE and 20.2% MAE improvement over the benchmark Persistent Model for 1-5 minute forecasts on a comprehensive 25-day dataset.« less

Covariance hypotheses for LANDSAT data

NASA Technical Reports Server (NTRS)

Decell, H. P.; Peters, C.

1983-01-01

Two covariance hypotheses are considered for LANDSAT data acquired by sampling fields, one an autoregressive covariance structure and the other the hypothesis of exchangeability. A minimum entropy approximation of the first structure by the second is derived and shown to have desirable properties for incorporation into a mixture density estimation procedure. Results of a rough test of the exchangeability hypothesis are presented.

Maximum Likelihood Dynamic Factor Modeling for Arbitrary "N" and "T" Using SEM

ERIC Educational Resources Information Center

Voelkle, Manuel C.; Oud, Johan H. L.; von Oertzen, Timo; Lindenberger, Ulman

2012-01-01

This article has 3 objectives that build on each other. First, we demonstrate how to obtain maximum likelihood estimates for dynamic factor models (the direct autoregressive factor score model) with arbitrary "T" and "N" by means of structural equation modeling (SEM) and compare the approach to existing methods. Second, we go beyond standard time…

Factors influencing crime rates: an econometric analysis approach

NASA Astrophysics Data System (ADS)

Bothos, John M. A.; Thomopoulos, Stelios C. A.

2016-05-01

The scope of the present study is to research the dynamics that determine the commission of crimes in the US society. Our study is part of a model we are developing to understand urban crime dynamics and to enhance citizens' "perception of security" in large urban environments. The main targets of our research are to highlight dependence of crime rates on certain social and economic factors and basic elements of state anticrime policies. In conducting our research, we use as guides previous relevant studies on crime dependence, that have been performed with similar quantitative analyses in mind, regarding the dependence of crime on certain social and economic factors using statistics and econometric modelling. Our first approach consists of conceptual state space dynamic cross-sectional econometric models that incorporate a feedback loop that describes crime as a feedback process. In order to define dynamically the model variables, we use statistical analysis on crime records and on records about social and economic conditions and policing characteristics (like police force and policing results - crime arrests), to determine their influence as independent variables on crime, as the dependent variable of our model. The econometric models we apply in this first approach are an exponential log linear model and a logit model. In a second approach, we try to study the evolvement of violent crime through time in the US, independently as an autonomous social phenomenon, using autoregressive and moving average time-series econometric models. Our findings show that there are certain social and economic characteristics that affect the formation of crime rates in the US, either positively or negatively. Furthermore, the results of our time-series econometric modelling show that violent crime, viewed solely and independently as a social phenomenon, correlates with previous years crime rates and depends on the social and economic environment's conditions during previous years.

An adaptive ARX model to estimate the RUL of aluminum plates based on its crack growth

NASA Astrophysics Data System (ADS)

Barraza-Barraza, Diana; Tercero-Gómez, Víctor G.; Beruvides, Mario G.; Limón-Robles, Jorge

2017-01-01

A wide variety of Condition-Based Maintenance (CBM) techniques deal with the problem of predicting the time for an asset fault. Most statistical approaches rely on historical failure data that might not be available in several practical situations. To address this issue, practitioners might require the use of self-starting approaches that consider only the available knowledge about the current degradation process and the asset operating context to update the prognostic model. Some authors use Autoregressive (AR) models for this purpose that are adequate when the asset operating context is constant, however, if it is variable, the accuracy of the models can be affected. In this paper, three autoregressive models with exogenous variables (ARX) were constructed, and their capability to estimate the remaining useful life (RUL) of a process was evaluated following the case of the aluminum crack growth problem. An existing stochastic model of aluminum crack growth was implemented and used to assess RUL estimation performance of the proposed ARX models through extensive Monte Carlo simulations. Point and interval estimations were made based only on individual history, behavior, operating conditions and failure thresholds. Both analytic and bootstrapping techniques were used in the estimation process. Finally, by including recursive parameter estimation and a forgetting factor, the ARX methodology adapts to changing operating conditions and maintain the focus on the current degradation level of an asset.

Assessment of variability in the hydrological cycle of the Loess Plateau, China: examining dependence structures of hydrological processes

NASA Astrophysics Data System (ADS)

Guo, A.; Wang, Y.

2017-12-01

Investigating variability in dependence structures of hydrological processes is of critical importance for developing an understanding of mechanisms of hydrological cycles in changing environments. In focusing on this topic, present work involves the following: (1) identifying and eliminating serial correlation and conditional heteroscedasticity in monthly streamflow (Q), precipitation (P) and potential evapotranspiration (PE) series using the ARMA-GARCH model (ARMA: autoregressive moving average; GARCH: generalized autoregressive conditional heteroscedasticity); (2) describing dependence structures of hydrological processes using partial copula coupled with the ARMA-GARCH model and identifying their variability via copula-based likelihood-ratio test method; and (3) determining conditional probability of annual Q under different climate scenarios on account of above results. This framework enables us to depict hydrological variables in the presence of conditional heteroscedasticity and to examine dependence structures of hydrological processes while excluding the influence of covariates by using partial copula-based ARMA-GARCH model. Eight major catchments across the Loess Plateau (LP) are used as study regions. Results indicate that (1) The occurrence of change points in dependence structures of Q and P (PE) varies across the LP. Change points of P-PE dependence structures in all regions almost fully correspond to the initiation of global warming, i.e., the early 1980s. (3) Conditional probabilities of annual Q under various P and PE scenarios are estimated from the 3-dimensional joint distribution of (Q, P and PE) based on the above change points. These findings shed light on mechanisms of the hydrological cycle and can guide water supply planning and management, particularly in changing environments.

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.

Modeling of Engine Parameters for Condition-Based Maintenance of the MTU Series 2000 Diesel Engine

DTIC Science & Technology

2016-09-01

are suitable. To model the behavior of the engine, an autoregressive distributed lag (ARDL) time series model of engine speed and exhaust gas... time series model of engine speed and exhaust gas temperature is derived. The lag length for ARDL is determined by whitening of residuals using the...15 B. REGRESSION ANALYSIS ....................................................................15 1. Time Series Analysis

Trans-dimensional inversion of microtremor array dispersion data with hierarchical autoregressive error models

NASA Astrophysics Data System (ADS)

Dettmer, Jan; Molnar, Sheri; Steininger, Gavin; Dosso, Stan E.; Cassidy, John F.

2012-02-01

This paper applies a general trans-dimensional Bayesian inference methodology and hierarchical autoregressive data-error models to the inversion of microtremor array dispersion data for shear wave velocity (vs) structure. This approach accounts for the limited knowledge of the optimal earth model parametrization (e.g. the number of layers in the vs profile) and of the data-error statistics in the resulting vs parameter uncertainty estimates. The assumed earth model parametrization influences estimates of parameter values and uncertainties due to different parametrizations leading to different ranges of data predictions. The support of the data for a particular model is often non-unique and several parametrizations may be supported. A trans-dimensional formulation accounts for this non-uniqueness by including a model-indexing parameter as an unknown so that groups of models (identified by the indexing parameter) are considered in the results. The earth model is parametrized in terms of a partition model with interfaces given over a depth-range of interest. In this work, the number of interfaces (layers) in the partition model represents the trans-dimensional model indexing. In addition, serial data-error correlations are addressed by augmenting the geophysical forward model with a hierarchical autoregressive error model that can account for a wide range of error processes with a small number of parameters. Hence, the limited knowledge about the true statistical distribution of data errors is also accounted for in the earth model parameter estimates, resulting in more realistic uncertainties and parameter values. Hierarchical autoregressive error models do not rely on point estimates of the model vector to estimate data-error statistics, and have no requirement for computing the inverse or determinant of a data-error covariance matrix. This approach is particularly useful for trans-dimensional inverse problems, as point estimates may not be representative of the state space that spans multiple subspaces of different dimensionalities. The order of the autoregressive process required to fit the data is determined here by posterior residual-sample examination and statistical tests. Inference for earth model parameters is carried out on the trans-dimensional posterior probability distribution by considering ensembles of parameter vectors. In particular, vs uncertainty estimates are obtained by marginalizing the trans-dimensional posterior distribution in terms of vs-profile marginal distributions. The methodology is applied to microtremor array dispersion data collected at two sites with significantly different geology in British Columbia, Canada. At both sites, results show excellent agreement with estimates from invasive measurements.

On the Trajectories of the Predetermined ALT Model: What Are We Really Modeling?

ERIC Educational Resources Information Center

Jongerling, Joran; Hamaker, Ellen L.

2011-01-01

This article shows that the mean and covariance structure of the predetermined autoregressive latent trajectory (ALT) model are very flexible. As a result, the shape of the modeled growth curve can be quite different from what one might expect at first glance. This is illustrated with several numerical examples that show that, for example, a…

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

NASA Astrophysics Data System (ADS)

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

2015-08-01

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

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

PubMed

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

2015-08-03

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

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

Forecasting Natural Gas Prices Using Wavelets, Time Series, and Artificial Neural Networks

PubMed Central

2015-01-01

Following the unconventional gas revolution, the forecasting of natural gas prices has become increasingly important because the association of these prices with those of crude oil has weakened. With this as motivation, we propose some modified hybrid models in which various combinations of the wavelet approximation, detail components, autoregressive integrated moving average, generalized autoregressive conditional heteroskedasticity, and artificial neural network models are employed to predict natural gas prices. We also emphasize the boundary problem in wavelet decomposition, and compare results that consider the boundary problem case with those that do not. The empirical results show that our suggested approach can handle the boundary problem, such that it facilitates the extraction of the appropriate forecasting results. The performance of the wavelet-hybrid approach was superior in all cases, whereas the application of detail components in the forecasting was only able to yield a small improvement in forecasting performance. Therefore, forecasting with only an approximation component would be acceptable, in consideration of forecasting efficiency. PMID:26539722

Forecasting Natural Gas Prices Using Wavelets, Time Series, and Artificial Neural Networks.

PubMed

Jin, Junghwan; Kim, Jinsoo

2015-01-01

Following the unconventional gas revolution, the forecasting of natural gas prices has become increasingly important because the association of these prices with those of crude oil has weakened. With this as motivation, we propose some modified hybrid models in which various combinations of the wavelet approximation, detail components, autoregressive integrated moving average, generalized autoregressive conditional heteroskedasticity, and artificial neural network models are employed to predict natural gas prices. We also emphasize the boundary problem in wavelet decomposition, and compare results that consider the boundary problem case with those that do not. The empirical results show that our suggested approach can handle the boundary problem, such that it facilitates the extraction of the appropriate forecasting results. The performance of the wavelet-hybrid approach was superior in all cases, whereas the application of detail components in the forecasting was only able to yield a small improvement in forecasting performance. Therefore, forecasting with only an approximation component would be acceptable, in consideration of forecasting efficiency.

Clustering of time-course gene expression profiles using normal mixture models with autoregressive random effects

PubMed Central

2012-01-01

Background Time-course gene expression data such as yeast cell cycle data may be periodically expressed. To cluster such data, currently used Fourier series approximations of periodic gene expressions have been found not to be sufficiently adequate to model the complexity of the time-course data, partly due to their ignoring the dependence between the expression measurements over time and the correlation among gene expression profiles. We further investigate the advantages and limitations of available models in the literature and propose a new mixture model with autoregressive random effects of the first order for the clustering of time-course gene-expression profiles. Some simulations and real examples are given to demonstrate the usefulness of the proposed models. Results We illustrate the applicability of our new model using synthetic and real time-course datasets. We show that our model outperforms existing models to provide more reliable and robust clustering of time-course data. Our model provides superior results when genetic profiles are correlated. It also gives comparable results when the correlation between the gene profiles is weak. In the applications to real time-course data, relevant clusters of coregulated genes are obtained, which are supported by gene-function annotation databases. Conclusions Our new model under our extension of the EMMIX-WIRE procedure is more reliable and robust for clustering time-course data because it adopts a random effects model that allows for the correlation among observations at different time points. It postulates gene-specific random effects with an autocorrelation variance structure that models coregulation within the clusters. The developed R package is flexible in its specification of the random effects through user-input parameters that enables improved modelling and consequent clustering of time-course data. PMID:23151154

Prediction of high airway pressure using a non-linear autoregressive model of pulmonary mechanics.

PubMed

Langdon, Ruby; Docherty, Paul D; Schranz, Christoph; Chase, J Geoffrey

2017-11-02

For mechanically ventilated patients with acute respiratory distress syndrome (ARDS), suboptimal PEEP levels can cause ventilator induced lung injury (VILI). In particular, high PEEP and high peak inspiratory pressures (PIP) can cause over distension of alveoli that is associated with VILI. However, PEEP must also be sufficient to maintain recruitment in ARDS lungs. A lung model that accurately and precisely predicts the outcome of an increase in PEEP may allow dangerous high PIP to be avoided, and reduce the incidence of VILI. Sixteen pressure-flow data sets were collected from nine mechanically ventilated ARDs patients that underwent one or more recruitment manoeuvres. A nonlinear autoregressive (NARX) model was identified on one or more adjacent PEEP steps, and extrapolated to predict PIP at 2, 4, and 6 cmH 2 O PEEP horizons. The analysis considered whether the predicted and measured PIP exceeded a threshold of 40 cmH 2 O. A direct comparison of the method was made using the first order model of pulmonary mechanics (FOM(I)). Additionally, a further, more clinically appropriate method for the FOM was tested, in which the FOM was trained on a single PEEP prior to prediction (FOM(II)). The NARX model exhibited very high sensitivity (> 0.96) in all cases, and a high specificity (> 0.88). While both FOM methods had a high specificity (> 0.96), the sensitivity was much lower, with a mean of 0.68 for FOM(I), and 0.82 for FOM(II). Clinically, false negatives are more harmful than false positives, as a high PIP may result in distension and VILI. Thus, the NARX model may be more effective than the FOM in allowing clinicians to reduce the risk of applying a PEEP that results in dangerously high airway pressures.

Detection of tripping gait patterns in the elderly using autoregressive features and support vector machines.

PubMed

Lai, Daniel T H; Begg, Rezaul K; Taylor, Simon; Palaniswami, Marimuthu

2008-01-01

Elderly tripping falls cost billions annually in medical funds and result in high mortality rates often perpetrated by pulmonary embolism (internal bleeding) and infected fractures that do not heal well. In this paper, we propose an intelligent gait detection system (AR-SVM) for screening elderly individuals at risk of suffering tripping falls. The motivation of this system is to provide early detection of elderly gait reminiscent of tripping characteristics so that preventive measures could be administered. Our system is composed of two stages, a predictor model estimated by an autoregressive (AR) process and a support vector machine (SVM) classifier. The system input is a digital signal constructed from consecutive measurements of minimum toe clearance (MTC) representative of steady-state walking. The AR-SVM system was tested on 23 individuals (13 healthy and 10 having suffered at least one tripping fall in the past year) who each completed a minimum of 10 min of walking on a treadmill at a self-selected pace. In the first stage, a fourth order AR model required at least 64 MTC values to correctly detect all fallers and non-fallers. Detection was further improved to less than 1 min of walking when the model coefficients were used as input features to the SVM classifier. The system achieved a detection accuracy of 95.65% with the leave one out method using only 16 MTC samples, but was reduced to 69.57% when eight MTC samples were used. These results demonstrate a fast and efficient system requiring a small number of strides and only MTC measurements for accurate detection of tripping gait characteristics.

Shifts of environmental and phytoplankton variables in a regulated river: A spatial-driven analysis.

PubMed

Sabater-Liesa, Laia; Ginebreda, Antoni; Barceló, Damià

2018-06-18

The longitudinal structure of the environmental and phytoplankton variables was investigated in the Ebro River (NE Spain), which is heavily affected by water abstraction and regulation. A first exploration indicated that the phytoplankton community did not resist the impact of reservoirs and barely recovered downstream of them. The spatial analysis showed that the responses of the phytoplankton and environmental variables were not uniform. The two set of variables revealed spatial variability discontinuities and river fragmentation upstream and downstream from the reservoirs. Reservoirs caused the replacement of spatially heterogeneous habitats by homogeneous spatially distributed water bodies, these new environmental conditions downstream benefiting the opportunist and cosmopolitan algal taxa. The application of a spatial auto-regression model to algal biomass (chlorophyll-a) permitted to capture the relevance and contribution of extra-local influences in the river ecosystem. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

The compass rose pattern in electricity prices.

PubMed

Batten, Jonathan A; Hamada, Mahmoud

2009-12-01

The "compass rose pattern" is known to appear in the phase portraits, or scatter diagrams, of the high-frequency returns of financial series. We first show that this pattern is also present in the returns of spot electricity prices. Early researchers investigating these phenomena hoped that these patterns signaled the presence of rich dynamics, possibly chaotic or fractal in nature. Although there is a definite autoregressive and conditional heteroscedasticity structure in electricity returns, we find that after simple filtering no pattern remains. While the series is non-normal in terms of their distribution and statistical tests fail to identify significant chaos, there is evidence of fractal structures in periodic price returns when measured over the trading day. The phase diagram of the filtered returns provides a useful visual check on independence, a property necessary for pricing and trading derivatives and portfolio construction, as well as providing useful insights into the market dynamics.

Kepler AutoRegressive Planet Search

NASA Astrophysics Data System (ADS)

Caceres, Gabriel Antonio; Feigelson, Eric

2016-01-01

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

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.

The Natural History of Kidney Graft Cortical Microcirculation Determined by Real-Time Contrast-Enhanced Sonography (RT-CES).

PubMed

Jiménez, Carlos; López, María Ovidia; Ros, Amaia; Aguilar, Ana; Menendez, David; Rivas, Begoña; Santana, María José; Vaca, Marco Antonio; Escuin, Fernando; Madero, Rosario; Selgas, Rafael

2016-01-01

Kidney transplantation is the therapy of choice for end-stage kidney disease. Graft's life span is shorter than expected due in part to the delayed diagnosis of various complications, specifically those related to silent progression. It is recognized that serum creatinine levels and proteinuria are poor markers of mild kidney lesions, which results in delayed clinical information. There are many investigation looking for early markers of graft damage. Decreasing kidney graft cortical microcirculation has been related to poor prognosis in kidney transplantation. Cortical capillary blood flow (CCBF) can be measured by real-time contrast-enhanced sonography (RT-CES). Our aim was to describe the natural history of CCBF over time under diverse conditions of kidney transplantation, to explore the influence of donor conditions and recipient events, and to determine the capacity of CCBF for predicting renal function in medium term. RT-CES was performed in 79 consecutive kidney transplant recipients during the first year under regular clinical practice. Cortical capillary blood flow was measured. Clinical variables were analyzed. The influence of CCBF has been determined by univariate and multivariate analysis using mixed regression models based on sequential measurements for each patient over time. We used a first-order autoregression model as the structure of the covariation between measures. The post-hoc comparisons were considered using the Bonferroni correction. The CCBF values varied significantly over the study periods and were significantly lower at 48 h and day 7. Brain-death donor age and CCBF levels showed an inverse relationship (r: -0.62, p<0.001). Living donors showed higher mean CCBF levels than brain-death donors at each point in the study. These significant differences persisted at month 12 (54.5 ± 28.2 vs 33.7 ± 30 dB/sec, living vs brain-death donor, respectively, p = 0.004) despite similar serum creatinine levels (1.5 ± 0.3 and 1.5 ± 0.5 mg/dL). A sole rejection episode was associated with lower overall CCBF values over the first year. CCBF defined better than level of serum creatinine the graft function status at medium-term. RT-CES is a non-invasive tool that can quantify and iteratively estimate cortical microcirculation. We have described the natural history of cortical capillary blood flow under regular clinical conditions.

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

QAARM: quasi-anharmonic autoregressive model reveals molecular recognition pathways in ubiquitin

PubMed Central

Savol, Andrej J.; Burger, Virginia M.; Agarwal, Pratul K.; Ramanathan, Arvind; Chennubhotla, Chakra S.

2011-01-01

Motivation: Molecular dynamics (MD) simulations have dramatically improved the atomistic understanding of protein motions, energetics and function. These growing datasets have necessitated a corresponding emphasis on trajectory analysis methods for characterizing simulation data, particularly since functional protein motions and transitions are often rare and/or intricate events. Observing that such events give rise to long-tailed spatial distributions, we recently developed a higher-order statistics based dimensionality reduction method, called quasi-anharmonic analysis (QAA), for identifying biophysically-relevant reaction coordinates and substates within MD simulations. Further characterization of conformation space should consider the temporal dynamics specific to each identified substate. Results: Our model uses hierarchical clustering to learn energetically coherent substates and dynamic modes of motion from a 0.5 μs ubiqutin simulation. Autoregressive (AR) modeling within and between states enables a compact and generative description of the conformational landscape as it relates to functional transitions between binding poses. Lacking a predictive component, QAA is extended here within a general AR model appreciative of the trajectory's temporal dependencies and the specific, local dynamics accessible to a protein within identified energy wells. These metastable states and their transition rates are extracted within a QAA-derived subspace using hierarchical Markov clustering to provide parameter sets for the second-order AR model. We show the learned model can be extrapolated to synthesize trajectories of arbitrary length. Contact: ramanathana@ornl.gov; chakracs@pitt.edu PMID:21685101

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

DOE PAGES

Buitrago, Jaime; Asfour, Shihab

2017-01-01

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

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

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

Buitrago, Jaime; Asfour, Shihab

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

Microforms in gravel bed rivers: Formation, disintegration, and effects on bedload transport

USGS Publications Warehouse

Strom, K.; Papanicolaou, A.N.; Evangelopoulos, N.; Odeh, M.

2004-01-01

This research aims to advance current knowledge on cluster formation and evolution by tackling some of the aspects associated with cluster microtopography and the effects of clusters on bedload transport. The specific objectives of the study are (1) to identify the bed shear stress range in which clusters form and disintegrate, (2) to quantitatively describe the spacing characteristics and orientation of clusters with respect to flow characteristics, (3) to quantify the effects clusters have on the mean bedload rate, and (4) to assess the effects of clusters on the pulsating nature of bedload. In order to meet the objectives of this study, two main experimental scenarios, namely, Test Series A and B (20 experiments overall) are considered in a laboratory flume under well-controlled conditions. Series A tests are performed to address objectives (1) and (2) while Series B is designed to meet objectives (3) and (4). Results show that cluster microforms develop in uniform sediment at 1.25 to 2 times the Shields parameter of an individual particle and start disintegrating at about 2.25 times the Shields parameter. It is found that during an unsteady flow event, effects of clusters on bedload transport rate can be classified in three different phases: a sink phase where clusters absorb incoming sediment, a neutral phase where clusters do not affect bedload, and a source phase where clusters release particles. Clusters also increase the magnitude of the fluctuations in bedload transport rate, showing that clusters amplify the unsteady nature of bedload transport. A fourth-order autoregressive, autoregressive integrated moving average model is employed to describe the time series of bedload and provide a predictive formula for predicting bedload at different periods. Finally, a change-point analysis enhanced with a binary segmentation procedure is performed to identify the abrupt changes in the bedload statistic characteristics due to the effects of clusters and detect the different phases in bedload time series using probability theory. The analysis verifies the experimental findings that three phases are detected in the bedload rate time series structure, namely, sink, neutral, and source. ?? ASCE / JUNE 2004.

The comparison study among several data transformations in autoregressive modeling

NASA Astrophysics Data System (ADS)

Setiyowati, Susi; Waluyo, Ramdhani Try

2015-12-01

In finance, the adjusted close of stocks are used to observe the performance of a company. The extreme prices, which may increase or decrease drastically, are often become particular concerned since it can impact to bankruptcy. As preventing action, the investors have to observe the future (forecasting) stock prices comprehensively. For that purpose, time series analysis could be one of statistical methods that can be implemented, for both stationary and non-stationary processes. Since the variability process of stocks prices tend to large and also most of time the extreme values are always exist, then it is necessary to do data transformation so that the time series models, i.e. autoregressive model, could be applied appropriately. One of popular data transformation in finance is return model, in addition to ratio of logarithm and some others Tukey ladder transformation. In this paper these transformations are applied to AR stationary models and non-stationary ARCH and GARCH models through some simulations with varying parameters. As results, this work present the suggestion table that shows transformations behavior for some condition of parameters and models. It is confirmed that the better transformation is obtained, depends on type of data distributions. In other hands, the parameter conditions term give significant influence either.

Sinusoidal synthesis based adaptive tracking for rotating machinery fault detection

NASA Astrophysics Data System (ADS)

Li, Gang; McDonald, Geoff L.; Zhao, Qing

2017-01-01

This paper presents a novel Sinusoidal Synthesis Based Adaptive Tracking (SSBAT) technique for vibration-based rotating machinery fault detection. The proposed SSBAT algorithm is an adaptive time series technique that makes use of both frequency and time domain information of vibration signals. Such information is incorporated in a time varying dynamic model. Signal tracking is then realized by applying adaptive sinusoidal synthesis to the vibration signal. A modified Least-Squares (LS) method is adopted to estimate the model parameters. In addition to tracking, the proposed vibration synthesis model is mainly used as a linear time-varying predictor. The health condition of the rotating machine is monitored by checking the residual between the predicted and measured signal. The SSBAT method takes advantage of the sinusoidal nature of vibration signals and transfers the nonlinear problem into a linear adaptive problem in the time domain based on a state-space realization. It has low computation burden and does not need a priori knowledge of the machine under the no-fault condition which makes the algorithm ideal for on-line fault detection. The method is validated using both numerical simulation and practical application data. Meanwhile, the fault detection results are compared with the commonly adopted autoregressive (AR) and autoregressive Minimum Entropy Deconvolution (ARMED) method to verify the feasibility and performance of the SSBAT method.

Exploring the Specifications of Spatial Adjacencies and Weights in Bayesian Spatial Modeling with Intrinsic Conditional Autoregressive Priors in a Small-area Study of Fall Injuries

PubMed Central

Law, Jane

2016-01-01

Intrinsic conditional autoregressive modeling in a Bayeisan hierarchical framework has been increasingly applied in small-area ecological studies. This study explores the specifications of spatial structure in this Bayesian framework in two aspects: adjacency, i.e., the set of neighbor(s) for each area; and (spatial) weight for each pair of neighbors. Our analysis was based on a small-area study of falling injuries among people age 65 and older in Ontario, Canada, that was aimed to estimate risks and identify risk factors of such falls. In the case study, we observed incorrect adjacencies information caused by deficiencies in the digital map itself. Further, when equal weights was replaced by weights based on a variable of expected count, the range of estimated risks increased, the number of areas with probability of estimated risk greater than one at different probability thresholds increased, and model fit improved. More importantly, significance of a risk factor diminished. Further research to thoroughly investigate different methods of variable weights; quantify the influence of specifications of spatial weights; and develop strategies for better defining spatial structure of a map in small-area analysis in Bayesian hierarchical spatial modeling is recommended. PMID:29546147

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.

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

PubMed

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

2017-09-18

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

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

PubMed

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

2017-08-01

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

Large deviation probabilities for correlated Gaussian stochastic processes and daily temperature anomalies

NASA Astrophysics Data System (ADS)

Massah, Mozhdeh; Kantz, Holger

2016-04-01

As we have one and only one earth and no replicas, climate characteristics are usually computed as time averages from a single time series. For understanding climate variability, it is essential to understand how close a single time average will typically be to an ensemble average. To answer this question, we study large deviation probabilities (LDP) of stochastic processes and characterize them by their dependence on the time window. In contrast to iid variables for which there exists an analytical expression for the rate function, the correlated variables such as auto-regressive (short memory) and auto-regressive fractionally integrated moving average (long memory) processes, have not an analytical LDP. We study LDP for these processes, in order to see how correlation affects this probability in comparison to iid data. Although short range correlations lead to a simple correction of sample size, long range correlations lead to a sub-exponential decay of LDP and hence to a very slow convergence of time averages. This effect is demonstrated for a 120 year long time series of daily temperature anomalies measured in Potsdam (Germany).

State Space Modeling of Time-Varying Contemporaneous and Lagged Relations in Connectivity Maps

PubMed Central

Molenaar, Peter C. M.; Beltz, Adriene M.; Gates, Kathleen M.; Wilson, Stephen J.

2017-01-01

Most connectivity mapping techniques for neuroimaging data assume stationarity (i.e., network parameters are constant across time), but this assumption does not always hold true. The authors provide a description of a new approach for simultaneously detecting time-varying (or dynamic) contemporaneous and lagged relations in brain connectivity maps. Specifically, they use a novel raw data likelihood estimation technique (involving a second-order extended Kalman filter/smoother embedded in a nonlinear optimizer) to determine the variances of the random walks associated with state space model parameters and their autoregressive components. The authors illustrate their approach with simulated and blood oxygen level-dependent functional magnetic resonance imaging data from 30 daily cigarette smokers performing a verbal working memory task, focusing on seven regions of interest (ROIs). Twelve participants had dynamic directed functional connectivity maps: Eleven had one or more time-varying contemporaneous ROI state loadings, and one had a time-varying autoregressive parameter. Compared to smokers without dynamic maps, smokers with dynamic maps performed the task with greater accuracy. Thus, accurate detection of dynamic brain processes is meaningfully related to behavior in a clinical sample. PMID:26546863

New Insights into Signed Path Coefficient Granger Causality Analysis.

PubMed

Zhang, Jian; Li, Chong; Jiang, Tianzi

2016-01-01

Granger causality analysis, as a time series analysis technique derived from econometrics, has been applied in an ever-increasing number of publications in the field of neuroscience, including fMRI, EEG/MEG, and fNIRS. The present study mainly focuses on the validity of "signed path coefficient Granger causality," a Granger-causality-derived analysis method that has been adopted by many fMRI researches in the last few years. This method generally estimates the causality effect among the time series by an order-1 autoregression, and defines a positive or negative coefficient as an "excitatory" or "inhibitory" influence. In the current work we conducted a series of computations from resting-state fMRI data and simulation experiments to illustrate the signed path coefficient method was flawed and untenable, due to the fact that the autoregressive coefficients were not always consistent with the real causal relationships and this would inevitablely lead to erroneous conclusions. Overall our findings suggested that the applicability of this kind of causality analysis was rather limited, hence researchers should be more cautious in applying the signed path coefficient Granger causality to fMRI data to avoid misinterpretation.

Short-term forecasts gain in accuracy. [Regression technique using ''Box-Jenkins'' analysis

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

Not Available

Box-Jenkins time-series models offer accuracy for short-term forecasts that compare with large-scale macroeconomic forecasts. Utilities need to be able to forecast peak demand in order to plan their generating, transmitting, and distribution systems. This new method differs from conventional models by not assuming specific data patterns, but by fitting available data into a tentative pattern on the basis of auto-correlations. Three types of models (autoregressive, moving average, or mixed autoregressive/moving average) can be used according to which provides the most appropriate combination of autocorrelations and related derivatives. Major steps in choosing a model are identifying potential models, estimating the parametersmore » of the problem, and running a diagnostic check to see if the model fits the parameters. The Box-Jenkins technique is well suited for seasonal patterns, which makes it possible to have as short as hourly forecasts of load demand. With accuracy up to two years, the method will allow electricity price-elasticity forecasting that can be applied to facility planning and rate design. (DCK)« less

The dynamic conditional relationship between stock market returns and implied volatility

NASA Astrophysics Data System (ADS)

Park, Sung Y.; Ryu, Doojin; Song, Jeongseok

2017-09-01

Using the dynamic conditional correlation multivariate generalized autoregressive conditional heteroskedasticity (DCC-MGARCH) model, we empirically examine the dynamic relationship between stock market returns (KOSPI200 returns) and implied volatility (VKOSPI), as well as their statistical mechanics, in the Korean market, a representative and leading emerging market. We consider four macroeconomic variables (exchange rates, risk-free rates, term spreads, and credit spreads) as potential determinants of the dynamic conditional correlation between returns and volatility. Of these macroeconomic variables, the change in exchange rates has a significant impact on the dynamic correlation between KOSPI200 returns and the VKOSPI, especially during the recent financial crisis. We also find that the risk-free rate has a marginal effect on this dynamic conditional relationship.

Optimization of autoregressive, exogenous inputs-based typhoon inundation forecasting models using a multi-objective genetic algorithm

NASA Astrophysics Data System (ADS)

Ouyang, Huei-Tau

2017-07-01

Three types of model for forecasting inundation levels during typhoons were optimized: the linear autoregressive model with exogenous inputs (LARX), the nonlinear autoregressive model with exogenous inputs with wavelet function (NLARX-W) and the nonlinear autoregressive model with exogenous inputs with sigmoid function (NLARX-S). The forecast performance was evaluated by three indices: coefficient of efficiency, error in peak water level and relative time shift. Historical typhoon data were used to establish water-level forecasting models that satisfy all three objectives. A multi-objective genetic algorithm was employed to search for the Pareto-optimal model set that satisfies all three objectives and select the ideal models for the three indices. Findings showed that the optimized nonlinear models (NLARX-W and NLARX-S) outperformed the linear model (LARX). Among the nonlinear models, the optimized NLARX-W model achieved a more balanced performance on the three indices than the NLARX-S models and is recommended for inundation forecasting during typhoons.

Heart rate measurement based on a time-lapse image.

PubMed

Takano, Chihiro; Ohta, Yuji

2007-10-01

Using a time-lapse image acquired from a CCD camera, we developed a non-contact and non-invasive device, which could measure both the respiratory and pulse rate simultaneously. The time-lapse image of a part of the subject's skin was consecutively captured, and the changes in the average image brightness of the region of interest (ROI) were measured for 30s. The brightness data were processed by a series of operations of interpolation as follows a first-order derivative, a low pass filter of 2 Hz, and a sixth-order auto-regressive (AR) spectral analysis. Fourteen sound and healthy female subjects (22-27 years of age) participated in the experiments. Each subject was told to keep a relaxed seating posture with no physical restriction. At the same time, heart rate was measured by a pulse oximeter and respiratory rate was measured by a thermistor placed at the external naris. Using AR spectral analysis, two clear peaks could be detected at approximately 0.3 and 1.2 Hz. The peaks were thought to correspond to the respiratory rate and the heart rate. Correlation coefficients of 0.90 and 0.93 were obtained for the measurement of heart rate and respiratory rate, respectively.

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.

Spatial Dynamics and Determinants of County-Level Education Expenditure in China

ERIC Educational Resources Information Center

Gu, Jiafeng

2012-01-01

In this paper, a multivariate spatial autoregressive model of local public education expenditure determination with autoregressive disturbance is developed and estimated. The existence of spatial interdependence is tested using Moran's I statistic and Lagrange multiplier test statistics for both the spatial error and spatial lag models. The full…

The Disparate Labor Market Impacts of Monetary Policy

ERIC Educational Resources Information Center

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

2004-01-01

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

Spatial Autocorrelation And Autoregressive Models In Ecology

Treesearch

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

2003-01-01

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

Are renewable energy policies upsetting carbon dioxide emissions? The case of Latin America countries.

PubMed

Fuinhas, José Alberto; Marques, António Cardoso; Koengkan, Matheus

2017-06-01

The impact of renewable energy policies in carbon dioxide emissions was analysed for a panel of ten Latin American countries, for the period from 1991 to 2012. Panel autoregressive distributed lag methodology was used to decompose the total effect of renewable energy policies on carbon dioxide emissions in its short- and long-run components. There is evidence for the presence of cross-sectional dependence, confirming that Latin American countries share spatial patterns. Heteroskedasticity, contemporaneous correlation, and first-order autocorrelation cross-sectional dependence are also present. To cope with these phenomena, the robust dynamic Driscoll-Kraay estimator, with fixed effects, was used. It was confirmed that the primary energy consumption per capita, in both the short- and long-run, contributes to an increase in carbon dioxide emissions, and also that renewable energy policies in the long-run, and renewable electricity generation per capita both in the short- and long-run, help to mitigate per capita carbon dioxide emissions.

Spatio-temporal statistical models for river monitoring networks.

PubMed

Clement, L; Thas, O; Vanrolleghem, P A; Ottoy, J P

2006-01-01

When introducing new wastewater treatment plants (WWTP), investors and policy makers often want to know if there indeed is a beneficial effect of the installation of a WWTP on the river water quality. Such an effect can be established in time as well as in space. Since both temporal and spatial components affect the output of a monitoring network, their dependence structure has to be modelled. River water quality data typically come from a river monitoring network for which the spatial dependence structure is unidirectional. Thus the traditional spatio-temporal models are not appropriate, as they cannot take advantage of this directional information. In this paper, a state-space model is presented in which the spatial dependence of the state variable is represented by a directed acyclic graph, and the temporal dependence by a first-order autoregressive process. The state-space model is extended with a linear model for the mean to estimate the effect of the activation of a WWTP on the dissolved oxygen concentration downstream.

Analysis and generation of groundwater concentration time series

NASA Astrophysics Data System (ADS)

Crăciun, Maria; Vamoş, Călin; Suciu, Nicolae

2018-01-01

Concentration time series are provided by simulated concentrations of a nonreactive solute transported in groundwater, integrated over the transverse direction of a two-dimensional computational domain and recorded at the plume center of mass. The analysis of a statistical ensemble of time series reveals subtle features that are not captured by the first two moments which characterize the approximate Gaussian distribution of the two-dimensional concentration fields. The concentration time series exhibit a complex preasymptotic behavior driven by a nonstationary trend and correlated fluctuations with time-variable amplitude. Time series with almost the same statistics are generated by successively adding to a time-dependent trend a sum of linear regression terms, accounting for correlations between fluctuations around the trend and their increments in time, and terms of an amplitude modulated autoregressive noise of order one with time-varying parameter. The algorithm generalizes mixing models used in probability density function approaches. The well-known interaction by exchange with the mean mixing model is a special case consisting of a linear regression with constant coefficients.

Load forecasting via suboptimal seasonal autoregressive models and iteratively reweighted least squares estimation

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

Mbamalu, G.A.N.; El-Hawary, M.E.

The authors propose suboptimal least squares or IRWLS procedures for estimating the parameters of a seasonal multiplicative AR model encountered during power system load forecasting. The proposed method involves using an interactive computer environment to estimate the parameters of a seasonal multiplicative AR process. The method comprises five major computational steps. The first determines the order of the seasonal multiplicative AR process, and the second uses the least squares or the IRWLS to estimate the optimal nonseasonal AR model parameters. In the third step one obtains the intermediate series by back forecast, which is followed by using the least squaresmore » or the IRWLS to estimate the optimal season AR parameters. The final step uses the estimated parameters to forecast future load. The method is applied to predict the Nova Scotia Power Corporation's 168 lead time hourly load. The results obtained are documented and compared with results based on the Box and Jenkins method.« less

Enhancing second-order conditioning with lesions of the basolateral amygdala.

PubMed

Holland, Peter C

2016-04-01

Because the occurrence of primary reinforcers in natural environments is relatively rare, conditioned reinforcement plays an important role in many accounts of behavior, including pathological behaviors such as the abuse of alcohol or drugs. As a result of pairing with natural or drug reinforcers, initially neutral cues acquire the ability to serve as reinforcers for subsequent learning. Accepting a major role for conditioned reinforcement in everyday learning is complicated by the often-evanescent nature of this phenomenon in the laboratory, especially when primary reinforcers are entirely absent from the test situation. Here, I found that under certain conditions, the impact of conditioned reinforcement could be extended by lesions of the basolateral amygdala (BLA). Rats received first-order Pavlovian conditioning pairings of 1 visual conditioned stimulus (CS) with food prior to receiving excitotoxic or sham lesions of the BLA, and first-order pairings of another visual CS with food after that surgery. Finally, each rat received second-order pairings of a different auditory cue with each visual first-order CS. As in prior studies, relative to sham-lesioned control rats, lesioned rats were impaired in their acquisition of second-order conditioning to the auditory cue paired with the first-order CS that was trained after surgery. However, lesioned rats showed enhanced and prolonged second-order conditioning to the auditory cue paired with the first-order CS that was trained before amygdala damage was made. Implications for an enhanced role for conditioned reinforcement by drug-related cues after drug-induced alterations in neural plasticity are discussed. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

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

Friedman, N.; Koller, D.; Halpern, J.Y.

Conditional logics play an important role in recent attempts to investigate default reasoning. This paper investigates first-order conditional logic. We show that, as for first-order probabilistic logic, it is important not to confound statistical conditionals over the domain (such as {open_quotes}most birds fly{close_quotes}), and subjective conditionals over possible worlds (such as I believe that Tweety is unlikely to fly). We then address the issue of ascribing semantics to first-order conditional logic. As in the propositional case, there are many possible semantics. To study the problem in a coherent way, we use plausibility structures. These provide us with a general frameworkmore » in which many of the standard approaches can be embedded. We show that while these standard approaches are all the same at the propositional level, they are significantly different in the context of a first-order language. We show that plausibilities provide the most natural extension of conditional logic to the first-order case: We provide a sound and complete axiomatization that contains only the KLM properties and standard axioms of first-order modal logic. We show that most of the other approaches have additional properties, which result in an inappropriate treatment of an infinitary version of the lottery paradox.« less

Impact of Autocorrelation on Functional Connectivity

PubMed Central

Arbabshirani, Mohammad R.; Damaraju, Eswar; Phlypo, Ronald; Plis, Sergey; Allen, Elena; Ma, Sai; Mathalon, Daniel; Preda, Adrian; Vaidya, Jatin G.; Adali, Tülay; Calhoun, Vince D.

2014-01-01

Although the impact of serial correlation (autocorrelation) in residuals of general linear models for fMRI time-series has been studied extensively, the effect of autocorrelation on functional connectivity studies has been largely neglected until recently. Some recent studies based on results from economics have questioned the conventional estimation of functional connectivity and argue that not correcting for autocorrelation in fMRI time-series results in “spurious” correlation coefficients. In this paper, first we assess the effect of autocorrelation on Pearson correlation coefficient through theoretical approximation and simulation. Then we present this effect on real fMRI data. To our knowledge this is the first work comprehensively investigating the effect of autocorrelation on functional connectivity estimates. Our results show that although FC values are altered, even following correction for autocorrelation, results of hypothesis testing on FC values remain very similar to those before correction. In real data we show this is true for main effects and also for group difference testing between healthy controls and schizophrenia patients. We further discuss model order selection in the context of autoregressive processes, effects of frequency filtering and propose a preprocessing pipeline for connectivity studies. PMID:25072392

The progression of the entropy of a five dimensional psychotherapeutic system

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

Badalamenti, A.F.; Langs, R.J.

This paper presents a study of the deterministic and stochastic behavior of the entropy of a 5-dimensional, 2400 state, system across each of six psychotherapeutic sessions. The growth of entropy was found to be logarithmic in each session. The stochastic behavior of a moving 600 second estimator of entropy revealed a Box-Jenkins model of type (1,1,0) - that is, the first difference of the entropy series was first order autoregressive or prior state sensitive. In addition, the patient and therapist entropy series exhibited no significant cross correlation across lags of -300 to +300 seconds. Yet all such pairs of seriesmore » exhibited high coherency past the frequency of .06 (on a full range of 0 to .5). Furthermore, all the patients and therapists were attracted to a geometric center of mass in 5-dimensional space which was different from the geometric center of the region where the system lived. The process significance of the findings and the relationship between the deterministic and stochastic results are discussed. The paper is then placed in the broader context of our efforts to provide new and meaningful quantitative dimensions and mathematical models to psychotherapy research. 59 refs.« less

Application of multivariate autoregressive spectrum estimation to ULF waves

NASA Technical Reports Server (NTRS)

Ioannidis, G. A.

1975-01-01

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

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

PubMed Central

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

2011-01-01

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

Measurement of distributed strain and temperature based on higher order and higher mode Bragg conditions

NASA Technical Reports Server (NTRS)

Sirkis, James S. (Inventor); Sivanesan, Ponniah (Inventor); Venkat, Venki S. (Inventor)

2001-01-01

A Bragg grating sensor for measuring distributed strain and temperature at the same time comprises an optical fiber having a single mode operating wavelength region and below a cutoff wavelength of the fiber having a multimode operating wavelength region. A saturated, higher order Bragg grating having first and second order Bragg conditions is fabricated in the optical fiber. The first order of Bragg resonance wavelength of the Bragg grating is within the single mode operating wavelength region of the optical fiber and the second order of Bragg resonance wavelength is below the cutoff wavelength of the fiber within the multimode operating wavelength region. The reflectivities of the saturated Bragg grating at the first and second order Bragg conditions are less than two orders of magnitude of one another. In use, the first and second order Bragg conditions are simultaneously created in the sensor at the respective wavelengths and a signal from the sensor is demodulated with respect to each of the wavelengths corresponding to the first and second order Bragg conditions. Two Bragg conditions have different responsivities to strain and temperature, thus allowing two equations for axial strain and temperature to be found in terms of the measure shifts in the primary and second order Bragg wavelengths. This system of equations can be solved for strain and temperature.

Modeling Seasonality in Carbon Dioxide Emissions From Fossil Fuel Consumption

NASA Astrophysics Data System (ADS)

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

2004-05-01

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

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.

Reconstruction of late Holocene climate based on tree growth and mechanistic hierarchical models

USGS Publications Warehouse

Tipton, John; Hooten, Mevin B.; Pederson, Neil; Tingley, Martin; Bishop, Daniel

2016-01-01

Reconstruction of pre-instrumental, late Holocene climate is important for understanding how climate has changed in the past and how climate might change in the future. Statistical prediction of paleoclimate from tree ring widths is challenging because tree ring widths are a one-dimensional summary of annual growth that represents a multi-dimensional set of climatic and biotic influences. We develop a Bayesian hierarchical framework using a nonlinear, biologically motivated tree ring growth model to jointly reconstruct temperature and precipitation in the Hudson Valley, New York. Using a common growth function to describe the response of a tree to climate, we allow for species-specific parameterizations of the growth response. To enable predictive backcasts, we model the climate variables with a vector autoregressive process on an annual timescale coupled with a multivariate conditional autoregressive process that accounts for temporal correlation and cross-correlation between temperature and precipitation on a monthly scale. Our multi-scale temporal model allows for flexibility in the climate response through time at different temporal scales and predicts reasonable climate scenarios given tree ring width data.

Estimation of Value-at-Risk for Energy Commodities via CAViaR Model

NASA Astrophysics Data System (ADS)

Xiliang, Zhao; Xi, Zhu

This paper uses the Conditional Autoregressive Value at Risk model (CAViaR) proposed by Engle and Manganelli (2004) to evaluate the value-at-risk for daily spot prices of Brent crude oil and West Texas Intermediate crude oil covering the period May 21th, 1987 to Novermber 18th, 2008. Then the accuracy of the estimates of CAViaR model, Normal-GARCH, and GED-GARCH was compared. The results show that all the methods do good job for the low confidence level (95%), and GED-GARCH is the best for spot WTI price, Normal-GARCH and Adaptive-CAViaR are the best for spot Brent price. However, for the high confidence level (99%), Normal-GARCH do a good job for spot WTI, GED-GARCH and four kind of CAViaR specifications do well for spot Brent price. Normal-GARCH does badly for spot Brent price. The result seems suggest that CAViaR do well as well as GED-GARCH since CAViaR directly model the quantile autoregression, but it does not outperform GED-GARCH although it does outperform Normal-GARCH.

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

Treesearch

Consuelo Brandeis; Dayton M. Lambert

2014-01-01

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

Functional MRI and Multivariate Autoregressive Models

PubMed Central

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

2010-01-01

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

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

PubMed

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

2016-09-26

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

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

PubMed Central

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

2016-01-01

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

Relative risk for HIV in India - An estimate using conditional auto-regressive models with Bayesian approach.

PubMed

Kandhasamy, Chandrasekaran; Ghosh, Kaushik

2017-02-01

Indian states are currently classified into HIV-risk categories based on the observed prevalence counts, percentage of infected attendees in antenatal clinics, and percentage of infected high-risk individuals. This method, however, does not account for the spatial dependence among the states nor does it provide any measure of statistical uncertainty. We provide an alternative model-based approach to address these issues. Our method uses Poisson log-normal models having various conditional autoregressive structures with neighborhood-based and distance-based weight matrices and incorporates all available covariate information. We use R and WinBugs software to fit these models to the 2011 HIV data. Based on the Deviance Information Criterion, the convolution model using distance-based weight matrix and covariate information on female sex workers, literacy rate and intravenous drug users is found to have the best fit. The relative risk of HIV for the various states is estimated using the best model and the states are then classified into the risk categories based on these estimated values. An HIV risk map of India is constructed based on these results. The choice of the final model suggests that an HIV control strategy which focuses on the female sex workers, intravenous drug users and literacy rate would be most effective. Copyright © 2017 Elsevier Ltd. All rights reserved.

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

NASA Astrophysics Data System (ADS)

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

2017-04-01

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

Granger causality revisited

PubMed Central

Friston, Karl J.; Bastos, André M.; Oswal, Ashwini; van Wijk, Bernadette; Richter, Craig; Litvak, Vladimir

2014-01-01

This technical paper offers a critical re-evaluation of (spectral) Granger causality measures in the analysis of biological timeseries. Using realistic (neural mass) models of coupled neuronal dynamics, we evaluate the robustness of parametric and nonparametric Granger causality. Starting from a broad class of generative (state-space) models of neuronal dynamics, we show how their Volterra kernels prescribe the second-order statistics of their response to random fluctuations; characterised in terms of cross-spectral density, cross-covariance, autoregressive coefficients and directed transfer functions. These quantities in turn specify Granger causality — providing a direct (analytic) link between the parameters of a generative model and the expected Granger causality. We use this link to show that Granger causality measures based upon autoregressive models can become unreliable when the underlying dynamics is dominated by slow (unstable) modes — as quantified by the principal Lyapunov exponent. However, nonparametric measures based on causal spectral factors are robust to dynamical instability. We then demonstrate how both parametric and nonparametric spectral causality measures can become unreliable in the presence of measurement noise. Finally, we show that this problem can be finessed by deriving spectral causality measures from Volterra kernels, estimated using dynamic causal modelling. PMID:25003817

State space modeling of time-varying contemporaneous and lagged relations in connectivity maps.

PubMed

Molenaar, Peter C M; Beltz, Adriene M; Gates, Kathleen M; Wilson, Stephen J

2016-01-15

Most connectivity mapping techniques for neuroimaging data assume stationarity (i.e., network parameters are constant across time), but this assumption does not always hold true. The authors provide a description of a new approach for simultaneously detecting time-varying (or dynamic) contemporaneous and lagged relations in brain connectivity maps. Specifically, they use a novel raw data likelihood estimation technique (involving a second-order extended Kalman filter/smoother embedded in a nonlinear optimizer) to determine the variances of the random walks associated with state space model parameters and their autoregressive components. The authors illustrate their approach with simulated and blood oxygen level-dependent functional magnetic resonance imaging data from 30 daily cigarette smokers performing a verbal working memory task, focusing on seven regions of interest (ROIs). Twelve participants had dynamic directed functional connectivity maps: Eleven had one or more time-varying contemporaneous ROI state loadings, and one had a time-varying autoregressive parameter. Compared to smokers without dynamic maps, smokers with dynamic maps performed the task with greater accuracy. Thus, accurate detection of dynamic brain processes is meaningfully related to behavior in a clinical sample. Published by Elsevier Inc.

Ensemble Nonlinear Autoregressive Exogenous Artificial Neural Networks for Short-Term Wind Speed and Power Forecasting.

PubMed

Men, Zhongxian; Yee, Eugene; Lien, Fue-Sang; Yang, Zhiling; Liu, Yongqian

2014-01-01

Short-term wind speed and wind power forecasts (for a 72 h period) are obtained using a nonlinear autoregressive exogenous artificial neural network (ANN) methodology which incorporates either numerical weather prediction or high-resolution computational fluid dynamics wind field information as an exogenous input. An ensemble approach is used to combine the predictions from many candidate ANNs in order to provide improved forecasts for wind speed and power, along with the associated uncertainties in these forecasts. More specifically, the ensemble ANN is used to quantify the uncertainties arising from the network weight initialization and from the unknown structure of the ANN. All members forming the ensemble of neural networks were trained using an efficient particle swarm optimization algorithm. The results of the proposed methodology are validated using wind speed and wind power data obtained from an operational wind farm located in Northern China. The assessment demonstrates that this methodology for wind speed and power forecasting generally provides an improvement in predictive skills when compared to the practice of using an "optimal" weight vector from a single ANN while providing additional information in the form of prediction uncertainty bounds.

Ensemble Nonlinear Autoregressive Exogenous Artificial Neural Networks for Short-Term Wind Speed and Power Forecasting

PubMed Central

Lien, Fue-Sang; Yang, Zhiling; Liu, Yongqian

2014-01-01

Short-term wind speed and wind power forecasts (for a 72 h period) are obtained using a nonlinear autoregressive exogenous artificial neural network (ANN) methodology which incorporates either numerical weather prediction or high-resolution computational fluid dynamics wind field information as an exogenous input. An ensemble approach is used to combine the predictions from many candidate ANNs in order to provide improved forecasts for wind speed and power, along with the associated uncertainties in these forecasts. More specifically, the ensemble ANN is used to quantify the uncertainties arising from the network weight initialization and from the unknown structure of the ANN. All members forming the ensemble of neural networks were trained using an efficient particle swarm optimization algorithm. The results of the proposed methodology are validated using wind speed and wind power data obtained from an operational wind farm located in Northern China. The assessment demonstrates that this methodology for wind speed and power forecasting generally provides an improvement in predictive skills when compared to the practice of using an “optimal” weight vector from a single ANN while providing additional information in the form of prediction uncertainty bounds. PMID:27382627

New Insights into Signed Path Coefficient Granger Causality Analysis

PubMed Central

Zhang, Jian; Li, Chong; Jiang, Tianzi

2016-01-01

Granger causality analysis, as a time series analysis technique derived from econometrics, has been applied in an ever-increasing number of publications in the field of neuroscience, including fMRI, EEG/MEG, and fNIRS. The present study mainly focuses on the validity of “signed path coefficient Granger causality,” a Granger-causality-derived analysis method that has been adopted by many fMRI researches in the last few years. This method generally estimates the causality effect among the time series by an order-1 autoregression, and defines a positive or negative coefficient as an “excitatory” or “inhibitory” influence. In the current work we conducted a series of computations from resting-state fMRI data and simulation experiments to illustrate the signed path coefficient method was flawed and untenable, due to the fact that the autoregressive coefficients were not always consistent with the real causal relationships and this would inevitablely lead to erroneous conclusions. Overall our findings suggested that the applicability of this kind of causality analysis was rather limited, hence researchers should be more cautious in applying the signed path coefficient Granger causality to fMRI data to avoid misinterpretation. PMID:27833547

Hybrid wavelet-support vector machine approach for modelling rainfall-runoff process.

PubMed

Komasi, Mehdi; Sharghi, Soroush

2016-01-01

Because of the importance of water resources management, the need for accurate modeling of the rainfall-runoff process has rapidly grown in the past decades. Recently, the support vector machine (SVM) approach has been used by hydrologists for rainfall-runoff modeling and the other fields of hydrology. Similar to the other artificial intelligence models, such as artificial neural network (ANN) and adaptive neural fuzzy inference system, the SVM model is based on the autoregressive properties. In this paper, the wavelet analysis was linked to the SVM model concept for modeling the rainfall-runoff process of Aghchai and Eel River watersheds. In this way, the main time series of two variables, rainfall and runoff, were decomposed to multiple frequent time series by wavelet theory; then, these time series were imposed as input data on the SVM model in order to predict the runoff discharge one day ahead. The obtained results show that the wavelet SVM model can predict both short- and long-term runoff discharges by considering the seasonality effects. Also, the proposed hybrid model is relatively more appropriate than classical autoregressive ones such as ANN and SVM because it uses the multi-scale time series of rainfall and runoff data in the modeling process.

Modulation of electroencephalograph activity by manual acupuncture stimulation in healthy subjects: An autoregressive spectral analysis

NASA Astrophysics Data System (ADS)

Yi, Guo-Sheng; Wang, Jiang; Deng, Bin; Wei, Xi-Le; Han, Chun-Xiao

2013-02-01

To investigate whether and how manual acupuncture (MA) modulates brain activities, we design an experiment where acupuncture at acupoint ST36 of the right leg is used to obtain electroencephalograph (EEG) signals in healthy subjects. We adopt the autoregressive (AR) Burg method to estimate the power spectrum of EEG signals and analyze the relative powers in delta (0 Hz-4 Hz), theta (4 Hz-8 Hz), alpha (8 Hz-13 Hz), and beta (13 Hz-30 Hz) bands. Our results show that MA at ST36 can significantly increase the EEG slow wave relative power (delta band) and reduce the fast wave relative powers (alpha and beta bands), while there are no statistical differences in theta band relative power between different acupuncture states. In order to quantify the ratio of slow to fast wave EEG activity, we compute the power ratio index. It is found that the MA can significantly increase the power ratio index, especially in frontal and central lobes. All the results highlight the modulation of brain activities with MA and may provide potential help for the clinical use of acupuncture. The proposed quantitative method of acupuncture signals may be further used to make MA more standardized.

Typology of alcohol users based on longitudinal patterns of drinking.

PubMed

Harrington, Magdalena; Velicer, Wayne F; Ramsey, Susan

2014-03-01

Worldwide, alcohol is the most commonly used psychoactive substance. However, heterogeneity among alcohol users has been widely recognized. This paper presents a typology of alcohol users based on an implementation of idiographic methodology to examine longitudinal daily and cyclic (weekly) patterns of alcohol use at the individual level. A secondary data analysis was performed on the pre-intervention data from a large randomized control trial. A time series analysis was performed at the individual level, and a dynamic cluster analysis was employed to identify homogenous longitudinal patterns of drinking behavior at the group level. The analysis employed 180 daily observations of alcohol use in a sample of 177 alcohol users. The first order autocorrelations ranged from -.76 to .72, and seventh order autocorrelations ranged from -.27 to .79. Eight distinct profiles of alcohol users were identified, each characterized by a unique configuration of first and seventh autoregressive terms and longitudinal trajectories of alcohol use. External validity of the profiles confirmed the theoretical relevance of different patterns of alcohol use. Significant differences among the eight subtypes were found on gender, marital status, frequency of drug use, lifetime alcohol dependence, family history of alcohol use and the Short Index of Problems. Our findings demonstrate that individuals can have very different temporal patterns of drinking behavior. The daily and cyclic patterns of alcohol use may be important for designing tailored interventions for problem drinkers. Copyright © 2013 Elsevier Ltd. All rights reserved.

Typology of Alcohol Users Based on Longitudinal Patterns of Drinking

PubMed Central

Harrington, Magdalena; Velicer, Wayne F.; Ramsey, Susan

2014-01-01

Objective Worldwide, alcohol is the most commonly used psychoactive substance. However, heterogeneity among alcohol users has been widely recognized. This paper presents a typology of alcohol users based on an implementation of idiographic methodology to examine longitudinal daily and cyclic (weekly) patterns of alcohol use at the individual level. Method A secondary data analysis was performed on the pre-intervention data from a large randomized control trial. A time series analysis was performed at the individual level, and a dynamic cluster analysis was employed to identify homogenous longitudinal patterns of drinking behavior at the group level. The analysis employed 180 daily observations of alcohol use in a sample of 177 alcohol users. Results The first order autocorrelations ranged from −.76 to .72, and seventh order autocorrelations ranged from −.27 to .79. Eight distinct profiles of alcohol users were identified, each characterized by a unique configuration of first and seventh autoregressive terms and longitudinal trajectories of alcohol use. External validity of the profiles confirmed the theoretical relevance of different patterns of alcohol use. Significant differences among the eight subtypes were found on gender, marital status, frequency of drug use, lifetime alcohol dependence, family history of alcohol use and the Short Index of Problems. Conclusions Our findings demonstrate that individuals can have very different temporal patterns of drinking behavior. The daily and cyclic patterns of alcohol use may be important for designing tailored interventions for problem drinkers. PMID:24333036

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

NASA Astrophysics Data System (ADS)

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

2007-12-01

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

Equivalent Dynamic Models.

PubMed

Molenaar, Peter C M

2017-01-01

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

Reconstruction of Complex Directional Networks with Group Lasso Nonlinear Conditional Granger Causality.

PubMed

Yang, Guanxue; Wang, Lin; Wang, Xiaofan

2017-06-07

Reconstruction of networks underlying complex systems is one of the most crucial problems in many areas of engineering and science. In this paper, rather than identifying parameters of complex systems governed by pre-defined models or taking some polynomial and rational functions as a prior information for subsequent model selection, we put forward a general framework for nonlinear causal network reconstruction from time-series with limited observations. With obtaining multi-source datasets based on the data-fusion strategy, we propose a novel method to handle nonlinearity and directionality of complex networked systems, namely group lasso nonlinear conditional granger causality. Specially, our method can exploit different sets of radial basis functions to approximate the nonlinear interactions between each pair of nodes and integrate sparsity into grouped variables selection. The performance characteristic of our approach is firstly assessed with two types of simulated datasets from nonlinear vector autoregressive model and nonlinear dynamic models, and then verified based on the benchmark datasets from DREAM3 Challenge4. Effects of data size and noise intensity are also discussed. All of the results demonstrate that the proposed method performs better in terms of higher area under precision-recall curve.

The effect of case management and vector-control interventions on space-time patterns of malaria incidence in Uganda.

PubMed

Ssempiira, Julius; Kissa, John; Nambuusi, Betty; Kyozira, Carol; Rutazaana, Damian; Mukooyo, Eddie; Opigo, Jimmy; Makumbi, Fredrick; Kasasa, Simon; Vounatsou, Penelope

2018-04-12

Electronic reporting of routine health facility data in Uganda began with the adoption of the District Health Information Software System version 2 (DHIS2) in 2011. This has improved health facility reporting and overall data quality. In this study, the effects of case management with artemisinin-based combination therapy (ACT) and vector control interventions on space-time patterns of disease incidence were determined using DHIS2 data reported during 2013-2016. Bayesian spatio-temporal negative binomial models were fitted on district-aggregated monthly malaria cases, reported by two age groups, defined by a cut-off age of 5 years. The effects of interventions were adjusted for socio-economic and climatic factors. Spatial and temporal correlations were taken into account by assuming a conditional autoregressive and a first-order autoregressive AR(1) process on district and monthly specific random effects, respectively. Fourier trigonometric functions were incorporated in the models to take into account seasonal fluctuations in malaria transmission. The temporal variation in incidence was similar in both age groups and depicted a steady decline up to February 2014, followed by an increase from March 2015 onwards. The trends were characterized by a strong bi-annual seasonal pattern with two peaks during May-July and September-December. Average monthly incidence in children < 5 years declined from 74.7 cases (95% CI 72.4-77.1) in 2013 to 49.4 (95% CI 42.9-55.8) per 1000 in 2015 and followed by an increase in 2016 of up to 51.3 (95% CI 42.9-55.8). In individuals ≥ 5 years, a decline in incidence from 2013 to 2015 was followed by an increase in 2016. A 100% increase in insecticide-treated nets (ITN) coverage was associated with a decline in incidence by 44% (95% BCI 28-59%). Similarly, a 100% increase in ACT coverage reduces incidence by 28% (95% BCI 11-45%) and 25% (95% BCI 20-28%) in children < 5 years and individuals ≥ 5 years, respectively. The ITN effect was not statistically important in older individuals. The space-time patterns of malaria incidence in children < 5 are similar to those of parasitaemia risk predicted from the malaria indicator survey of 2014-15. The decline in malaria incidence highlights the effectiveness of vector-control interventions and case management with ACT in Uganda. This calls for optimizing and sustaining interventions to achieve universal coverage and curb reverses in malaria decline.

Linear and nonlinear trending and prediction for AVHRR time series data

NASA Technical Reports Server (NTRS)

Smid, J.; Volf, P.; Slama, M.; Palus, M.

1995-01-01

The variability of AVHRR calibration coefficient in time was analyzed using algorithms of linear and non-linear time series analysis. Specifically we have used the spline trend modeling, autoregressive process analysis, incremental neural network learning algorithm and redundancy functional testing. The analysis performed on available AVHRR data sets revealed that (1) the calibration data have nonlinear dependencies, (2) the calibration data depend strongly on the target temperature, (3) both calibration coefficients and the temperature time series can be modeled, in the first approximation, as autonomous dynamical systems, (4) the high frequency residuals of the analyzed data sets can be best modeled as an autoregressive process of the 10th degree. We have dealt with a nonlinear identification problem and the problem of noise filtering (data smoothing). The system identification and filtering are significant problems for AVHRR data sets. The algorithms outlined in this study can be used for the future EOS missions. Prediction and smoothing algorithms for time series of calibration data provide a functional characterization of the data. Those algorithms can be particularly useful when calibration data are incomplete or sparse.

Pavlovian second-order conditioned analgesia.

PubMed

Ross, R T

1986-01-01

Three experiments with rat subjects assessed conditioned analgesia in a Pavlovian second-order conditioning procedure by using inhibition of responding to thermal stimulation as an index of pain sensitivity. In Experiment 1, rats receiving second-order conditioning showed longer response latencies during a test of pain sensitivity in the presence of the second-order conditioned stimulus (CS) than rats receiving appropriate control procedures. Experiment 2 found that extinction of the first-order CS had no effect on established second-order conditioned analgesia. Experiment 3 evaluated the effects of post second-order conditioning pairings of morphine and the shock unconditioned stimulus (US). Rats receiving paired morphine-shock presentations showed significantly shorter response latencies during a hot-plate test of pain sensitivity in the presence of the second-order CS than did groups of rats receiving various control procedures; second-order analgesia was attenuated. These data extend the associative account of conditioned analgesia to second-order conditioning situations and are discussed in terms of the mediation of both first- and second-order analgesia by an association between the CS and a representation or expectancy of the US, which may directly activate endogenous pain inhibition systems.

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

ERIC Educational Resources Information Center

Murphy, Daniel L.; Pituch, Keenan A.

2009-01-01

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

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

ERIC Educational Resources Information Center

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

2016-01-01

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

Stratospheric ozone profile and total ozone trends derived from the SAGE I and SAGE II data

NASA Technical Reports Server (NTRS)

Mccormick, M. P.; Veiga, Robert E.; Chu, William P.

1992-01-01

Global trends in both stratospheric column ozone and as a function of altitude are derived on the basis of SAGE I/II ozone data from the period 1979-1991. A statistical model containing quasi-biennial, seasonal, and semiannual oscillations, a linear component, and a first-order autoregressive noise process was fit to the time series of SAGE I/II monthly zonal mean data. The linear trend in column ozone above 17-km altitude, averaged between 65 deg S and 65 deg N, is -0.30 +/-0.19 percent/yr, or -3.6 percent over the time period February 1979 through April 1991. The data show that the column trend above 17 km is nearly zero in the tropics and increases towards the high latitudes with values of -0.6 percent/yr at 60 deg S and -0.35 percent/yr at 60 deg N. Both these results are in agreement with the recent TOMS results. The profile trend analyses show that the column ozone losses are occurring below 25 km, with most of the loss coming from the region between 17 and 20 km. Negative trend values on the order of -2 percent/yr are found at 17 km in midlatitudes.

Persistence of non-Markovian Gaussian stationary processes in discrete time

NASA Astrophysics Data System (ADS)

Nyberg, Markus; Lizana, Ludvig

2018-04-01

The persistence of a stochastic variable is the probability that it does not cross a given level during a fixed time interval. Although persistence is a simple concept to understand, it is in general hard to calculate. Here we consider zero mean Gaussian stationary processes in discrete time n . Few results are known for the persistence P0(n ) in discrete time, except the large time behavior which is characterized by the nontrivial constant θ through P0(n ) ˜θn . Using a modified version of the independent interval approximation (IIA) that we developed before, we are able to calculate P0(n ) analytically in z -transform space in terms of the autocorrelation function A (n ) . If A (n )→0 as n →∞ , we extract θ numerically, while if A (n )=0 , for finite n >N , we find θ exactly (within the IIA). We apply our results to three special cases: the nearest-neighbor-correlated "first order moving average process", where A (n )=0 for n >1 , the double exponential-correlated "second order autoregressive process", where A (n ) =c1λ1n+c2λ2n , and power-law-correlated variables, where A (n ) ˜n-μ . Apart from the power-law case when μ <5 , we find excellent agreement with simulations.

The Effect of a Smoking Ban on Hospitalization Rates for Cardiovascular and Respiratory Conditions in Prince Edward Island, Canada

PubMed Central

Gaudreau, Katherine; Sanford, Carolyn J.; Cheverie, Connie; McClure, Carol

2013-01-01

Background This is the first study to have examined the effect of smoking bans on hospitalizations in the Atlantic Canadian socio-economic, cultural and climatic context. On June 1, 2003 Prince Edward Island (PEI) enacted a province-wide smoking ban in public places and workplaces. Changes in hospital admission rates for cardiovascular (acute myocardial infarction, angina, and stroke) and respiratory (chronic obstructive pulmonary disease and asthma) conditions were examined before and after the smoking ban. Methods Crude annual and monthly admission rates for the above conditions were calculated from April 1, 1995 to December 31, 2008 in all PEI acute care hospitals. Autoregressive Integrated Moving Average time series models were used to test for changes in mean and trend of monthly admission rates for study conditions, control conditions and a control province after the comprehensive smoking ban. Age- and sex-based analyses were completed. Results The mean rate of acute myocardial infarctions was reduced by 5.92 cases per 100,000 person-months (P = 0.04) immediately after the smoking ban. The trend of monthly angina admissions in men was reduced by −0.44 cases per 100,000 person-months (P = 0.01) in the 67 months after the smoking ban. All other cardiovascular and respiratory admission changes were non-significant. Conclusions A comprehensive smoking ban in PEI reduced the overall mean number of acute myocardial infarction admissions and the trend of angina hospital admissions. PMID:23520450

The effect of a smoking ban on hospitalization rates for cardiovascular and respiratory conditions in Prince Edward Island, Canada.

PubMed

Gaudreau, Katherine; Sanford, Carolyn J; Cheverie, Connie; McClure, Carol

2013-01-01

This is the first study to have examined the effect of smoking bans on hospitalizations in the Atlantic Canadian socio-economic, cultural and climatic context. On June 1, 2003 Prince Edward Island (PEI) enacted a province-wide smoking ban in public places and workplaces. Changes in hospital admission rates for cardiovascular (acute myocardial infarction, angina, and stroke) and respiratory (chronic obstructive pulmonary disease and asthma) conditions were examined before and after the smoking ban. Crude annual and monthly admission rates for the above conditions were calculated from April 1, 1995 to December 31, 2008 in all PEI acute care hospitals. Autoregressive Integrated Moving Average time series models were used to test for changes in mean and trend of monthly admission rates for study conditions, control conditions and a control province after the comprehensive smoking ban. Age- and sex-based analyses were completed. The mean rate of acute myocardial infarctions was reduced by 5.92 cases per 100,000 person-months (P = 0.04) immediately after the smoking ban. The trend of monthly angina admissions in men was reduced by -0.44 cases per 100,000 person-months (P = 0.01) in the 67 months after the smoking ban. All other cardiovascular and respiratory admission changes were non-significant. A comprehensive smoking ban in PEI reduced the overall mean number of acute myocardial infarction admissions and the trend of angina hospital admissions.

Structured Spatial Modeling and Mapping of Domestic Violence Against Women of Reproductive Age in Rwanda.

PubMed

Habyarimana, Faustin; Zewotir, Temesgen; Ramroop, Shaun

2018-03-01

The main objective of this study was to assess the risk factors and spatial correlates of domestic violence against women of reproductive age in Rwanda. A structured spatial approach was used to account for the nonlinear nature of some covariates and the spatial variability on domestic violence. The nonlinear effect was modeled through second-order random walk, and the structured spatial effect was modeled through Gaussian Markov Random Fields specified as an intrinsic conditional autoregressive model. The data from the Rwanda Demographic and Health Survey 2014/2015 were used as an application. The findings of this study revealed that the risk factors of domestic violence against women are the wealth quintile of the household, the size of the household, the husband or partner's age, the husband or partner's level of education, ownership of the house, polygamy, the alcohol consumption status of the husband or partner, the woman's perception of wife-beating attitude, and the use of contraceptive methods. The study also highlighted the significant spatial variation of domestic violence against women at district level.

Characterization of depressive States in bipolar patients using wearable textile technology and instantaneous heart rate variability assessment.

PubMed

Valenza, Gaetano; Citi, Luca; Gentili, Claudio; Lanata, Antonio; Scilingo, Enzo Pasquale; Barbieri, Riccardo

2015-01-01

The analysis of cognitive and autonomic responses to emotionally relevant stimuli could provide a viable solution for the automatic recognition of different mood states, both in normal and pathological conditions. In this study, we present a methodological application describing a novel system based on wearable textile technology and instantaneous nonlinear heart rate variability assessment, able to characterize the autonomic status of bipolar patients by considering only electrocardiogram recordings. As a proof of this concept, our study presents results obtained from eight bipolar patients during their normal daily activities and being elicited according to a specific emotional protocol through the presentation of emotionally relevant pictures. Linear and nonlinear features were computed using a novel point-process-based nonlinear autoregressive integrative model and compared with traditional algorithmic methods. The estimated indices were used as the input of a multilayer perceptron to discriminate the depressive from the euthymic status. Results show that our system achieves much higher accuracy than the traditional techniques. Moreover, the inclusion of instantaneous higher order spectra features significantly improves the accuracy in successfully recognizing depression from euthymia.

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

NASA Astrophysics Data System (ADS)

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

2015-09-01

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

Real-time processing of radar return on a parallel computer

NASA Technical Reports Server (NTRS)

Aalfs, David D.

1992-01-01

NASA is working with the FAA to demonstrate the feasibility of pulse Doppler radar as a candidate airborne sensor to detect low altitude windshears. The need to provide the pilot with timely information about possible hazards has motivated a demand for real-time processing of a radar return. Investigated here is parallel processing as a means of accommodating the high data rates required. A PC based parallel computer, called the transputer, is used to investigate issues in real time concurrent processing of radar signals. A transputer network is made up of an array of single instruction stream processors that can be networked in a variety of ways. They are easily reconfigured and software development is largely independent of the particular network topology. The performance of the transputer is evaluated in light of the computational requirements. A number of algorithms have been implemented on the transputers in OCCAM, a language specially designed for parallel processing. These include signal processing algorithms such as the Fast Fourier Transform (FFT), pulse-pair, and autoregressive modelling, as well as routing software to support concurrency. The most computationally intensive task is estimating the spectrum. Two approaches have been taken on this problem, the first and most conventional of which is to use the FFT. By using table look-ups for the basis function and other optimizing techniques, an algorithm has been developed that is sufficient for real time. The other approach is to model the signal as an autoregressive process and estimate the spectrum based on the model coefficients. This technique is attractive because it does not suffer from the spectral leakage problem inherent in the FFT. Benchmark tests indicate that autoregressive modeling is feasible in real time.

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.

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

NASA Technical Reports Server (NTRS)

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

2001-01-01

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

Asymmetric statistical features of the Chinese domestic and international gold price fluctuation

NASA Astrophysics Data System (ADS)

Cao, Guangxi; Zhao, Yingchao; Han, Yan

2015-05-01

Analyzing the statistical features of fluctuation is remarkably significant for financial risk identification and measurement. In this study, the asymmetric detrended fluctuation analysis (A-DFA) method was applied to evaluate asymmetric multifractal scaling behaviors in the Shanghai and New York gold markets. Our findings showed that the multifractal features of the Chinese and international gold spot markets were asymmetric. The gold return series persisted longer in an increasing trend than in a decreasing trend. Moreover, the asymmetric degree of multifractals in the Chinese and international gold markets decreased with the increase in fluctuation range. In addition, the empirical analysis using sliding window technology indicated that multifractal asymmetry in the Chinese and international gold markets was characterized by its time-varying feature. However, the Shanghai and international gold markets basically shared a similar asymmetric degree evolution pattern. The American subprime mortgage crisis (2008) and the European debt crisis (2010) enhanced the asymmetric degree of the multifractal features of the Chinese and international gold markets. Furthermore, we also make statistical tests for the results of multifractatity and asymmetry, and discuss the origin of them. Finally, results of the empirical analysis using the threshold autoregressive conditional heteroskedasticity (TARCH) and exponential generalized autoregressive conditional heteroskedasticity (EGARCH) models exhibited that good news had a more significant effect on the cyclical fluctuation of the gold market than bad news. Moreover, good news exerted a more significant effect on the Chinese gold market than on the international gold market.

Fuzzy neural network technique for system state forecasting.

PubMed

Li, Dezhi; Wang, Wilson; Ismail, Fathy

2013-10-01

In many system state forecasting applications, the prediction is performed based on multiple datasets, each corresponding to a distinct system condition. The traditional methods dealing with multiple datasets (e.g., vector autoregressive moving average models and neural networks) have some shortcomings, such as limited modeling capability and opaque reasoning operations. To tackle these problems, a novel fuzzy neural network (FNN) is proposed in this paper to effectively extract information from multiple datasets, so as to improve forecasting accuracy. The proposed predictor consists of both autoregressive (AR) nodes modeling and nonlinear nodes modeling; AR models/nodes are used to capture the linear correlation of the datasets, and the nonlinear correlation of the datasets are modeled with nonlinear neuron nodes. A novel particle swarm technique [i.e., Laplace particle swarm (LPS) method] is proposed to facilitate parameters estimation of the predictor and improve modeling accuracy. The effectiveness of the developed FNN predictor and the associated LPS method is verified by a series of tests related to Mackey-Glass data forecast, exchange rate data prediction, and gear system prognosis. Test results show that the developed FNN predictor and the LPS method can capture the dynamics of multiple datasets effectively and track system characteristics accurately.

Autoregressive model in the Lp norm space for EEG analysis.

PubMed

Li, Peiyang; Wang, Xurui; Li, Fali; Zhang, Rui; Ma, Teng; Peng, Yueheng; Lei, Xu; Tian, Yin; Guo, Daqing; Liu, Tiejun; Yao, Dezhong; Xu, Peng

2015-01-30

The autoregressive (AR) model is widely used in electroencephalogram (EEG) analyses such as waveform fitting, spectrum estimation, and system identification. In real applications, EEGs are inevitably contaminated with unexpected outlier artifacts, and this must be overcome. However, most of the current AR models are based on the L2 norm structure, which exaggerates the outlier effect due to the square property of the L2 norm. In this paper, a novel AR object function is constructed in the Lp (p≤1) norm space with the aim to compress the outlier effects on EEG analysis, and a fast iteration procedure is developed to solve this new AR model. The quantitative evaluation using simulated EEGs with outliers proves that the proposed Lp (p≤1) AR can estimate the AR parameters more robustly than the Yule-Walker, Burg and LS methods, under various simulated outlier conditions. The actual application to the resting EEG recording with ocular artifacts also demonstrates that Lp (p≤1) AR can effectively address the outliers and recover a resting EEG power spectrum that is more consistent with its physiological basis. Copyright © 2014 Elsevier B.V. All rights reserved.

Non-linear models for the detection of impaired cerebral blood flow autoregulation.

PubMed

Chacón, Max; Jara, José Luis; Miranda, Rodrigo; Katsogridakis, Emmanuel; Panerai, Ronney B

2018-01-01

The ability to discriminate between normal and impaired dynamic cerebral autoregulation (CA), based on measurements of spontaneous fluctuations in arterial blood pressure (BP) and cerebral blood flow (CBF), has considerable clinical relevance. We studied 45 normal subjects at rest and under hypercapnia induced by breathing a mixture of carbon dioxide and air. Non-linear models with BP as input and CBF velocity (CBFV) as output, were implemented with support vector machines (SVM) using separate recordings for learning and validation. Dynamic SVM implementations used either moving average or autoregressive structures. The efficiency of dynamic CA was estimated from the model's derived CBFV response to a step change in BP as an autoregulation index for both linear and non-linear models. Non-linear models with recurrences (autoregressive) showed the best results, with CA indexes of 5.9 ± 1.5 in normocapnia, and 2.5 ± 1.2 for hypercapnia with an area under the receiver-operator curve of 0.955. The high performance achieved by non-linear SVM models to detect deterioration of dynamic CA should encourage further assessment of its applicability to clinical conditions where CA might be impaired.

Non-linear models for the detection of impaired cerebral blood flow autoregulation

PubMed Central

Miranda, Rodrigo; Katsogridakis, Emmanuel

2018-01-01

The ability to discriminate between normal and impaired dynamic cerebral autoregulation (CA), based on measurements of spontaneous fluctuations in arterial blood pressure (BP) and cerebral blood flow (CBF), has considerable clinical relevance. We studied 45 normal subjects at rest and under hypercapnia induced by breathing a mixture of carbon dioxide and air. Non-linear models with BP as input and CBF velocity (CBFV) as output, were implemented with support vector machines (SVM) using separate recordings for learning and validation. Dynamic SVM implementations used either moving average or autoregressive structures. The efficiency of dynamic CA was estimated from the model’s derived CBFV response to a step change in BP as an autoregulation index for both linear and non-linear models. Non-linear models with recurrences (autoregressive) showed the best results, with CA indexes of 5.9 ± 1.5 in normocapnia, and 2.5 ± 1.2 for hypercapnia with an area under the receiver-operator curve of 0.955. The high performance achieved by non-linear SVM models to detect deterioration of dynamic CA should encourage further assessment of its applicability to clinical conditions where CA might be impaired. PMID:29381724

Autoregressive processes with exponentially decaying probability distribution functions: applications to daily variations of a stock market index.

PubMed

Porto, Markus; Roman, H Eduardo

2002-04-01

We consider autoregressive conditional heteroskedasticity (ARCH) processes in which the variance sigma(2)(y) depends linearly on the absolute value of the random variable y as sigma(2)(y) = a+b absolute value of y. While for the standard model, where sigma(2)(y) = a + b y(2), the corresponding probability distribution function (PDF) P(y) decays as a power law for absolute value of y-->infinity, in the linear case it decays exponentially as P(y) approximately exp(-alpha absolute value of y), with alpha = 2/b. We extend these results to the more general case sigma(2)(y) = a+b absolute value of y(q), with 0 < q < 2. We find stretched exponential decay for 1 < q < 2 and stretched Gaussian behavior for 0 < q < 1. As an application, we consider the case q=1 as our starting scheme for modeling the PDF of daily (logarithmic) variations in the Dow Jones stock market index. When the history of the ARCH process is taken into account, the resulting PDF becomes a stretched exponential even for q = 1, with a stretched exponent beta = 2/3, in a much better agreement with the empirical data.

Fractal and chaotic laws on seismic dissipated energy in an energy system of engineering structures

NASA Astrophysics Data System (ADS)

Cui, Yu-Hong; Nie, Yong-An; Yan, Zong-Da; Wu, Guo-You

1998-09-01

Fractal and chaotic laws of engineering structures are discussed in this paper, it means that the intrinsic essences and laws on dynamic systems which are made from seismic dissipated energy intensity E d and intensity of seismic dissipated energy moment I e are analyzed. Based on the intrinsic characters of chaotic and fractal dynamic system of E d and I e, three kinds of approximate dynamic models are rebuilt one by one: index autoregressive model, threshold autoregressive model and local-approximate autoregressive model. The innate laws, essences and systematic error of evolutional behavior I e are explained over all, the short-term behavior predictability and long-term behavior probability of which are analyzed in the end. That may be valuable for earthquake-resistant theory and analysis method in practical engineering structures.

Evolution of a plastic quantitative trait in an age-structured population in a fluctuating environment.

PubMed

Engen, Steinar; Lande, Russell; Saether, Bernt-Erik

2011-10-01

We analyze weak fluctuating selection on a quantitative character in an age-structured population not subject to density regulation. We assume that early in the first year of life before selection, during a critical state of development, environments exert a plastic effect on the phenotype, which remains constant throughout the life of an individual. Age-specific selection on the character affects survival and fecundity, which have intermediate optima subject to temporal environmental fluctuations with directional selection in some age classes as special cases. Weighting individuals by their reproductive value, as suggested by Fisher, we show that the expected response per year in the weighted mean character has the same form as for models with no age structure. Environmental stochasticity generates stochastic fluctuations in the weighted mean character following a first-order autoregressive model with a temporally autocorrelated noise term and stationary variance depending on the amount of phenotypic plasticity. The parameters of the process are simple weighted averages of parameters used to describe age-specific survival and fecundity. The "age-specific selective weights" are related to the stable distribution of reproductive values among age classes. This allows partitioning of the change in the weighted mean character into age-specific components. © 2011 The Author(s). Evolution© 2011 The Society for the Study of Evolution.

A theoretical stochastic control framework for adapting radiotherapy to hypoxia

NASA Astrophysics Data System (ADS)

Saberian, Fatemeh; Ghate, Archis; Kim, Minsun

2016-10-01

Hypoxia, that is, insufficient oxygen partial pressure, is a known cause of reduced radiosensitivity in solid tumors, and especially in head-and-neck tumors. It is thus believed to adversely affect the outcome of fractionated radiotherapy. Oxygen partial pressure varies spatially and temporally over the treatment course and exhibits inter-patient and intra-tumor variation. Emerging advances in non-invasive functional imaging offer the future possibility of adapting radiotherapy plans to this uncertain spatiotemporal evolution of hypoxia over the treatment course. We study the potential benefits of such adaptive planning via a theoretical stochastic control framework using computer-simulated evolution of hypoxia on computer-generated test cases in head-and-neck cancer. The exact solution of the resulting control problem is computationally intractable. We develop an approximation algorithm, called certainty equivalent control, that calls for the solution of a sequence of convex programs over the treatment course; dose-volume constraints are handled using a simple constraint generation method. These convex programs are solved using an interior point algorithm with a logarithmic barrier via Newton’s method and backtracking line search. Convexity of various formulations in this paper is guaranteed by a sufficient condition on radiobiological tumor-response parameters. This condition is expected to hold for head-and-neck tumors and for other similarly responding tumors where the linear dose-response parameter is larger than the quadratic dose-response parameter. We perform numerical experiments on four test cases by using a first-order vector autoregressive process with exponential and rational-quadratic covariance functions from the spatiotemporal statistics literature to simulate the evolution of hypoxia. Our results suggest that dynamic planning could lead to a considerable improvement in the number of tumor cells remaining at the end of the treatment course. Through these simulations, we also gain insights into when and why dynamic planning is likely to yield the largest benefits.

Stochastic Generation of Monthly Rainfall Data

NASA Astrophysics Data System (ADS)

Srikanthan, R.

2009-03-01

Monthly rainfall data is generally needed in the simulation of water resources systems, and in the estimation of water yield from large catchments. Monthly streamflow data generation models are usually applied to generate monthly rainfall data, but this presents problems for most regions, which have significant months of no rainfall. In an earlier study, Srikanthan et al. (J. Hydrol. Eng., ASCE 11(3) (2006) 222-229) recommended the modified method of fragments to disaggregate the annual rainfall data generated by a first-order autoregressive model. The main drawback of this approach is the occurrence of similar patterns when only a short length of historic data is available. Porter and Pink (Hydrol. Water Res. Symp. (1991) 187-191) used synthetic fragments from a Thomas-Fiering monthly model to overcome this drawback. As an alternative, a new two-part monthly model is nested in an annual model to generate monthly rainfall data which preserves both the monthly and annual characteristics. This nested model was applied to generate rainfall data from seven rainfall stations located in eastern and southern parts of Australia, and the results showed that the model performed satisfactorily.

SEM Based CARMA Time Series Modeling for Arbitrary N.

PubMed

Oud, Johan H L; Voelkle, Manuel C; Driver, Charles C

2018-01-01

This article explains in detail the state space specification and estimation of first and higher-order autoregressive moving-average models in continuous time (CARMA) in an extended structural equation modeling (SEM) context for N = 1 as well as N > 1. To illustrate the approach, simulations will be presented in which a single panel model (T = 41 time points) is estimated for a sample of N = 1,000 individuals as well as for samples of N = 100 and N = 50 individuals, followed by estimating 100 separate models for each of the one-hundred N = 1 cases in the N = 100 sample. Furthermore, we will demonstrate how to test the difference between the full panel model and each N = 1 model by means of a subject-group-reproducibility test. Finally, the proposed analyses will be applied in an empirical example, in which the relationships between mood at work and mood at home are studied in a sample of N = 55 women. All analyses are carried out by ctsem, an R-package for continuous time modeling, interfacing to OpenMx.

Short-term climate change impacts on Mara basin hydrology

NASA Astrophysics Data System (ADS)

Demaria, E. M.; Roy, T.; Valdés, J. B.; Lyon, B.; Valdés-Pineda, R.; Serrat-Capdevila, A.; Durcik, M.; Gupta, H.

2017-12-01

The predictability of climate diminishes significantly at shorter time scales (e.g. decadal). Both natural variability as well as sampling variability of climate can obscure or enhance climate change signals in these shorter scales. Therefore, in order to assess the impacts of climate change on basin hydrology, it is important to design climate projections with exhaustive climate scenarios. In this study, we first create seasonal climate scenarios by combining (1) synthetic precipitation projections generated from a Vector Auto-Regressive (VAR) model using the University of East Anglia Climate Research Unit (UEA-CRU) data with (2) seasonal trends calculated from 31 models in the Coupled Model Intercomparison Project Phase 5 (CMIP). The seasonal climate projections are then disaggregated to daily level using the Agricultural Modern-Era Retrospective Analysis for Research and Applications (AgMERRA) data. The daily climate data are then bias-corrected and used as forcings to the land-surface model, Variable Infiltration Capacity (VIC), to generate different hydrological projections for the Mara River basin in East Africa, which are then evaluated to study the hydrologic changes in the basin in the next three decades (2020-2050).

The Lateral Tracking Control for the Intelligent Vehicle Based on Adaptive PID Neural Network.

PubMed

Han, Gaining; Fu, Weiping; Wang, Wen; Wu, Zongsheng

2017-05-30

The intelligent vehicle is a complicated nonlinear system, and the design of a path tracking controller is one of the key technologies in intelligent vehicle research. This paper mainly designs a lateral control dynamic model of the intelligent vehicle, which is used for lateral tracking control. Firstly, the vehicle dynamics model (i.e., transfer function) is established according to the vehicle parameters. Secondly, according to the vehicle steering control system and the CARMA (Controlled Auto-Regression and Moving-Average) model, a second-order control system model is built. Using forgetting factor recursive least square estimation (FFRLS), the system parameters are identified. Finally, a neural network PID (Proportion Integral Derivative) controller is established for lateral path tracking control based on the vehicle model and the steering system model. Experimental simulation results show that the proposed model and algorithm have the high real-time and robustness in path tracing control. This provides a certain theoretical basis for intelligent vehicle autonomous navigation tracking control, and lays the foundation for the vertical and lateral coupling control.

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.

The Lateral Tracking Control for the Intelligent Vehicle Based on Adaptive PID Neural Network

PubMed Central

Han, Gaining; Fu, Weiping; Wang, Wen; Wu, Zongsheng

2017-01-01

The intelligent vehicle is a complicated nonlinear system, and the design of a path tracking controller is one of the key technologies in intelligent vehicle research. This paper mainly designs a lateral control dynamic model of the intelligent vehicle, which is used for lateral tracking control. Firstly, the vehicle dynamics model (i.e., transfer function) is established according to the vehicle parameters. Secondly, according to the vehicle steering control system and the CARMA (Controlled Auto-Regression and Moving-Average) model, a second-order control system model is built. Using forgetting factor recursive least square estimation (FFRLS), the system parameters are identified. Finally, a neural network PID (Proportion Integral Derivative) controller is established for lateral path tracking control based on the vehicle model and the steering system model. Experimental simulation results show that the proposed model and algorithm have the high real-time and robustness in path tracing control. This provides a certain theoretical basis for intelligent vehicle autonomous navigation tracking control, and lays the foundation for the vertical and lateral coupling control. PMID:28556817

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

PubMed

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

2013-09-01

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

Critical ignition conditions in exothermically reacting systems: first-order reactions

NASA Astrophysics Data System (ADS)

Filimonov, Valeriy Yu.

2017-10-01

In this paper, the comparative analysis of the thermal explosion (TE) critical conditions on the planes temperature-conversion degree and temperature-time was conducted. It was established that the ignition criteria are almost identical only at relatively small values of Todes parameter. Otherwise, the results of critical conditions analysis on the plane temperature-conversion degree may be wrong. The asymptotic method of critical conditions calculation for the first-order reactions was proposed (taking into account the reactant consumption). The degeneration conditions of TE were determined. The calculation of critical conditions for specific first-order reaction was made. The comparison of the analytical results obtained with the results of numerical calculations and experimental data showed that they are in good agreement.

Critical ignition conditions in exothermically reacting systems: first-order reactions.

PubMed

Filimonov, Valeriy Yu

2017-10-01

In this paper, the comparative analysis of the thermal explosion (TE) critical conditions on the planes temperature-conversion degree and temperature-time was conducted. It was established that the ignition criteria are almost identical only at relatively small values of Todes parameter. Otherwise, the results of critical conditions analysis on the plane temperature-conversion degree may be wrong. The asymptotic method of critical conditions calculation for the first-order reactions was proposed (taking into account the reactant consumption). The degeneration conditions of TE were determined. The calculation of critical conditions for specific first-order reaction was made. The comparison of the analytical results obtained with the results of numerical calculations and experimental data showed that they are in good agreement.

Brillouin Scattering Spectrum Analysis Based on Auto-Regressive Spectral Estimation

NASA Astrophysics Data System (ADS)

Huang, Mengyun; Li, Wei; Liu, Zhangyun; Cheng, Linghao; Guan, Bai-Ou

2018-06-01

Auto-regressive (AR) spectral estimation technology is proposed to analyze the Brillouin scattering spectrum in Brillouin optical time-domain refelectometry. It shows that AR based method can reliably estimate the Brillouin frequency shift with an accuracy much better than fast Fourier transform (FFT) based methods provided the data length is not too short. It enables about 3 times improvement over FFT at a moderate spatial resolution.

Hedonic price models with omitted variables and measurement errors: a constrained autoregression-structural equation modeling approach with application to urban Indonesia

NASA Astrophysics Data System (ADS)

Suparman, Yusep; Folmer, Henk; Oud, Johan H. L.

2014-01-01

Omitted variables and measurement errors in explanatory variables frequently occur in hedonic price models. Ignoring these problems leads to biased estimators. In this paper, we develop a constrained autoregression-structural equation model (ASEM) to handle both types of problems. Standard panel data models to handle omitted variables bias are based on the assumption that the omitted variables are time-invariant. ASEM allows handling of both time-varying and time-invariant omitted variables by constrained autoregression. In the case of measurement error, standard approaches require additional external information which is usually difficult to obtain. ASEM exploits the fact that panel data are repeatedly measured which allows decomposing the variance of a variable into the true variance and the variance due to measurement error. We apply ASEM to estimate a hedonic housing model for urban Indonesia. To get insight into the consequences of measurement error and omitted variables, we compare the ASEM estimates with the outcomes of (1) a standard SEM, which does not account for omitted variables, (2) a constrained autoregression model, which does not account for measurement error, and (3) a fixed effects hedonic model, which ignores measurement error and time-varying omitted variables. The differences between the ASEM estimates and the outcomes of the three alternative approaches are substantial.

Seasonality and Trend Forecasting of Tuberculosis Prevalence Data in Eastern Cape, South Africa, Using a Hybrid Model.

PubMed

Azeez, Adeboye; Obaromi, Davies; Odeyemi, Akinwumi; Ndege, James; Muntabayi, Ruffin

2016-07-26

Tuberculosis (TB) is a deadly infectious disease caused by Mycobacteria tuberculosis. Tuberculosis as a chronic and highly infectious disease is prevalent in almost every part of the globe. More than 95% of TB mortality occurs in low/middle income countries. In 2014, approximately 10 million people were diagnosed with active TB and two million died from the disease. In this study, our aim is to compare the predictive powers of the seasonal autoregressive integrated moving average (SARIMA) and neural network auto-regression (SARIMA-NNAR) models of TB incidence and analyse its seasonality in South Africa. TB incidence cases data from January 2010 to December 2015 were extracted from the Eastern Cape Health facility report of the electronic Tuberculosis Register (ERT.Net). A SARIMA model and a combined model of SARIMA model and a neural network auto-regression (SARIMA-NNAR) model were used in analysing and predicting the TB data from 2010 to 2015. Simulation performance parameters of mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), mean percent error (MPE), mean absolute scaled error (MASE) and mean absolute percentage error (MAPE) were applied to assess the better performance of prediction between the models. Though practically, both models could predict TB incidence, the combined model displayed better performance. For the combined model, the Akaike information criterion (AIC), second-order AIC (AICc) and Bayesian information criterion (BIC) are 288.56, 308.31 and 299.09 respectively, which were lower than the SARIMA model with corresponding values of 329.02, 327.20 and 341.99, respectively. The seasonality trend of TB incidence was forecast to have a slightly increased seasonal TB incidence trend from the SARIMA-NNAR model compared to the single model. The combined model indicated a better TB incidence forecasting with a lower AICc. The model also indicates the need for resolute intervention to reduce infectious disease transmission with co-infection with HIV and other concomitant diseases, and also at festival peak periods.

European regional efficiency and geographical externalities: a spatial nonparametric frontier analysis

NASA Astrophysics Data System (ADS)

Ramajo, Julián; Cordero, José Manuel; Márquez, Miguel Ángel

2017-10-01

This paper analyses region-level technical efficiency in nine European countries over the 1995-2007 period. We propose the application of a nonparametric conditional frontier approach to account for the presence of heterogeneous conditions in the form of geographical externalities. Such environmental factors are beyond the control of regional authorities, but may affect the production function. Therefore, they need to be considered in the frontier estimation. Specifically, a spatial autoregressive term is included as an external conditioning factor in a robust order- m model. Thus we can test the hypothesis of non-separability (the external factor impacts both the input-output space and the distribution of efficiencies), demonstrating the existence of significant global interregional spillovers into the production process. Our findings show that geographical externalities affect both the frontier level and the probability of being more or less efficient. Specifically, the results support the fact that the spatial lag variable has an inverted U-shaped non-linear impact on the performance of regions. This finding can be interpreted as a differential effect of interregional spillovers depending on the size of the neighboring economies: positive externalities for small values, possibly related to agglomeration economies, and negative externalities for high values, indicating the possibility of production congestion. Additionally, evidence of the existence of a strong geographic pattern of European regional efficiency is reported and the levels of technical efficiency are acknowledged to have converged during the period under analysis.

Non-parametric directionality analysis - Extension for removal of a single common predictor and application to time series.

PubMed

Halliday, David M; Senik, Mohd Harizal; Stevenson, Carl W; Mason, Rob

2016-08-01

The ability to infer network structure from multivariate neuronal signals is central to computational neuroscience. Directed network analyses typically use parametric approaches based on auto-regressive (AR) models, where networks are constructed from estimates of AR model parameters. However, the validity of using low order AR models for neurophysiological signals has been questioned. A recent article introduced a non-parametric approach to estimate directionality in bivariate data, non-parametric approaches are free from concerns over model validity. We extend the non-parametric framework to include measures of directed conditional independence, using scalar measures that decompose the overall partial correlation coefficient summatively by direction, and a set of functions that decompose the partial coherence summatively by direction. A time domain partial correlation function allows both time and frequency views of the data to be constructed. The conditional independence estimates are conditioned on a single predictor. The framework is applied to simulated cortical neuron networks and mixtures of Gaussian time series data with known interactions. It is applied to experimental data consisting of local field potential recordings from bilateral hippocampus in anaesthetised rats. The framework offers a non-parametric approach to estimation of directed interactions in multivariate neuronal recordings, and increased flexibility in dealing with both spike train and time series data. The framework offers a novel alternative non-parametric approach to estimate directed interactions in multivariate neuronal recordings, and is applicable to spike train and time series data. Copyright © 2016 Elsevier B.V. All rights reserved.

Two Different Points of View through Artificial Intelligence and Vector Autoregressive Models for Ex Post and Ex Ante Forecasting

PubMed Central

Aydin, Alev Dilek; Caliskan Cavdar, Seyma

2015-01-01

The ANN method has been applied by means of multilayered feedforward neural networks (MLFNs) by using different macroeconomic variables such as the exchange rate of USD/TRY, gold prices, and the Borsa Istanbul (BIST) 100 index based on monthly data over the period of January 2000 and September 2014 for Turkey. Vector autoregressive (VAR) method has also been applied with the same variables for the same period of time. In this study, different from other studies conducted up to the present, ENCOG machine learning framework has been used along with JAVA programming language in order to constitute the ANN. The training of network has been done by resilient propagation method. The ex post and ex ante estimates obtained by the ANN method have been compared with the results obtained by the econometric forecasting method of VAR. Strikingly, our findings based on the ANN method reveal that there is a possibility of financial distress or a financial crisis in Turkey starting from October 2017. The results which were obtained with the method of VAR also support the results of ANN method. Additionally, our results indicate that the ANN approach has more superior prediction performance than the VAR method. PMID:26550010

Two Different Points of View through Artificial Intelligence and Vector Autoregressive Models for Ex Post and Ex Ante Forecasting.

PubMed

Aydin, Alev Dilek; Caliskan Cavdar, Seyma

2015-01-01

The ANN method has been applied by means of multilayered feedforward neural networks (MLFNs) by using different macroeconomic variables such as the exchange rate of USD/TRY, gold prices, and the Borsa Istanbul (BIST) 100 index based on monthly data over the period of January 2000 and September 2014 for Turkey. Vector autoregressive (VAR) method has also been applied with the same variables for the same period of time. In this study, different from other studies conducted up to the present, ENCOG machine learning framework has been used along with JAVA programming language in order to constitute the ANN. The training of network has been done by resilient propagation method. The ex post and ex ante estimates obtained by the ANN method have been compared with the results obtained by the econometric forecasting method of VAR. Strikingly, our findings based on the ANN method reveal that there is a possibility of financial distress or a financial crisis in Turkey starting from October 2017. The results which were obtained with the method of VAR also support the results of ANN method. Additionally, our results indicate that the ANN approach has more superior prediction performance than the VAR method.

An Analysis of Second-Order Autoshaping

ERIC Educational Resources Information Center

Ward-Robinson, Jasper

2004-01-01

Three mechanisms can explain second-order conditioning: (1) The second-order conditioned stimulus (CS2) could activate a representation of the first-order conditioned stimulus (CS1), thereby provoking the conditioned response (CR); The CS2 could enter into an excitatory association with either (2) the representation governing the CR, or (3) with a…

Prediction of Muscle Performance During Dynamic Repetitive Exercise

NASA Technical Reports Server (NTRS)

Byerly, D. L.; Byerly, K. A.; Sognier, M. A.; Squires, W. G.

2002-01-01

A method for predicting human muscle performance was developed. Eight test subjects performed a repetitive dynamic exercise to failure using a Lordex spinal machine. Electromyography (EMG) data was collected from the erector spinae. Evaluation of the EMG data using a 5th order Autoregressive (AR) model and statistical regression analysis revealed that an AR parameter, the mean average magnitude of AR poles, can predict performance to failure as early as the second repetition of the exercise. Potential applications to the space program include evaluating on-orbit countermeasure effectiveness, maximizing post-flight recovery, and future real-time monitoring capability during Extravehicular Activity.

Stochastic-Constraints Method in Nonstationary Hot-Clutter Cancellation Part I: Fundamentals and Supervised Training Applications

DTIC Science & Technology

2003-04-01

any of the P interfering sources, and Hkt i (1) (P)] T is defined below. The P-variate vector = t kt , • t J consists of complex waveforms radiated by...line. More precisely, the (i, j ) t element of the matrix Hke is a complex 4-4 coefficient which is practically constant over the kth PRI, and is a...multivariate auto-regressive (AR) model of order n: Ykt + Z Bj Yk- j , t = tkt (25) j =l In the above equation, Bj are the M-variate matrices which are the

ARMAX-Based Transfer Function Model Identification Using Wide-Area Measurement for Adaptive and Coordinated Damping Control

DOE PAGES

Liu, Hesen; Zhu, Lin; Pan, Zhuohong; ...

2015-09-14

One of the main drawbacks of the existing oscillation damping controllers that are designed based on offline dynamic models is adaptivity to the power system operating condition. With the increasing availability of wide-area measurements and the rapid development of system identification techniques, it is possible to identify a measurement-based transfer function model online that can be used to tune the oscillation damping controller. Such a model could capture all dominant oscillation modes for adaptive and coordinated oscillation damping control. our paper describes a comprehensive approach to identify a low-order transfer function model of a power system using a multi-input multi-outputmore » (MIMO) autoregressive moving average exogenous (ARMAX) model. This methodology consists of five steps: 1) input selection; 2) output selection; 3) identification trigger; 4) model estimation; and 5) model validation. The proposed method is validated by using ambient data and ring-down data in the 16-machine 68-bus Northeast Power Coordinating Council system. Our results demonstrate that the measurement-based model using MIMO ARMAX can capture all the dominant oscillation modes. Compared with the MIMO subspace state space model, the MIMO ARMAX model has equivalent accuracy but lower order and improved computational efficiency. The proposed model can be applied for adaptive and coordinated oscillation damping control.« less

Autoregressive Methods for Spectral Estimation from Interferograms.

DTIC Science & Technology

1986-09-19

RL83 6?6 AUTOREGRESSIVE METHODS FOR SPECTRAL. ESTIMTION FROM / SPACE ENGINEERING E N RICHARDS ET AL. 19 SEPINEFRGAS.()UA TT NV GNCNE O C: 31SSF...was AUG1085 performed under subcontract to . Center for Space Engineering Utah State University Logan, UT 84322-4140 4 4 Scientific Report No. 17 AFGL...MONITORING ORGANIZATION Center for Space Engineering (iapplicable) Air Force Geophysics Laboratory e. AORESS (City. State and ZIP Code) 7b. AOORESS (City

Foreign Exchange Value-at-Risk with Multiple Currency Exposure: A Multivariate and Copula Generalized Autoregressive Conditional Heteroskedasticity Approach

DTIC Science & Technology

2014-11-01

du taux de change, et les responsables de la gestion interne se voient donc pressés de trouver des ... mesurer les effets négatifs que peuvent avoir les fluctuations mo- nétaires sur le budget et la planification du MDN, il faut connaître le poids des ...qualités comparables et qu’ils permettent d’effectuer une meilleure évaluation du risque qu’avec la méthode courante. On obtient désormais des estimations de

Gaussian Process Autoregression for Simultaneous Proportional Multi-Modal Prosthetic Control With Natural Hand Kinematics.

PubMed

Xiloyannis, Michele; Gavriel, Constantinos; Thomik, Andreas A C; Faisal, A Aldo

2017-10-01

Matching the dexterity, versatility, and robustness of the human hand is still an unachieved goal in bionics, robotics, and neural engineering. A major limitation for hand prosthetics lies in the challenges of reliably decoding user intention from muscle signals when controlling complex robotic hands. Most of the commercially available prosthetic hands use muscle-related signals to decode a finite number of predefined motions and some offer proportional control of open/close movements of the whole hand. Here, in contrast, we aim to offer users flexible control of individual joints of their artificial hand. We propose a novel framework for decoding neural information that enables a user to independently control 11 joints of the hand in a continuous manner-much like we control our natural hands. Toward this end, we instructed six able-bodied subjects to perform everyday object manipulation tasks combining both dynamic, free movements (e.g., grasping) and isometric force tasks (e.g., squeezing). We recorded the electromyographic and mechanomyographic activities of five extrinsic muscles of the hand in the forearm, while simultaneously monitoring 11 joints of hand and fingers using a sensorized data glove that tracked the joints of the hand. Instead of learning just a direct mapping from current muscle activity to intended hand movement, we formulated a novel autoregressive approach that combines the context of previous hand movements with instantaneous muscle activity to predict future hand movements. Specifically, we evaluated a linear vector autoregressive moving average model with exogenous inputs and a novel Gaussian process ( ) autoregressive framework to learn the continuous mapping from hand joint dynamics and muscle activity to decode intended hand movement. Our approach achieves high levels of performance (RMSE of 8°/s and ). Crucially, we use a small set of sensors that allows us to control a larger set of independently actuated degrees of freedom of a hand. This novel undersensored control is enabled through the combination of nonlinear autoregressive continuous mapping between muscle activity and joint angles. The system evaluates the muscle signals in the context of previous natural hand movements. This enables us to resolve ambiguities in situations, where muscle signals alone cannot determine the correct action as we evaluate the muscle signals in their context of natural hand movements. autoregression is a particularly powerful approach which makes not only a prediction based on the context but also represents the associated uncertainty of its predictions, thus enabling the novel notion of risk-based control in neuroprosthetics. Our results suggest that autoregressive approaches with exogenous inputs lend themselves for natural, intuitive, and continuous control in neurotechnology, with the particular focus on prosthetic restoration of natural limb function, where high dexterity is required for complex movements.

Climate variations and salmonellosis transmission in Adelaide, South Australia: a comparison between regression models

NASA Astrophysics Data System (ADS)

Zhang, Ying; Bi, Peng; Hiller, Janet

2008-01-01

This is the first study to identify appropriate regression models for the association between climate variation and salmonellosis transmission. A comparison between different regression models was conducted using surveillance data in Adelaide, South Australia. By using notified salmonellosis cases and climatic variables from the Adelaide metropolitan area over the period 1990-2003, four regression methods were examined: standard Poisson regression, autoregressive adjusted Poisson regression, multiple linear regression, and a seasonal autoregressive integrated moving average (SARIMA) model. Notified salmonellosis cases in 2004 were used to test the forecasting ability of the four models. Parameter estimation, goodness-of-fit and forecasting ability of the four regression models were compared. Temperatures occurring 2 weeks prior to cases were positively associated with cases of salmonellosis. Rainfall was also inversely related to the number of cases. The comparison of the goodness-of-fit and forecasting ability suggest that the SARIMA model is better than the other three regression models. Temperature and rainfall may be used as climatic predictors of salmonellosis cases in regions with climatic characteristics similar to those of Adelaide. The SARIMA model could, thus, be adopted to quantify the relationship between climate variations and salmonellosis transmission.

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

PubMed

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

2016-01-01

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

Chaotic Oscillations of Second Order Linear Hyperbolic Equations with Nonlinear Boundary Conditions: A Factorizable but Noncommutative Case

NASA Astrophysics Data System (ADS)

Li, Liangliang; Huang, Yu; Chen, Goong; Huang, Tingwen

If a second order linear hyperbolic partial differential equation in one-space dimension can be factorized as a product of two first order operators and if the two first order operators commute, with one boundary condition being the van der Pol type and the other being linear, one can establish the occurrence of chaos when the parameters enter a certain regime [Chen et al., 2014]. However, if the commutativity of the two first order operators fails to hold, then the treatment in [Chen et al., 2014] no longer works and significant new challenges arise in determining nonlinear boundary conditions that engenders chaos. In this paper, we show that by incorporating a linear memory effect, a nonlinear van der Pol boundary condition can cause chaotic oscillations when the parameter enters a certain regime. Numerical simulations illustrating chaotic oscillations are also presented.

A robust damage-detection technique with environmental variability combining time-series models with principal components

NASA Astrophysics Data System (ADS)

Lakshmi, K.; Rama Mohan Rao, A.

2014-10-01

In this paper, a novel output-only damage-detection technique based on time-series models for structural health monitoring in the presence of environmental variability and measurement noise is presented. The large amount of data obtained in the form of time-history response is transformed using principal component analysis, in order to reduce the data size and thereby improve the computational efficiency of the proposed algorithm. The time instant of damage is obtained by fitting the acceleration time-history data from the structure using autoregressive (AR) and AR with exogenous inputs time-series prediction models. The probability density functions (PDFs) of damage features obtained from the variances of prediction errors corresponding to references and healthy current data are found to be shifting from each other due to the presence of various uncertainties such as environmental variability and measurement noise. Control limits using novelty index are obtained using the distances of the peaks of the PDF curves in healthy condition and used later for determining the current condition of the structure. Numerical simulation studies have been carried out using a simply supported beam and also validated using an experimental benchmark data corresponding to a three-storey-framed bookshelf structure proposed by Los Alamos National Laboratory. Studies carried out in this paper clearly indicate the efficiency of the proposed algorithm for damage detection in the presence of measurement noise and environmental variability.

Time-varying correlations in global real estate markets: A multivariate GARCH with spatial effects approach

NASA Astrophysics Data System (ADS)

Gu, Huaying; Liu, Zhixue; Weng, Yingliang

2017-04-01

The present study applies the multivariate generalized autoregressive conditional heteroscedasticity (MGARCH) with spatial effects approach for the analysis of the time-varying conditional correlations and contagion effects among global real estate markets. A distinguishing feature of the proposed model is that it can simultaneously capture the spatial interactions and the dynamic conditional correlations compared with the traditional MGARCH models. Results reveal that the estimated dynamic conditional correlations have exhibited significant increases during the global financial crisis from 2007 to 2009, thereby suggesting contagion effects among global real estate markets. The analysis further indicates that the returns of the regional real estate markets that are in close geographic and economic proximities exhibit strong co-movement. In addition, evidence of significantly positive leverage effects in global real estate markets is also determined. The findings have significant implications on global portfolio diversification opportunities and risk management practices.

Anomalous Fluctuations in Autoregressive Models with Long-Term Memory

NASA Astrophysics Data System (ADS)

Sakaguchi, Hidetsugu; Honjo, Haruo

2015-10-01

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

On the Stationarity of Multiple Autoregressive Approximants: Theory and Algorithms

DTIC Science & Technology

1976-08-01

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

Equilibrium Policy Proposals with Abstentions.

DTIC Science & Technology

1981-05-01

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

Improvements to surrogate data methods for nonstationary time series.

PubMed

Lucio, J H; Valdés, R; Rodríguez, L R

2012-05-01

The method of surrogate data has been extensively applied to hypothesis testing of system linearity, when only one realization of the system, a time series, is known. Normally, surrogate data should preserve the linear stochastic structure and the amplitude distribution of the original series. Classical surrogate data methods (such as random permutation, amplitude adjusted Fourier transform, or iterative amplitude adjusted Fourier transform) are successful at preserving one or both of these features in stationary cases. However, they always produce stationary surrogates, hence existing nonstationarity could be interpreted as dynamic nonlinearity. Certain modifications have been proposed that additionally preserve some nonstationarity, at the expense of reproducing a great deal of nonlinearity. However, even those methods generally fail to preserve the trend (i.e., global nonstationarity in the mean) of the original series. This is the case of time series with unit roots in their autoregressive structure. Additionally, those methods, based on Fourier transform, either need first and last values in the original series to match, or they need to select a piece of the original series with matching ends. These conditions are often inapplicable and the resulting surrogates are adversely affected by the well-known artefact problem. In this study, we propose a simple technique that, applied within existing Fourier-transform-based methods, generates surrogate data that jointly preserve the aforementioned characteristics of the original series, including (even strong) trends. Moreover, our technique avoids the negative effects of end mismatch. Several artificial and real, stationary and nonstationary, linear and nonlinear time series are examined, in order to demonstrate the advantages of the methods. Corresponding surrogate data are produced with the classical and with the proposed methods, and the results are compared.

Characterization of the spatial variability of channel morphology

USGS Publications Warehouse

Moody, J.A.; Troutman, B.M.

2002-01-01

The spatial variability of two fundamental morphological variables is investigated for rivers having a wide range of discharge (five orders of magnitude). The variables, water-surface width and average depth, were measured at 58 to 888 equally spaced cross-sections in channel links (river reaches between major tributaries). These measurements provide data to characterize the two-dimensional structure of a channel link which is the fundamental unit of a channel network. The morphological variables have nearly log-normal probability distributions. A general relation was determined which relates the means of the log-transformed variables to the logarithm of discharge similar to previously published downstream hydraulic geometry relations. The spatial variability of the variables is described by two properties: (1) the coefficient of variation which was nearly constant (0.13-0.42) over a wide range of discharge; and (2) the integral length scale in the downstream direction which was approximately equal to one to two mean channel widths. The joint probability distribution of the morphological variables in the downstream direction was modelled as a first-order, bivariate autoregressive process. This model accounted for up to 76 per cent of the total variance. The two-dimensional morphological variables can be scaled such that the channel width-depth process is independent of discharge. The scaling properties will be valuable to modellers of both basin and channel dynamics. Published in 2002 John Wiley and Sons, Ltd.

Construction of Covariance Functions with Variable Length Fields

NASA Technical Reports Server (NTRS)

Gaspari, Gregory; Cohn, Stephen E.; Guo, Jing; Pawson, Steven

2005-01-01

This article focuses on construction, directly in physical space, of three-dimensional covariance functions parametrized by a tunable length field, and on an application of this theory to reproduce the Quasi-Biennial Oscillation (QBO) in the Goddard Earth Observing System, Version 4 (GEOS-4) data assimilation system. These Covariance models are referred to as multi-level or nonseparable, to associate them with the application where a multi-level covariance with a large troposphere to stratosphere length field gradient is used to reproduce the QBO from sparse radiosonde observations in the tropical lower stratosphere. The multi-level covariance functions extend well-known single level covariance functions depending only on a length scale. Generalizations of the first- and third-order autoregressive covariances in three dimensions are given, providing multi-level covariances with zero and three derivatives at zero separation, respectively. Multi-level piecewise rational covariances with two continuous derivatives at zero separation are also provided. Multi-level powerlaw covariances are constructed with continuous derivatives of all orders. Additional multi-level covariance functions are constructed using the Schur product of single and multi-level covariance functions. A multi-level powerlaw covariance used to reproduce the QBO in GEOS-4 is described along with details of the assimilation experiments. The new covariance model is shown to represent the vertical wind shear associated with the QBO much more effectively than in the baseline GEOS-4 system.

Assessment of cardio-respiratory interactions in preterm infants by bivariate autoregressive modeling and surrogate data analysis.

PubMed

Indic, Premananda; Bloch-Salisbury, Elisabeth; Bednarek, Frank; Brown, Emery N; Paydarfar, David; Barbieri, Riccardo

2011-07-01

Cardio-respiratory interactions are weak at the earliest stages of human development, suggesting that assessment of their presence and integrity may be an important indicator of development in infants. Despite the valuable research devoted to infant development, there is still a need for specifically targeted standards and methods to assess cardiopulmonary functions in the early stages of life. We present a new methodological framework for the analysis of cardiovascular variables in preterm infants. Our approach is based on a set of mathematical tools that have been successful in quantifying important cardiovascular control mechanisms in adult humans, here specifically adapted to reflect the physiology of the developing cardiovascular system. We applied our methodology in a study of cardio-respiratory responses for 11 preterm infants. We quantified cardio-respiratory interactions using specifically tailored multivariate autoregressive analysis and calculated the coherence as well as gain using causal approaches. The significance of the interactions in each subject was determined by surrogate data analysis. The method was tested in control conditions as well as in two different experimental conditions; with and without use of mild mechanosensory intervention. Our multivariate analysis revealed a significantly higher coherence, as confirmed by surrogate data analysis, in the frequency range associated with eupneic breathing compared to the other ranges. Our analysis validates the models behind our new approaches, and our results confirm the presence of cardio-respiratory coupling in early stages of development, particularly during periods of mild mechanosensory intervention, thus encouraging further application of our approach. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

The dynamic correlation between policy uncertainty and stock market returns in China

NASA Astrophysics Data System (ADS)

Yang, Miao; Jiang, Zhi-Qiang

2016-11-01

The dynamic correlation is examined between government's policy uncertainty and Chinese stock market returns in the period from January 1995 to December 2014. We find that the stock market is significantly correlated to policy uncertainty based on the results of the Vector Auto Regression (VAR) and Structural Vector Auto Regression (SVAR) models. In contrast, the results of the Dynamic Conditional Correlation Generalized Multivariate Autoregressive Conditional Heteroscedasticity (DCC-MGARCH) model surprisingly show a low dynamic correlation coefficient between policy uncertainty and market returns, suggesting that the fluctuations of each variable are greatly influenced by their values in the preceding period. Our analysis highlights the understanding of the dynamical relationship between stock market and fiscal and monetary policy.

Model inversion via multi-fidelity Bayesian optimization: a new paradigm for parameter estimation in haemodynamics, and beyond.

PubMed

Perdikaris, Paris; Karniadakis, George Em

2016-05-01

We present a computational framework for model inversion based on multi-fidelity information fusion and Bayesian optimization. The proposed methodology targets the accurate construction of response surfaces in parameter space, and the efficient pursuit to identify global optima while keeping the number of expensive function evaluations at a minimum. We train families of correlated surrogates on available data using Gaussian processes and auto-regressive stochastic schemes, and exploit the resulting predictive posterior distributions within a Bayesian optimization setting. This enables a smart adaptive sampling procedure that uses the predictive posterior variance to balance the exploration versus exploitation trade-off, and is a key enabler for practical computations under limited budgets. The effectiveness of the proposed framework is tested on three parameter estimation problems. The first two involve the calibration of outflow boundary conditions of blood flow simulations in arterial bifurcations using multi-fidelity realizations of one- and three-dimensional models, whereas the last one aims to identify the forcing term that generated a particular solution to an elliptic partial differential equation. © 2016 The Author(s).

Model inversion via multi-fidelity Bayesian optimization: a new paradigm for parameter estimation in haemodynamics, and beyond

PubMed Central

Perdikaris, Paris; Karniadakis, George Em

2016-01-01

We present a computational framework for model inversion based on multi-fidelity information fusion and Bayesian optimization. The proposed methodology targets the accurate construction of response surfaces in parameter space, and the efficient pursuit to identify global optima while keeping the number of expensive function evaluations at a minimum. We train families of correlated surrogates on available data using Gaussian processes and auto-regressive stochastic schemes, and exploit the resulting predictive posterior distributions within a Bayesian optimization setting. This enables a smart adaptive sampling procedure that uses the predictive posterior variance to balance the exploration versus exploitation trade-off, and is a key enabler for practical computations under limited budgets. The effectiveness of the proposed framework is tested on three parameter estimation problems. The first two involve the calibration of outflow boundary conditions of blood flow simulations in arterial bifurcations using multi-fidelity realizations of one- and three-dimensional models, whereas the last one aims to identify the forcing term that generated a particular solution to an elliptic partial differential equation. PMID:27194481

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

PubMed

Mocan, N H

1990-01-01

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

Fisher Consistency of AM-Estimates of the Autoregression Parameter Using Hard Rejection Filter Cleaners

DTIC Science & Technology

1987-02-04

U5tr,)! P(U 5-t Since U - F with F RS, we get (3.1). Case b: 0 S 5 k -a Now P([U~t]riM) = P(UZk-a) and P([ Ugt ]rM) = P(US-k-a) S P(US-(k-a)) which again...robustness for autoregressive processes." The Annals of Statistics, 12, 843-863. Mallows, C.L. (1980). "Some theory of nonlinear smoothen." The Annals of

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

PubMed

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

2011-11-01

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

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.

Acceleration and Velocity Sensing from Measured Strain

NASA Technical Reports Server (NTRS)

Pak, Chan-Gi; Truax, Roger

2016-01-01

A simple approach for computing acceleration and velocity of a structure from the strain is proposed in this study. First, deflection and slope of the structure are computed from the strain using a two-step theory. Frequencies of the structure are computed from the time histories of strain using a parameter estimation technique together with an Autoregressive Moving Average model. From deflection, slope, and frequencies of the structure, acceleration and velocity of the structure can be obtained using the proposed approach. shape sensing, fiber optic strain sensor, system equivalent reduction and expansion process.

Unsteady Aerodynamic Force Sensing from Strain Data

NASA Technical Reports Server (NTRS)

Pak, Chan-Gi

2017-01-01

A simple approach for computing unsteady aerodynamic forces from simulated measured strain data is proposed in this study. First, the deflection and slope of the structure are computed from the unsteady strain using the two-step approach. Velocities and accelerations of the structure are computed using the autoregressive moving average model, on-line parameter estimator, low-pass filter, and a least-squares curve fitting method together with analytical derivatives with respect to time. Finally, aerodynamic forces over the wing are computed using modal aerodynamic influence coefficient matrices, a rational function approximation, and a time-marching algorithm.

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.

Missing in space: an evaluation of imputation methods for missing data in spatial analysis of risk factors for type II diabetes.

PubMed

Baker, Jannah; White, Nicole; Mengersen, Kerrie

2014-11-20

Spatial analysis is increasingly important for identifying modifiable geographic risk factors for disease. However, spatial health data from surveys are often incomplete, ranging from missing data for only a few variables, to missing data for many variables. For spatial analyses of health outcomes, selection of an appropriate imputation method is critical in order to produce the most accurate inferences. We present a cross-validation approach to select between three imputation methods for health survey data with correlated lifestyle covariates, using as a case study, type II diabetes mellitus (DM II) risk across 71 Queensland Local Government Areas (LGAs). We compare the accuracy of mean imputation to imputation using multivariate normal and conditional autoregressive prior distributions. Choice of imputation method depends upon the application and is not necessarily the most complex method. Mean imputation was selected as the most accurate method in this application. Selecting an appropriate imputation method for health survey data, after accounting for spatial correlation and correlation between covariates, allows more complete analysis of geographic risk factors for disease with more confidence in the results to inform public policy decision-making.

The asymmetry of U.S. monetary policy: Evidence from a threshold Taylor rule with time-varying threshold values

NASA Astrophysics Data System (ADS)

Zhu, Yanli; Chen, Haiqiang

2017-05-01

In this paper, we revisit the issue whether U.S. monetary policy is asymmetric by estimating a forward-looking threshold Taylor rule with quarterly data from 1955 to 2015. In order to capture the potential heterogeneity for regime shift mechanism under different economic conditions, we modify the threshold model by assuming the threshold value as a latent variable following an autoregressive (AR) dynamic process. We use the unemployment rate as the threshold variable and separate the sample into two periods: expansion periods and recession periods. Our findings support that the U.S. monetary policy operations are asymmetric in these two regimes. More precisely, the monetary authority tends to implement an active Taylor rule with a weaker response to the inflation gap (the deviation of inflation from its target) and a stronger response to the output gap (the deviation of output from its potential level) in recession periods. The threshold value, interpreted as the targeted unemployment rate of monetary authorities, exhibits significant time-varying properties, confirming the conjecture that policy makers may adjust their reference point for the unemployment rate accordingly to reflect their attitude on the health of general economy.

CFD determination of flow perturbation boundary conditions for seal rotordynamic modeling

NASA Astrophysics Data System (ADS)

Venkatesan, Ganesh

2002-09-01

A new approach has been developed and utilized to determine the flow field perturbations (i.e. disturbance due to rotor eccentricity and/or motion) upstream of and within a non-contacting seal. The results are proposed for use with bulk-flow perturbation and CFD-perturbation seal rotordynamic models, as well as in fully 3-D CFD models, to specify approximate boundary conditions for the first-order variables at the computational domain inlet. The perturbation quantities were evaluated by subtracting the numerical flow field solutions corresponding to the concentric rotor position from that for an eccentric rotor position. The disturbance pressure quantities predicted from the numerical solutions were validated by comparing with previous pressure measurements. A parametric study was performed to understand the influence of upstream chamber height, seal clearance, shaft speed, whirl speed, zeroth-order streamwise and swirl velocities, and downstream pressure on the distribution of the first-order quantities in the upstream chamber, seal inlet and seal exit regions. Radially bulk-averaged first-order quantities were evaluated in the upstream chamber, as well as at the seal inlet and exit. The results were finally presented in the form of generalized dimensionless boundary condition correlations so that they can be applied to seal rotordynamic models over a wide range of operating conditions and geometries. To examine the effect of the proposed, approximate first-order boundary conditions on the solutions of the fully 3-D CFD rotordynamic models, the first-order boundary condition correlations for the upstream chamber were used to adjust the circumferential distribution of domain inlet values. The benefit of the boundary condition expressions was assessed for two previously measured test cases, one for a gas seal and the other for a liquid seal. For the gas seal case, a significant improvement in the prediction of the cross-coupled stiffness, when including the proposed first-order inlet boundary values, was found. In the case of liquid seals the tangential impedance values obtained with boundary condition adjustments showed a very slight improvement for a range of whirl speeds over those obtained without them. The radial impedance values obtained with the new adjustments showed a significant improvement over those obtained without them.

Pre-conception energy balance and secondary sex ratio--partial support for the Trivers-Willard hypothesis in dairy cows.

PubMed

Roche, J R; Lee, J M; Berry, D P

2006-06-01

According to the Trivers-Willard hypothesis, maternal condition at or around conception affects the secondary sex ratio in mammals. However, there are little or no data available on indicators of maternal condition in dairy cows on the sex of the resultant offspring. A total of 76,607 body condition score (BCS; scale of 1 to 5) records and 76,611 body weight (BW) records from 3,209 lactations across 1,172 cows were extracted from a research database collated from one research herd between 1986 and 2004, inclusive. Exclusion of multiple births and cows with no information before calving (e.g., nulliparous animals) resulted in 2,029 records with BCS and BW observations from the previous calving, and 2,002 and 1,872 lactations with BCS and BW observations at conception and midgestation, respectively. Change in BCS and BW between calving and conception and between conception and midgestation was calculated per lactation. Generalized estimating equations were used to model the logit of the probability of a male calf, in which cow was included as a repeated effect with a first-order autoregressive correlation structure assumed among records within cow. Of the BCS variables investigated, there was a linear relationship between the logit of the probability of a male calf and BCS change between calving and conception, the rate of BCS change over this period (BCS divided by days in milk), and BCS at the calving event immediately before conception. The birth of a bull calf was 1.85 times more likely in cows that lost no BCS from calving to conception compared with cows that lost one BCS unit from calving to conception. This increase in odds was equivalent to a 14% unit increase in the probability of a male calf (from 54 to 68%). The amount of BW lost between calving and conception and the rate of loss affected the sex of the resultant offspring. Less BW loss or greater BW gain between calving and conception was associated with greater likelihood of a male calf. Results suggested a positive effect of pre-conception BCS and BW change on secondary sex ratio, agreeing with the Trivers-Willard hypothesis that females in good physiological condition are more likely to produce male offspring.

Real-time spectral analysis of HRV signals: an interactive and user-friendly PC system.

PubMed

Basano, L; Canepa, F; Ottonello, P

1998-01-01

We present a real-time system, built around a PC and a low-cost data acquisition board, for the spectral analysis of the heart rate variability signal. The Windows-like operating environment on which it is based makes the computer program very user-friendly even for non-specialized personnel. The Power Spectral Density is computed through the use of a hybrid method, in which a classical FFT analysis follows an autoregressive finite-extension of data; the stationarity of the sequence is continuously checked. The use of this algorithm gives a high degree of robustness of the spectral estimation. Moreover, always in real time, the FFT of every data block is computed and displayed in order to corroborate the results as well as to allow the user to interactively choose a proper AR model order.

Assessing First- and Second-Order Equity for the Common-Item Nonequivalent Groups Design Using Multidimensional IRT

ERIC Educational Resources Information Center

Andrews, Benjamin James

2011-01-01

The equity properties can be used to assess the quality of an equating. The degree to which expected scores conditional on ability are similar between test forms is referred to as first-order equity. Second-order equity is the degree to which conditional standard errors of measurement are similar between test forms after equating. The purpose of…

Distributed parameter system coupled ARMA expansion identification and adaptive parallel IIR filtering - A unified problem statement. [Auto Regressive Moving-Average

NASA Technical Reports Server (NTRS)

Johnson, C. R., Jr.; Balas, M. J.

1980-01-01

A novel interconnection of distributed parameter system (DPS) identification and adaptive filtering is presented, which culminates in a common statement of coupled autoregressive, moving-average expansion or parallel infinite impulse response configuration adaptive parameterization. The common restricted complexity filter objectives are seen as similar to the reduced-order requirements of the DPS expansion description. The interconnection presents the possibility of an exchange of problem formulations and solution approaches not yet easily addressed in the common finite dimensional lumped-parameter system context. It is concluded that the shared problems raised are nevertheless many and difficult.

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

NASA Astrophysics Data System (ADS)

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

2017-09-01

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

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

NASA Astrophysics Data System (ADS)

Wang, Qijie

2015-08-01

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

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

PubMed Central

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

2009-01-01

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

Adaptive clutter rejection filters for airborne Doppler weather radar applied to the detection of low altitude windshear

NASA Technical Reports Server (NTRS)

Keel, Byron M.

1989-01-01

An optimum adaptive clutter rejection filter for use with airborne Doppler weather radar is presented. The radar system is being designed to operate at low-altitudes for the detection of windshear in an airport terminal area where ground clutter returns may mask the weather return. The coefficients of the adaptive clutter rejection filter are obtained using a complex form of a square root normalized recursive least squares lattice estimation algorithm which models the clutter return data as an autoregressive process. The normalized lattice structure implementation of the adaptive modeling process for determining the filter coefficients assures that the resulting coefficients will yield a stable filter and offers possible fixed point implementation. A 10th order FIR clutter rejection filter indexed by geographical location is designed through autoregressive modeling of simulated clutter data. Filtered data, containing simulated dry microburst and clutter return, are analyzed using pulse-pair estimation techniques. To measure the ability of the clutter rejection filters to remove the clutter, results are compared to pulse-pair estimates of windspeed within a simulated dry microburst without clutter. In the filter evaluation process, post-filtered pulse-pair width estimates and power levels are also used to measure the effectiveness of the filters. The results support the use of an adaptive clutter rejection filter for reducing the clutter induced bias in pulse-pair estimates of windspeed.

Modeling volatility using state space models.

PubMed

Timmer, J; Weigend, A S

1997-08-01

In time series problems, noise can be divided into two categories: dynamic noise which drives the process, and observational noise which is added in the measurement process, but does not influence future values of the system. In this framework, we show that empirical volatilities (the squared relative returns of prices) exhibit a significant amount of observational noise. To model and predict their time evolution adequately, we estimate state space models that explicitly include observational noise. We obtain relaxation times for shocks in the logarithm of volatility ranging from three weeks (for foreign exchange) to three to five months (for stock indices). In most cases, a two-dimensional hidden state is required to yield residuals that are consistent with white noise. We compare these results with ordinary autoregressive models (without a hidden state) and find that autoregressive models underestimate the relaxation times by about two orders of magnitude since they do not distinguish between observational and dynamic noise. This new interpretation of the dynamics of volatility in terms of relaxators in a state space model carries over to stochastic volatility models and to GARCH models, and is useful for several problems in finance, including risk management and the pricing of derivative securities. Data sets used: Olsen & Associates high frequency DEM/USD foreign exchange rates (8 years). Nikkei 225 index (40 years). Dow Jones Industrial Average (25 years).

Studying the effect of weather conditions on daily crash counts using a discrete time-series model.

PubMed

Brijs, Tom; Karlis, Dimitris; Wets, Geert

2008-05-01

In previous research, significant effects of weather conditions on car crashes have been found. However, most studies use monthly or yearly data and only few studies are available analyzing the impact of weather conditions on daily car crash counts. Furthermore, the studies that are available on a daily level do not explicitly model the data in a time-series context, hereby ignoring the temporal serial correlation that may be present in the data. In this paper, we introduce an integer autoregressive model for modelling count data with time interdependencies. The model is applied to daily car crash data, metereological data and traffic exposure data from the Netherlands aiming at examining the risk impact of weather conditions on the observed counts. The results show that several assumptions related to the effect of weather conditions on crash counts are found to be significant in the data and that if serial temporal correlation is not accounted for in the model, this may produce biased results.

Estimating short-run and long-run interaction mechanisms in interictal state.

PubMed

Ozkaya, Ata; Korürek, Mehmet

2010-04-01

We address the issue of analyzing electroencephalogram (EEG) from seizure patients in order to test, model and determine the statistical properties that distinguish between EEG states (interictal, pre-ictal, ictal) by introducing a new class of time series analysis methods. In the present study: firstly, we employ statistical methods to determine the non-stationary behavior of focal interictal epileptiform series within very short time intervals; secondly, for such intervals that are deemed non-stationary we suggest the concept of Autoregressive Integrated Moving Average (ARIMA) process modelling, well known in time series analysis. We finally address the queries of causal relationships between epileptic states and between brain areas during epileptiform activity. We estimate the interaction between different EEG series (channels) in short time intervals by performing Granger-causality analysis and also estimate such interaction in long time intervals by employing Cointegration analysis, both analysis methods are well-known in econometrics. Here we find: first, that the causal relationship between neuronal assemblies can be identified according to the duration and the direction of their possible mutual influences; second, that although the estimated bidirectional causality in short time intervals yields that the neuronal ensembles positively affect each other, in long time intervals neither of them is affected (increasing amplitudes) from this relationship. Moreover, Cointegration analysis of the EEG series enables us to identify whether there is a causal link from the interictal state to ictal state.

Exploratory wavelet analysis of dengue seasonal patterns in Colombia.

PubMed

Fernández-Niño, Julián Alfredo; Cárdenas-Cárdenas, Luz Mery; Hernández-Ávila, Juan Eugenio; Palacio-Mejía, Lina Sofía; Castañeda-Orjuela, Carlos Andrés

2015-12-04

Dengue has a seasonal behavior associated with climatic changes, vector cycles, circulating serotypes, and population dynamics. The wavelet analysis makes it possible to separate a very long time series into calendar time and periods. This is the first time this technique is used in an exploratory manner to model the behavior of dengue in Colombia.  To explore the annual seasonal dengue patterns in Colombia and in its five most endemic municipalities for the period 2007 to 2012, and for roughly annual cycles between 1978 and 2013 at the national level.  We made an exploratory wavelet analysis using data from all incident cases of dengue per epidemiological week for the period 2007 to 2012, and per year for 1978 to 2013. We used a first-order autoregressive model as the null hypothesis.  The effect of the 2010 epidemic was evident in both the national time series and the series for the five municipalities. Differences in interannual seasonal patterns were observed among municipalities. In addition, we identified roughly annual cycles of 2 to 5 years since 2004 at a national level.  Wavelet analysis is useful to study a long time series containing changing seasonal patterns, as is the case of dengue in Colombia, and to identify differences among regions. These patterns need to be explored at smaller aggregate levels, and their relationships with different predictive variables need to be investigated.

A Power-Law Growth and Decay Model with Autocorrelation for Posting Data to Social Networking Services.

PubMed

Fujiyama, Toshifumi; Matsui, Chihiro; Takemura, Akimichi

2016-01-01

We propose a power-law growth and decay model for posting data to social networking services before and after social events. We model the time series structure of deviations from the power-law growth and decay with a conditional Poisson autoregressive (AR) model. Online postings related to social events are described by five parameters in the power-law growth and decay model, each of which characterizes different aspects of interest in the event. We assess the validity of parameter estimates in terms of confidence intervals, and compare various submodels based on likelihoods and information criteria.

New methods for the condition monitoring of level crossings

NASA Astrophysics Data System (ADS)

García Márquez, Fausto Pedro; Pedregal, Diego J.; Roberts, Clive

2015-04-01

Level crossings represent a high risk for railway systems. This paper demonstrates the potential to improve maintenance management through the use of intelligent condition monitoring coupled with reliability centred maintenance (RCM). RCM combines advanced electronics, control, computing and communication technologies to address the multiple objectives of cost effectiveness, improved quality, reliability and services. RCM collects digital and analogue signals utilising distributed transducers connected to either point-to-point or digital bus communication links. Assets in many industries use data logging capable of providing post-failure diagnostic support, but to date little use has been made of combined qualitative and quantitative fault detection techniques. The research takes the hydraulic railway level crossing barrier (LCB) system as a case study and develops a generic strategy for failure analysis, data acquisition and incipient fault detection. For each barrier the hydraulic characteristics, the motor's current and voltage, hydraulic pressure and the barrier's position are acquired. In order to acquire the data at a central point efficiently, without errors, a distributed single-cable Fieldbus is utilised. This allows the connection of all sensors through the project's proprietary communication nodes to a high-speed bus. The system developed in this paper for the condition monitoring described above detects faults by means of comparing what can be considered a 'normal' or 'expected' shape of a signal with respect to the actual shape observed as new data become available. ARIMA (autoregressive integrated moving average) models were employed for detecting faults. The statistical tests known as Jarque-Bera and Ljung-Box have been considered for testing the model.

No meditation-related changes in the auditory N1 during first-time meditation.

PubMed

Barnes, L J; McArthur, G M; Biedermann, B A; de Lissa, P; Polito, V; Badcock, N A

2018-05-01

Recent studies link meditation expertise with enhanced low-level attention, measured through auditory event-related potentials (ERPs). In this study, we tested the reliability and validity of a recent finding that the N1 ERP in first-time meditators is smaller during meditation than non-meditation - an effect not present in long-term meditators. In the first experiment, we replicated the finding in first-time meditators. In two subsequent experiments, we discovered that this finding was not due to stimulus-related instructions, but was explained by an effect of the order of conditions. Extended exposure to the same tones has been linked with N1 decrement in other studies, and may explain N1 decrement across our two conditions. We give examples of existing meditation and ERP studies that may include similar condition order effects. The role of condition order among first-time meditators in this study indicates the importance of counterbalancing meditation and non-mediation conditions in meditation studies that use event-related potentials. Copyright © 2018 Elsevier B.V. All rights reserved.

Investigating Causality Between Interacting Brain Areas with Multivariate Autoregressive Models of MEG Sensor Data

PubMed Central

Michalareas, George; Schoffelen, Jan-Mathijs; Paterson, Gavin; Gross, Joachim

2013-01-01

Abstract In this work, we investigate the feasibility to estimating causal interactions between brain regions based on multivariate autoregressive models (MAR models) fitted to magnetoencephalographic (MEG) sensor measurements. We first demonstrate the theoretical feasibility of estimating source level causal interactions after projection of the sensor-level model coefficients onto the locations of the neural sources. Next, we show with simulated MEG data that causality, as measured by partial directed coherence (PDC), can be correctly reconstructed if the locations of the interacting brain areas are known. We further demonstrate, if a very large number of brain voxels is considered as potential activation sources, that PDC as a measure to reconstruct causal interactions is less accurate. In such case the MAR model coefficients alone contain meaningful causality information. The proposed method overcomes the problems of model nonrobustness and large computation times encountered during causality analysis by existing methods. These methods first project MEG sensor time-series onto a large number of brain locations after which the MAR model is built on this large number of source-level time-series. Instead, through this work, we demonstrate that by building the MAR model on the sensor-level and then projecting only the MAR coefficients in source space, the true casual pathways are recovered even when a very large number of locations are considered as sources. The main contribution of this work is that by this methodology entire brain causality maps can be efficiently derived without any a priori selection of regions of interest. Hum Brain Mapp, 2013. © 2012 Wiley Periodicals, Inc. PMID:22328419

A Deep and Autoregressive Approach for Topic Modeling of Multimodal Data.

PubMed

Zheng, Yin; Zhang, Yu-Jin; Larochelle, Hugo

2016-06-01

Topic modeling based on latent Dirichlet allocation (LDA) has been a framework of choice to deal with multimodal data, such as in image annotation tasks. Another popular approach to model the multimodal data is through deep neural networks, such as the deep Boltzmann machine (DBM). Recently, a new type of topic model called the Document Neural Autoregressive Distribution Estimator (DocNADE) was proposed and demonstrated state-of-the-art performance for text document modeling. In this work, we show how to successfully apply and extend this model to multimodal data, such as simultaneous image classification and annotation. First, we propose SupDocNADE, a supervised extension of DocNADE, that increases the discriminative power of the learned hidden topic features and show how to employ it to learn a joint representation from image visual words, annotation words and class label information. We test our model on the LabelMe and UIUC-Sports data sets and show that it compares favorably to other topic models. Second, we propose a deep extension of our model and provide an efficient way of training the deep model. Experimental results show that our deep model outperforms its shallow version and reaches state-of-the-art performance on the Multimedia Information Retrieval (MIR) Flickr data set.

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.

Simulating extreme low-discharge events for the Rhine using a stochastic model

NASA Astrophysics Data System (ADS)

Macian-Sorribes, Hector; Mens, Marjolein; Schasfoort, Femke; Diermanse, Ferdinand; Pulido-Velazquez, Manuel

2017-04-01

The specific features of hydrological droughts make them more difficult to be analysed than other water-related phenomena: longer time scales (months to several years) so less historical events are available, and the drought severity and associate damage depends on a combination of variables with no clear prevalence (e.g., total water deficit, maximum deficit and duration). As part of drought risk analysis, which aims to provide insight into the variability of hydrological conditions and associated socio-economic impacts, long synthetic time series should therefore be developed. In this contribution, we increase the length of the available inflow time series using stochastic autoregressive modelling. This enhancement could improve the characterization of the extreme range and can define extreme droughts with similar periods of return but different patterns that can lead to distinctly different damages. The methodology consists of: 1) fitting an autoregressive model (AR, ARMA…) to the available records; 2) generating extended time series (thousands of years); 3) performing a frequency analysis with different characteristic variables (total, deficit, maximum deficit and so on); and 4) selecting extreme drought events associated with different characteristic variables and return periods. The methodology was applied to the Rhine river discharge at location Lobith, where the Rhine enters The Netherlands. A monthly ARMA(1,1) autoregressive model with seasonally varying parameters was fitted and successfully validated to the historical records available since year 1901. The maximum monthly deficit with respect to a threshold value of 1800 m3/s and the average discharge for a given time span in m3/s were chosen as indicators to identify drought periods. A synthetic series of 10,000 years of discharges was generated using the validated ARMA model. Two time spans were considered in the analysis: the whole calendar year and the half-year period between April and September (the summer half year, where water demands are highest). Frequency analysis was performed for both indicators and time spans for the generated time series and the historical records. The comparison between observed and generated series showed that the ARMA model provides a good reproduction of the maximum deficits and total discharges, especially for the summer half-year period. The resulting synthetic series are therefore considered credible. These synthetic series, with its wealth of information, can then be used as inputs for the damage assessment models, together with information on precipitation deficits, in order to estimate the risk that lower inflows can have on the urban, the agricultural, the shipping sector and so on. This will help in associating economic losses and periods of return, as well as for estimating how droughts with similar periods of return but different patterns can lead to different damages. ACKNOWLEDGEMENT This study has been supported by the European Union's Horizon 2020 research and innovation programme under the IMPREX project (grant agreement no: 641.811), and by the Climate-KIC Pioneers into Practice Program supported by the European Union's EIT.

Characterizing multivariate decoding models based on correlated EEG spectral features

PubMed Central

McFarland, Dennis J.

2013-01-01

Objective Multivariate decoding methods are popular techniques for analysis of neurophysiological data. The present study explored potential interpretative problems with these techniques when predictors are correlated. Methods Data from sensorimotor rhythm-based cursor control experiments was analyzed offline with linear univariate and multivariate models. Features were derived from autoregressive (AR) spectral analysis of varying model order which produced predictors that varied in their degree of correlation (i.e., multicollinearity). Results The use of multivariate regression models resulted in much better prediction of target position as compared to univariate regression models. However, with lower order AR features interpretation of the spectral patterns of the weights was difficult. This is likely to be due to the high degree of multicollinearity present with lower order AR features. Conclusions Care should be exercised when interpreting the pattern of weights of multivariate models with correlated predictors. Comparison with univariate statistics is advisable. Significance While multivariate decoding algorithms are very useful for prediction their utility for interpretation may be limited when predictors are correlated. PMID:23466267

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

NASA Astrophysics Data System (ADS)

Sun, Ruochen; Yuan, Huiling; Liu, Xiaoli

2017-11-01

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

A statistical model of the human core-temperature circadian rhythm

NASA Technical Reports Server (NTRS)

Brown, E. N.; Choe, Y.; Luithardt, H.; Czeisler, C. A.

2000-01-01

We formulate a statistical model of the human core-temperature circadian rhythm in which the circadian signal is modeled as a van der Pol oscillator, the thermoregulatory response is represented as a first-order autoregressive process, and the evoked effect of activity is modeled with a function specific for each circadian protocol. The new model directly links differential equation-based simulation models and harmonic regression analysis methods and permits statistical analysis of both static and dynamical properties of the circadian pacemaker from experimental data. We estimate the model parameters by using numerically efficient maximum likelihood algorithms and analyze human core-temperature data from forced desynchrony, free-run, and constant-routine protocols. By representing explicitly the dynamical effects of ambient light input to the human circadian pacemaker, the new model can estimate with high precision the correct intrinsic period of this oscillator ( approximately 24 h) from both free-run and forced desynchrony studies. Although the van der Pol model approximates well the dynamical features of the circadian pacemaker, the optimal dynamical model of the human biological clock may have a harmonic structure different from that of the van der Pol oscillator.

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

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

NASA Astrophysics Data System (ADS)

Luo, Xiaoguang; Mayer, Michael; Heck, Bernhard

2010-05-01

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

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.

AR(p) -based detrended fluctuation analysis

NASA Astrophysics Data System (ADS)

Alvarez-Ramirez, J.; Rodriguez, E.

2018-07-01

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

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.

Analyses of global sea surface temperature 1856-1991

NASA Astrophysics Data System (ADS)

Kaplan, Alexey; Cane, Mark A.; Kushnir, Yochanan; Clement, Amy C.; Blumenthal, M. Benno; Rajagopalan, Balaji

1998-08-01

Global analyses of monthly sea surface temperature (SST) anomalies from 1856 to 1991 are produced using three statistically based methods: optimal smoothing (OS), the Kaiman filter (KF) and optimal interpolation (OI). Each of these is accompanied by estimates of the error covariance of the analyzed fields. The spatial covariance function these methods require is estimated from the available data; the timemarching model is a first-order autoregressive model again estimated from data. The data input for the analyses are monthly anomalies from the United Kingdom Meteorological Office historical sea surface temperature data set (MOHSST5) [Parker et al., 1994] of the Global Ocean Surface Temperature Atlas (GOSTA) [Bottomley et al., 1990]. These analyses are compared with each other, with GOSTA, and with an analysis generated by projection (P) onto a set of empirical orthogonal functions (as in Smith et al. [1996]). In theory, the quality of the analyses should rank in the order OS, KF, OI, P, and GOSTA. It is found that the first four give comparable results in the data-rich periods (1951-1991), but at times when data is sparse the first three differ significantly from P and GOSTA. At these times the latter two often have extreme and fluctuating values, prima facie evidence of error. The statistical schemes are also verified against data not used in any of the analyses (proxy records derived from corals and air temperature records from coastal and island stations). We also present evidence that the analysis error estimates are indeed indicative of the quality of the products. At most times the OS and KF products are close to the OI product, but at times of especially poor coverage their use of information from other times is advantageous. The methods appear to reconstruct the major features of the global SST field from very sparse data. Comparison with other indications of the El Niño-Southern Oscillation cycle show that the analyses provide usable information on interannual variability as far back as the 1860s.

Disease Mapping of Zero-excessive Mesothelioma Data in Flanders

PubMed Central

Neyens, Thomas; Lawson, Andrew B.; Kirby, Russell S.; Nuyts, Valerie; Watjou, Kevin; Aregay, Mehreteab; Carroll, Rachel; Nawrot, Tim S.; Faes, Christel

2016-01-01

Purpose To investigate the distribution of mesothelioma in Flanders using Bayesian disease mapping models that account for both an excess of zeros and overdispersion. Methods The numbers of newly diagnosed mesothelioma cases within all Flemish municipalities between 1999 and 2008 were obtained from the Belgian Cancer Registry. To deal with overdispersion, zero-inflation and geographical association, the hurdle combined model was proposed, which has three components: a Bernoulli zero-inflation mixture component to account for excess zeros, a gamma random effect to adjust for overdispersion and a normal conditional autoregressive random effect to attribute spatial association. This model was compared with other existing methods in literature. Results The results indicate that hurdle models with a random effects term accounting for extra-variance in the Bernoulli zero-inflation component fit the data better than hurdle models that do not take overdispersion in the occurrence of zeros into account. Furthermore, traditional models that do not take into account excessive zeros but contain at least one random effects term that models extra-variance in the counts have better fits compared to their hurdle counterparts. In other words, the extra-variability, due to an excess of zeros, can be accommodated by spatially structured and/or unstructured random effects in a Poisson model such that the hurdle mixture model is not necessary. Conclusions Models taking into account zero-inflation do not always provide better fits to data with excessive zeros than less complex models. In this study, a simple conditional autoregressive model identified a cluster in mesothelioma cases near a former asbestos processing plant (Kapelle-op-den-Bos). This observation is likely linked with historical local asbestos exposures. Future research will clarify this. PMID:27908590

Spatial analysis of macro-level bicycle crashes using the class of conditional autoregressive models.

PubMed

Saha, Dibakar; Alluri, Priyanka; Gan, Albert; Wu, Wanyang

2018-02-21

The objective of this study was to investigate the relationship between bicycle crash frequency and their contributing factors at the census block group level in Florida, USA. Crashes aggregated over the census block groups tend to be clustered (i.e., spatially dependent) rather than randomly distributed. To account for the effect of spatial dependence across the census block groups, the class of conditional autoregressive (CAR) models were employed within the hierarchical Bayesian framework. Based on four years (2011-2014) of crash data, total and fatal-and-severe injury bicycle crash frequencies were modeled as a function of a large number of variables representing demographic and socio-economic characteristics, roadway infrastructure and traffic characteristics, and bicycle activity characteristics. This study explored and compared the performance of two CAR models, namely the Besag's model and the Leroux's model, in crash prediction. The Besag's models, which differ from the Leroux's models by the structure of how spatial autocorrelation are specified in the models, were found to fit the data better. A 95% Bayesian credible interval was selected to identify the variables that had credible impact on bicycle crashes. A total of 21 variables were found to be credible in the total crash model, while 18 variables were found to be credible in the fatal-and-severe injury crash model. Population, daily vehicle miles traveled, age cohorts, household automobile ownership, density of urban roads by functional class, bicycle trip miles, and bicycle trip intensity had positive effects in both the total and fatal-and-severe crash models. Educational attainment variables, truck percentage, and density of rural roads by functional class were found to be negatively associated with both total and fatal-and-severe bicycle crash frequencies. Published by Elsevier Ltd.

Disease mapping of zero-excessive mesothelioma data in Flanders.

PubMed

Neyens, Thomas; Lawson, Andrew B; Kirby, Russell S; Nuyts, Valerie; Watjou, Kevin; Aregay, Mehreteab; Carroll, Rachel; Nawrot, Tim S; Faes, Christel

2017-01-01

To investigate the distribution of mesothelioma in Flanders using Bayesian disease mapping models that account for both an excess of zeros and overdispersion. The numbers of newly diagnosed mesothelioma cases within all Flemish municipalities between 1999 and 2008 were obtained from the Belgian Cancer Registry. To deal with overdispersion, zero inflation, and geographical association, the hurdle combined model was proposed, which has three components: a Bernoulli zero-inflation mixture component to account for excess zeros, a gamma random effect to adjust for overdispersion, and a normal conditional autoregressive random effect to attribute spatial association. This model was compared with other existing methods in literature. The results indicate that hurdle models with a random effects term accounting for extra variance in the Bernoulli zero-inflation component fit the data better than hurdle models that do not take overdispersion in the occurrence of zeros into account. Furthermore, traditional models that do not take into account excessive zeros but contain at least one random effects term that models extra variance in the counts have better fits compared to their hurdle counterparts. In other words, the extra variability, due to an excess of zeros, can be accommodated by spatially structured and/or unstructured random effects in a Poisson model such that the hurdle mixture model is not necessary. Models taking into account zero inflation do not always provide better fits to data with excessive zeros than less complex models. In this study, a simple conditional autoregressive model identified a cluster in mesothelioma cases near a former asbestos processing plant (Kapelle-op-den-Bos). This observation is likely linked with historical local asbestos exposures. Future research will clarify this. Copyright © 2016 Elsevier Inc. All rights reserved.

Hierarchical Bayesian spatial models for multispecies conservation planning and monitoring.

PubMed

Carroll, Carlos; Johnson, Devin S; Dunk, Jeffrey R; Zielinski, William J

2010-12-01

Biologists who develop and apply habitat models are often familiar with the statistical challenges posed by their data's spatial structure but are unsure of whether the use of complex spatial models will increase the utility of model results in planning. We compared the relative performance of nonspatial and hierarchical Bayesian spatial models for three vertebrate and invertebrate taxa of conservation concern (Church's sideband snails [Monadenia churchi], red tree voles [Arborimus longicaudus], and Pacific fishers [Martes pennanti pacifica]) that provide examples of a range of distributional extents and dispersal abilities. We used presence-absence data derived from regional monitoring programs to develop models with both landscape and site-level environmental covariates. We used Markov chain Monte Carlo algorithms and a conditional autoregressive or intrinsic conditional autoregressive model framework to fit spatial models. The fit of Bayesian spatial models was between 35 and 55% better than the fit of nonspatial analogue models. Bayesian spatial models outperformed analogous models developed with maximum entropy (Maxent) methods. Although the best spatial and nonspatial models included similar environmental variables, spatial models provided estimates of residual spatial effects that suggested how ecological processes might structure distribution patterns. Spatial models built from presence-absence data improved fit most for localized endemic species with ranges constrained by poorly known biogeographic factors and for widely distributed species suspected to be strongly affected by unmeasured environmental variables or population processes. By treating spatial effects as a variable of interest rather than a nuisance, hierarchical Bayesian spatial models, especially when they are based on a common broad-scale spatial lattice (here the national Forest Inventory and Analysis grid of 24 km(2) hexagons), can increase the relevance of habitat models to multispecies conservation planning. Journal compilation © 2010 Society for Conservation Biology. No claim to original US government works.

Nonparametric autocovariance estimation from censored time series by Gaussian imputation.

PubMed

Park, Jung Wook; Genton, Marc G; Ghosh, Sujit K

2009-02-01

One of the most frequently used methods to model the autocovariance function of a second-order stationary time series is to use the parametric framework of autoregressive and moving average models developed by Box and Jenkins. However, such parametric models, though very flexible, may not always be adequate to model autocovariance functions with sharp changes. Furthermore, if the data do not follow the parametric model and are censored at a certain value, the estimation results may not be reliable. We develop a Gaussian imputation method to estimate an autocovariance structure via nonparametric estimation of the autocovariance function in order to address both censoring and incorrect model specification. We demonstrate the effectiveness of the technique in terms of bias and efficiency with simulations under various rates of censoring and underlying models. We describe its application to a time series of silicon concentrations in the Arctic.

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.

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.

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.

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

PubMed

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

2010-12-01

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

Time series modelling of increased soil temperature anomalies during long period

NASA Astrophysics Data System (ADS)

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

2015-10-01

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

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.

Effect of land cover change on snow free surface albedo across the continental United States

USGS Publications Warehouse

Wickham, J.; Nash, M.S.; Barnes, Christopher A.

2016-01-01

Land cover changes (e.g., forest to grassland) affect albedo, and changes in albedo can influence radiative forcing (warming, cooling). We empirically tested albedo response to land cover change for 130 locations across the continental United States using high resolution (30 m-×-30 m) land cover change data and moderate resolution (~ 500 m-×-500 m) albedo data. The land cover change data spanned 10 years (2001 − 2011) and the albedo data included observations every eight days for 13 years (2001 − 2013). Empirical testing was based on autoregressive time series analysis of snow free albedo for verified locations of land cover change. Approximately one-third of the autoregressive analyses for woody to herbaceous or forest to shrub change classes were not significant, indicating that albedo did not change significantly as a result of land cover change at these locations. In addition, ~ 80% of mean differences in albedo arising from land cover change were less than ± 0.02, a nominal benchmark for precision of albedo measurements that is related to significant changes in radiative forcing. Under snow free conditions, we found that land cover change does not guarantee a significant albedo response, and that the differences in mean albedo response for the majority of land cover change locations were small.

A test-retest assessment of the effects of mental load on ratings of affect, arousal and perceived exertion during submaximal cycling.

PubMed

Vera, Jesús; Perales, José C; Jiménez, Raimundo; Cárdenas, David

2018-04-24

This study aimed to test the effects of mental (i.e. executive) load during a dual physical-mental task on ratings of perceived exertion (RPE), affective valence, and arousal. The protocol included two dual tasks with matched physical demands but different executive demands (2-back and oddball), carried out on different days. The procedure was run twice to assess the sensitivity and stability of RPE, valence and arousal across the two trials. Linear mixed-effects analyses showed less positive valence (-0.44 points on average in a 1-9 scale; R β 2  = 0.074 [CI90%, 0.052-0.098]), and heightened arousal (+0.13 points on average in a 1-9 scale; R β 2  = 0.006 [CI90%, 0.001-0.015]), for the high executive load condition, but showed no effect of mental load on RPE. Separated analyses for the two task trials yielded best-fitting models that were identical across trials for RPE and valence, but not for arousal. Model fitting was improved by assuming a 1-level autoregressive covariance structure for all analyses. In conclusion, executive load during a dual physical-mental task modulates the emotional response to effort, but not RPE. The autoregressive covariance suggests that people tend to anchor estimates on prior ones, which imposes certain limits on scales' usability.

Granger causality for state-space models

NASA Astrophysics Data System (ADS)

Barnett, Lionel; Seth, Anil K.

2015-04-01

Granger causality has long been a prominent method for inferring causal interactions between stochastic variables for a broad range of complex physical systems. However, it has been recognized that a moving average (MA) component in the data presents a serious confound to Granger causal analysis, as routinely performed via autoregressive (AR) modeling. We solve this problem by demonstrating that Granger causality may be calculated simply and efficiently from the parameters of a state-space (SS) model. Since SS models are equivalent to autoregressive moving average models, Granger causality estimated in this fashion is not degraded by the presence of a MA component. This is of particular significance when the data has been filtered, downsampled, observed with noise, or is a subprocess of a higher dimensional process, since all of these operations—commonplace in application domains as diverse as climate science, econometrics, and the neurosciences—induce a MA component. We show how Granger causality, conditional and unconditional, in both time and frequency domains, may be calculated directly from SS model parameters via solution of a discrete algebraic Riccati equation. Numerical simulations demonstrate that Granger causality estimators thus derived have greater statistical power and smaller bias than AR estimators. We also discuss how the SS approach facilitates relaxation of the assumptions of linearity, stationarity, and homoscedasticity underlying current AR methods, thus opening up potentially significant new areas of research in Granger causal analysis.

Exploring heterogeneous market hypothesis using realized volatility

NASA Astrophysics Data System (ADS)

Chin, Wen Cheong; Isa, Zaidi; Mohd Nor, Abu Hassan Shaari

2013-04-01

This study investigates the heterogeneous market hypothesis using high frequency data. The cascaded heterogeneous trading activities with different time durations are modelled by the heterogeneous autoregressive framework. The empirical study indicated the presence of long memory behaviour and predictability elements in the financial time series which supported heterogeneous market hypothesis. Besides the common sum-of-square intraday realized volatility, we also advocated two power variation realized volatilities in forecast evaluation and risk measurement in order to overcome the possible abrupt jumps during the credit crisis. Finally, the empirical results are used in determining the market risk using the value-at-risk approach. The findings of this study have implications for informationally market efficiency analysis, portfolio strategies and risk managements.

Thermoelastic steam turbine rotor control based on neural network

NASA Astrophysics Data System (ADS)

Rzadkowski, Romuald; Dominiczak, Krzysztof; Radulski, Wojciech; Szczepanik, R.

2015-12-01

Considered here are Nonlinear Auto-Regressive neural networks with eXogenous inputs (NARX) as a mathematical model of a steam turbine rotor for controlling steam turbine stress on-line. In order to obtain neural networks that locate critical stress and temperature points in the steam turbine during transient states, an FE rotor model was built. This model was used to train the neural networks on the basis of steam turbine transient operating data. The training included nonlinearity related to steam turbine expansion, heat exchange and rotor material properties during transients. Simultaneous neural networks are algorithms which can be implemented on PLC controllers. This allows for the application neural networks to control steam turbine stress in industrial power plants.

A comparison of likelihood ratio tests and Rao's score test for three separable covariance matrix structures.

PubMed

Filipiak, Katarzyna; Klein, Daniel; Roy, Anuradha

2017-01-01

The problem of testing the separability of a covariance matrix against an unstructured variance-covariance matrix is studied in the context of multivariate repeated measures data using Rao's score test (RST). The RST statistic is developed with the first component of the separable structure as a first-order autoregressive (AR(1)) correlation matrix or an unstructured (UN) covariance matrix under the assumption of multivariate normality. It is shown that the distribution of the RST statistic under the null hypothesis of any separability does not depend on the true values of the mean or the unstructured components of the separable structure. A significant advantage of the RST is that it can be performed for small samples, even smaller than the dimension of the data, where the likelihood ratio test (LRT) cannot be used, and it outperforms the standard LRT in a number of contexts. Monte Carlo simulations are then used to study the comparative behavior of the null distribution of the RST statistic, as well as that of the LRT statistic, in terms of sample size considerations, and for the estimation of the empirical percentiles. Our findings are compared with existing results where the first component of the separable structure is a compound symmetry (CS) correlation matrix. It is also shown by simulations that the empirical null distribution of the RST statistic converges faster than the empirical null distribution of the LRT statistic to the limiting χ 2 distribution. The tests are implemented on a real dataset from medical studies. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

The effect of presentation rate on implicit sequence learning in aging.

PubMed

Foster, Chris M; Giovanello, Kelly S

2017-02-01

Implicit sequence learning is thought to be preserved in aging when the to-be learned associations are first-order; however, when associations are second-order, older adults (OAs) tend to experience deficits as compared to young adults (YAs). Two experiments were conducted using a first (Experiment 1) and second-order (Experiment 2) serial-reaction time task. Stimuli were presented at a constant rate of either 800 milliseconds (fast) or 1200 milliseconds (slow). Results indicate that both age groups learned first-order dependencies equally in both conditions. OAs and YAs also learned second-order dependencies, but the learning of lag-2 information was significantly impacted by the rate of presentation for both groups. OAs showed significant lag-2 learning in slow condition while YAs showed significant lag-2 learning in the fast condition. The sensitivity of implicit sequence learning to the rate of presentation supports the idea that OAs and YAs different processing speeds impact the ability to build complex associations across time and intervening events.

Invariance in the recurrence of large returns and the validation of models of price dynamics

NASA Astrophysics Data System (ADS)

Chang, Lo-Bin; Geman, Stuart; Hsieh, Fushing; Hwang, Chii-Ruey

2013-08-01

Starting from a robust, nonparametric definition of large returns (“excursions”), we study the statistics of their occurrences, focusing on the recurrence process. The empirical waiting-time distribution between excursions is remarkably invariant to year, stock, and scale (return interval). This invariance is related to self-similarity of the marginal distributions of returns, but the excursion waiting-time distribution is a function of the entire return process and not just its univariate probabilities. Generalized autoregressive conditional heteroskedasticity (GARCH) models, market-time transformations based on volume or trades, and generalized (Lévy) random-walk models all fail to fit the statistical structure of excursions.

A Power-Law Growth and Decay Model with Autocorrelation for Posting Data to Social Networking Services

PubMed Central

Fujiyama, Toshifumi; Matsui, Chihiro; Takemura, Akimichi

2016-01-01

We propose a power-law growth and decay model for posting data to social networking services before and after social events. We model the time series structure of deviations from the power-law growth and decay with a conditional Poisson autoregressive (AR) model. Online postings related to social events are described by five parameters in the power-law growth and decay model, each of which characterizes different aspects of interest in the event. We assess the validity of parameter estimates in terms of confidence intervals, and compare various submodels based on likelihoods and information criteria. PMID:27505155

Calibrating the pixel-level Kepler imaging data with a causal data-driven model

NASA Astrophysics Data System (ADS)

Wang, Dun; Foreman-Mackey, Daniel; Hogg, David W.; Schölkopf, Bernhard

2015-01-01

In general, astronomical observations are affected by several kinds of noise, each with it's own causal source; there is photon noise, stochastic source variability, and residuals coming from imperfect calibration of the detector or telescope. In particular, the precision of NASA Kepler photometry for exoplanet science—the most precise photometric measurements of stars ever made—appears to be limited by unknown or untracked variations in spacecraft pointing and temperature, and unmodeled stellar variability. Here we present the Causal Pixel Model (CPM) for Kepler data, a data-driven model intended to capture variability but preserve transit signals. The CPM works at the pixel level (not the photometric measurement level); it can capture more fine-grained information about the variation of the spacecraft than is available in the pixel-summed aperture photometry. The basic idea is that CPM predicts each target pixel value from a large number of pixels of other stars sharing the instrument variabilities while not containing any information on possible transits at the target star. In addition, we use the target star's future and past (auto-regression). By appropriately separating the data into training and test sets, we ensure that information about any transit will be perfectly isolated from the fitting of the model. The method has four hyper-parameters (the number of predictor stars, the auto-regressive window size, and two L2-regularization amplitudes for model components), which we set by cross-validation. We determine a generic set of hyper-parameters that works well on most of the stars with 11≤V≤12 mag and apply the method to a corresponding set of target stars with known planet transits. We find that we can consistently outperform (for the purposes of exoplanet detection) the Kepler Pre-search Data Conditioning (PDC) method for exoplanet discovery, often improving the SNR by a factor of two. While we have not yet exhaustively tested the method at other magnitudes, we expect that it should be generally applicable, with positive consequences for subsequent exoplanet detection or stellar variability (in which case we must exclude the autoregressive part to preserve intrinsic variability).

State estimation of spatio-temporal phenomena

NASA Astrophysics Data System (ADS)

Yu, Dan

This dissertation addresses the state estimation problem of spatio-temporal phenomena which can be modeled by partial differential equations (PDEs), such as pollutant dispersion in the atmosphere. After discretizing the PDE, the dynamical system has a large number of degrees of freedom (DOF). State estimation using Kalman Filter (KF) is computationally intractable, and hence, a reduced order model (ROM) needs to be constructed first. Moreover, the nonlinear terms, external disturbances or unknown boundary conditions can be modeled as unknown inputs, which leads to an unknown input filtering problem. Furthermore, the performance of KF could be improved by placing sensors at feasible locations. Therefore, the sensor scheduling problem to place multiple mobile sensors is of interest. The first part of the dissertation focuses on model reduction for large scale systems with a large number of inputs/outputs. A commonly used model reduction algorithm, the balanced proper orthogonal decomposition (BPOD) algorithm, is not computationally tractable for large systems with a large number of inputs/outputs. Inspired by the BPOD and randomized algorithms, we propose a randomized proper orthogonal decomposition (RPOD) algorithm and a computationally optimal RPOD (RPOD*) algorithm, which construct an ROM to capture the input-output behaviour of the full order model, while reducing the computational cost of BPOD by orders of magnitude. It is demonstrated that the proposed RPOD* algorithm could construct the ROM in real-time, and the performance of the proposed algorithms on different advection-diffusion equations. Next, we consider the state estimation problem of linear discrete-time systems with unknown inputs which can be treated as a wide-sense stationary process with rational power spectral density, while no other prior information needs to be known. We propose an autoregressive (AR) model based unknown input realization technique which allows us to recover the input statistics from the output data by solving an appropriate least squares problem, then fit an AR model to the recovered input statistics and construct an innovations model of the unknown inputs using the eigensystem realization algorithm. The proposed algorithm outperforms the augmented two-stage Kalman Filter (ASKF) and the unbiased minimum-variance (UMV) algorithm are shown in several examples. Finally, we propose a framework to place multiple mobile sensors to optimize the long-term performance of KF in the estimation of the state of a PDE. The major challenges are that placing multiple sensors is an NP-hard problem, and the optimization problem is non-convex in general. In this dissertation, first, we construct an ROM using RPOD* algorithm, and then reduce the feasible sensor locations into a subset using the ROM. The Information Space Receding Horizon Control (I-RHC) approach and a modified Monte Carlo Tree Search (MCTS) approach are applied to solve the sensor scheduling problem using the subset. Various applications have been provided to demonstrate the performance of the proposed approach.

Functional brain networks in healthy subjects under acupuncture stimulation: An EEG study based on nonlinear synchronization likelihood analysis

NASA Astrophysics Data System (ADS)

Yu, Haitao; Liu, Jing; Cai, Lihui; Wang, Jiang; Cao, Yibin; Hao, Chongqing

2017-02-01

Electroencephalogram (EEG) signal evoked by acupuncture stimulation at "Zusanli" acupoint is analyzed to investigate the modulatory effect of manual acupuncture on the functional brain activity. Power spectral density of EEG signal is first calculated based on the autoregressive Burg method. It is shown that the EEG power is significantly increased during and after acupuncture in delta and theta bands, but decreased in alpha band. Furthermore, synchronization likelihood is used to estimate the nonlinear correlation between each pairwise EEG signals. By applying a threshold to resulting synchronization matrices, functional networks for each band are reconstructed and further quantitatively analyzed to study the impact of acupuncture on network structure. Graph theoretical analysis demonstrates that the functional connectivity of the brain undergoes obvious change under different conditions: pre-acupuncture, acupuncture, and post-acupuncture. The minimum path length is largely decreased and the clustering coefficient keeps increasing during and after acupuncture in delta and theta bands. It is indicated that acupuncture can significantly modulate the functional activity of the brain, and facilitate the information transmission within different brain areas. The obtained results may facilitate our understanding of the long-lasting effect of acupuncture on the brain function.

A spatial analysis of social and economic determinants of tuberculosis in Brazil.

PubMed

Harling, Guy; Castro, Marcia C

2014-01-01

We investigated the spatial distribution, and social and economic correlates, of tuberculosis in Brazil between 2002 and 2009 using municipality-level age/sex-standardized tuberculosis notification data. Rates were very strongly spatially autocorrelated, being notably high in urban areas on the eastern seaboard and in the west of the country. Non-spatial ecological regression analyses found higher rates associated with urbanicity, population density, poor economic conditions, household crowding, non-white population and worse health and healthcare indicators. These associations remained in spatial conditional autoregressive models, although the effect of poverty appeared partially confounded by urbanicity, race and spatial autocorrelation, and partially mediated by household crowding. Our analysis highlights both the multiple relationships between socioeconomic factors and tuberculosis in Brazil, and the importance of accounting for spatial factors in analysing socioeconomic determinants of tuberculosis. © 2013 Published by Elsevier Ltd.

Biotransformation of trace organic chemicals during groundwater recharge: How useful are first-order rate constants?

PubMed

Regnery, J; Wing, A D; Alidina, M; Drewes, J E

2015-08-01

This study developed relationships between the attenuation of emerging trace organic chemicals (TOrC) during managed aquifer recharge (MAR) as a function of retention time, system characteristics, and operating conditions using controlled laboratory-scale soil column experiments simulating MAR. The results revealed that MAR performance in terms of TOrC attenuation is primarily determined by key environmental parameters (i.e., redox, primary substrate). Soil columns with suboxic and anoxic conditions performed poorly (i.e., less than 30% attenuation of moderately degradable TOrC) in comparison to oxic conditions (on average between 70-100% attenuation for the same compounds) within a residence time of three days. Given this dependency on redox conditions, it was investigated if key parameter-dependent rate constants are more suitable for contaminant transport modeling to properly capture the dynamic TOrC attenuation under field-scale conditions. Laboratory-derived first-order removal kinetics were determined for 19 TOrC under three different redox conditions and rate constants were applied to MAR field data. Our findings suggest that simplified first-order rate constants will most likely not provide any meaningful results if the target compounds exhibit redox dependent biotransformation behavior or if the intention is to exactly capture the decline in concentration over time and distance at field-scale MAR. However, if the intention is to calculate the percent removal after an extended time period and subsurface travel distance, simplified first-order rate constants seem to be sufficient to provide a first estimate on TOrC attenuation during MAR. Copyright © 2015 Elsevier B.V. All rights reserved.

Spatial relationships between alcohol-related road crashes and retail alcohol availability.

PubMed

Morrison, Christopher; Ponicki, William R; Gruenewald, Paul J; Wiebe, Douglas J; Smith, Karen

2016-05-01

This study examines spatial relationships between alcohol outlet density and the incidence of alcohol-related crashes. The few prior studies conducted in this area used relatively large spatial units; here we use highly resolved units from Melbourne, Australia (Statistical Area level 1 [SA1] units: mean land area=0.5 km(2); SD=2.2 km(2)), in order to assess different micro-scale spatial relationships for on- and off-premise outlets. Bayesian conditional autoregressive Poisson models were used to assess cross-sectional relationships of three-year counts of alcohol-related crashes (2010-2012) attended by Ambulance Victoria paramedics to densities of bars, restaurants, and off-premise outlets controlling for other land use, demographic and roadway characteristics. Alcohol-related crashes were not related to bar density within local SA1 units, but were positively related to bar density in adjacent SA1 units. Alcohol-related crashes were negatively related to off-premise outlet density in local SA1 units. Examined in one metropolitan area using small spatial units, bar density is related to greater crash risk in surrounding areas. Observed negative relationships for off-premise outlets may be because the origins and destinations of alcohol-affected journeys are in distal locations relative to outlets. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

[Treatment of recurrent neurocardiogenic syncope with cardiac inhibitors with ipratropium bromide].

PubMed

Friederich, H-C; Michaelsen, J; Hesse, C; Schellberg, D; Schwab, M; Herzog, W

2004-06-01

Pharmacological approaches for the treatment of cardioinhibitory vasovagal syncope are controversially discussed in the literature. In acute treatment of neurocardiogenic syncope, anticholinergics (atropine) are used effectively. Randomised and placebo-controlled clinical trials evaluating the preventive significance of anticholinergic agents in the therapy of cardioinhibitory vasovagal syncope are still missing. We report the case of an 18-year-old male patient with recurrent convulsive, cardioinhibitory neurocardiogenic syncope. Vasovagal syncope occurred predominantly as centrally induced syncope triggered by negative emotions such as fear or by seeing blood. Under resting conditions, the patient revealed increased parasympathetic tone with nocturnal bradycardia of 38 beats/min. In the course of head-up tilt table testing a cardioinhibitory syncope with an asystolic pause of 10 seconds occurred without any prodromes after 10 minutes of upright positioning. In order to inhibit parasympathetic tone, medication with ipratropiumbromide was initiated. Time-variant analysis of heart rate variability (autoregressive model) during head-up tilt table testing showed under the medication with ipratropiumbromide a vagal mediated cardioinhibition to 56 beats/min, but no further sinus arrest. Throughout clinical follow-up of 6 months the patient remained syncope-free under the medication. The usefulness of ipratropiumbromide in inhibiting vagal mediated cardioinhibition will be discussed referring to the case report and to studies evaluating anticholinergic agents in the treatment of neurocardiogenic syncope.

Autoregressive Processes in Homogenization of GNSS Tropospheric Data

NASA Astrophysics Data System (ADS)

Klos, A.; Bogusz, J.; Teferle, F. N.; Bock, O.; Pottiaux, E.; Van Malderen, R.

2016-12-01

Offsets due to changes in hardware equipment or any other artificial event are all a subject of a task of homogenization of tropospheric data estimated within a processing of Global Navigation Satellite System (GNSS) observables. This task is aimed at identifying exact epochs of offsets and estimate their magnitudes since they may artificially under- or over-estimate trend and its uncertainty delivered from tropospheric data and used in climate studies. In this research, we analysed a common data set of differences of Integrated Water Vapour (IWV) from GPS and ERA-Interim (1995-2010) provided for a homogenization group working within ES1206 COST Action GNSS4SWEC. We analysed daily IWV records of GPS and ERA-Interim in terms of trend, seasonal terms and noise model with Maximum Likelihood Estimation in Hector software. We found that this data has a character of autoregressive process (AR). Basing on this analysis, we performed Monte Carlo simulations of 25 years long data with two different noise types: white as well as combination of white and autoregressive and also added few strictly defined offsets. This synthetic data set of exactly the same character as IWV from GPS and ERA-Interim was then subjected to a task of manual and automatic/statistical homogenization. We made blind tests and detected possible epochs of offsets manually. We found that simulated offsets were easily detected in series with white noise, no influence of seasonal signal was noticed. The autoregressive series were much more problematic when offsets had to be determined. We found few epochs, for which no offset was simulated. This was mainly due to strong autocorrelation of data, which brings an artificial trend within. Due to regime-like behaviour of AR it is difficult for statistical methods to properly detect epochs of offsets, which was previously reported by climatologists.

Three-dimensional dominant frequency mapping using autoregressive spectral analysis of atrial electrograms of patients in persistent atrial fibrillation.

PubMed

Salinet, João L; Masca, Nicholas; Stafford, Peter J; Ng, G André; Schlindwein, Fernando S

2016-03-08

Areas with high frequency activity within the atrium are thought to be 'drivers' of the rhythm in patients with atrial fibrillation (AF) and ablation of these areas seems to be an effective therapy in eliminating DF gradient and restoring sinus rhythm. Clinical groups have applied the traditional FFT-based approach to generate the three-dimensional dominant frequency (3D DF) maps during electrophysiology (EP) procedures but literature is restricted on using alternative spectral estimation techniques that can have a better frequency resolution that FFT-based spectral estimation. Autoregressive (AR) model-based spectral estimation techniques, with emphasis on selection of appropriate sampling rate and AR model order, were implemented to generate high-density 3D DF maps of atrial electrograms (AEGs) in persistent atrial fibrillation (persAF). For each patient, 2048 simultaneous AEGs were recorded for 20.478 s-long segments in the left atrium (LA) and exported for analysis, together with their anatomical locations. After the DFs were identified using AR-based spectral estimation, they were colour coded to produce sequential 3D DF maps. These maps were systematically compared with maps found using the Fourier-based approach. 3D DF maps can be obtained using AR-based spectral estimation after AEGs downsampling (DS) and the resulting maps are very similar to those obtained using FFT-based spectral estimation (mean 90.23 %). There were no significant differences between AR techniques (p = 0.62). The processing time for AR-based approach was considerably shorter (from 5.44 to 5.05 s) when lower sampling frequencies and model order values were used. Higher levels of DS presented higher rates of DF agreement (sampling frequency of 37.5 Hz). We have demonstrated the feasibility of using AR spectral estimation methods for producing 3D DF maps and characterised their differences to the maps produced using the FFT technique, offering an alternative approach for 3D DF computation in human persAF studies.

Training Preschoolers on First-Order False Belief Understanding: Transfer on Advanced ToM Skills and Metamemory

ERIC Educational Resources Information Center

Lecce, Serena; Bianco, Federica; Demicheli, Patrizia; Cavallini, Elena

2014-01-01

This study investigated the relation between theory of mind (ToM) and metamemory knowledge using a training methodology. Sixty-two 4- to 5-year-old children were recruited and randomly assigned to one of two training conditions: A first-order false belief (ToM) and a control condition. Intervention and control groups were equivalent at pretest for…

Early detection of metabolic and energy disorders by thermal time series stochastic complexity analysis

PubMed Central

Lutaif, N.A.; Palazzo, R.; Gontijo, J.A.R.

2014-01-01

Maintenance of thermal homeostasis in rats fed a high-fat diet (HFD) is associated with changes in their thermal balance. The thermodynamic relationship between heat dissipation and energy storage is altered by the ingestion of high-energy diet content. Observation of thermal registers of core temperature behavior, in humans and rodents, permits identification of some characteristics of time series, such as autoreference and stationarity that fit adequately to a stochastic analysis. To identify this change, we used, for the first time, a stochastic autoregressive model, the concepts of which match those associated with physiological systems involved and applied in male HFD rats compared with their appropriate standard food intake age-matched male controls (n=7 per group). By analyzing a recorded temperature time series, we were able to identify when thermal homeostasis would be affected by a new diet. The autoregressive time series model (AR model) was used to predict the occurrence of thermal homeostasis, and this model proved to be very effective in distinguishing such a physiological disorder. Thus, we infer from the results of our study that maximum entropy distribution as a means for stochastic characterization of temperature time series registers may be established as an important and early tool to aid in the diagnosis and prevention of metabolic diseases due to their ability to detect small variations in thermal profile. PMID:24519093

Early detection of metabolic and energy disorders by thermal time series stochastic complexity analysis.

PubMed

Lutaif, N A; Palazzo, R; Gontijo, J A R

2014-01-01

Maintenance of thermal homeostasis in rats fed a high-fat diet (HFD) is associated with changes in their thermal balance. The thermodynamic relationship between heat dissipation and energy storage is altered by the ingestion of high-energy diet content. Observation of thermal registers of core temperature behavior, in humans and rodents, permits identification of some characteristics of time series, such as autoreference and stationarity that fit adequately to a stochastic analysis. To identify this change, we used, for the first time, a stochastic autoregressive model, the concepts of which match those associated with physiological systems involved and applied in male HFD rats compared with their appropriate standard food intake age-matched male controls (n=7 per group). By analyzing a recorded temperature time series, we were able to identify when thermal homeostasis would be affected by a new diet. The autoregressive time series model (AR model) was used to predict the occurrence of thermal homeostasis, and this model proved to be very effective in distinguishing such a physiological disorder. Thus, we infer from the results of our study that maximum entropy distribution as a means for stochastic characterization of temperature time series registers may be established as an important and early tool to aid in the diagnosis and prevention of metabolic diseases due to their ability to detect small variations in thermal profile.

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.

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

PubMed

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

2007-09-01

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

Increasing Accuracy in Computed Inviscid Boundary Conditions

NASA Technical Reports Server (NTRS)

Dyson, Roger

2004-01-01

A technique has been devised to increase the accuracy of computational simulations of flows of inviscid fluids by increasing the accuracy with which surface boundary conditions are represented. This technique is expected to be especially beneficial for computational aeroacoustics, wherein it enables proper accounting, not only for acoustic waves, but also for vorticity and entropy waves, at surfaces. Heretofore, inviscid nonlinear surface boundary conditions have been limited to third-order accuracy in time for stationary surfaces and to first-order accuracy in time for moving surfaces. For steady-state calculations, it may be possible to achieve higher accuracy in space, but high accuracy in time is needed for efficient simulation of multiscale unsteady flow phenomena. The present technique is the first surface treatment that provides the needed high accuracy through proper accounting of higher-order time derivatives. The present technique is founded on a method known in art as the Hermitian modified solution approximation (MESA) scheme. This is because high time accuracy at a surface depends upon, among other things, correction of the spatial cross-derivatives of flow variables, and many of these cross-derivatives are included explicitly on the computational grid in the MESA scheme. (Alternatively, a related method other than the MESA scheme could be used, as long as the method involves consistent application of the effects of the cross-derivatives.) While the mathematical derivation of the present technique is too lengthy and complex to fit within the space available for this article, the technique itself can be characterized in relatively simple terms: The technique involves correction of surface-normal spatial pressure derivatives at a boundary surface to satisfy the governing equations and the boundary conditions and thereby achieve arbitrarily high orders of time accuracy in special cases. The boundary conditions can now include a potentially infinite number of time derivatives of surface-normal velocity (consistent with no flow through the boundary) up to arbitrarily high order. The corrections for the first-order spatial derivatives of pressure are calculated by use of the first-order time derivative velocity. The corrected first-order spatial derivatives are used to calculate the second- order time derivatives of velocity, which, in turn, are used to calculate the corrections for the second-order pressure derivatives. The process as described is repeated, progressing through increasing orders of derivatives, until the desired accuracy is attained.

Training preschoolers on first-order false belief understanding: transfer on advanced ToM skills and metamemory.

PubMed

Lecce, Serena; Bianco, Federica; Demicheli, Patrizia; Cavallini, Elena

2014-01-01

This study investigated the relation between theory of mind (ToM) and metamemory knowledge using a training methodology. Sixty-two 4- to 5-year-old children were recruited and randomly assigned to one of two training conditions: A first-order false belief (ToM) and a control condition. Intervention and control groups were equivalent at pretest for age, parents' education, verbal ability, inhibition, and ToM. Results showed that after the intervention children in the ToM group improved in their first-order false belief understanding significantly more than children in the control condition. Crucially, the positive effect of the ToM intervention was stable over 2 months and generalized to more complex ToM tasks and metamemory. © 2014 The Authors. Child Development © 2014 Society for Research in Child Development, Inc.

Granger Causality and Transfer Entropy Are Equivalent for Gaussian Variables

NASA Astrophysics Data System (ADS)

Barnett, Lionel; Barrett, Adam B.; Seth, Anil K.

2009-12-01

Granger causality is a statistical notion of causal influence based on prediction via vector autoregression. Developed originally in the field of econometrics, it has since found application in a broader arena, particularly in neuroscience. More recently transfer entropy, an information-theoretic measure of time-directed information transfer between jointly dependent processes, has gained traction in a similarly wide field. While it has been recognized that the two concepts must be related, the exact relationship has until now not been formally described. Here we show that for Gaussian variables, Granger causality and transfer entropy are entirely equivalent, thus bridging autoregressive and information-theoretic approaches to data-driven causal inference.

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

PubMed

Liu, Siwei; Molenaar, Peter C M

2014-12-01

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

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

NASA Astrophysics Data System (ADS)

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

2014-10-01

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

Characterizing multivariate decoding models based on correlated EEG spectral features.

PubMed

McFarland, Dennis J

2013-07-01

Multivariate decoding methods are popular techniques for analysis of neurophysiological data. The present study explored potential interpretative problems with these techniques when predictors are correlated. Data from sensorimotor rhythm-based cursor control experiments was analyzed offline with linear univariate and multivariate models. Features were derived from autoregressive (AR) spectral analysis of varying model order which produced predictors that varied in their degree of correlation (i.e., multicollinearity). The use of multivariate regression models resulted in much better prediction of target position as compared to univariate regression models. However, with lower order AR features interpretation of the spectral patterns of the weights was difficult. This is likely to be due to the high degree of multicollinearity present with lower order AR features. Care should be exercised when interpreting the pattern of weights of multivariate models with correlated predictors. Comparison with univariate statistics is advisable. While multivariate decoding algorithms are very useful for prediction their utility for interpretation may be limited when predictors are correlated. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Learning investment indicators through data extension

NASA Astrophysics Data System (ADS)

Dvořák, Marek

2017-07-01

Stock prices in the form of time series were analysed using single and multivariate statistical methods. After simple data preprocessing in the form of logarithmic differences, we augmented this single variate time series to a multivariate representation. This method makes use of sliding windows to calculate several dozen of new variables using simple statistic tools like first and second moments as well as more complicated statistic, like auto-regression coefficients and residual analysis, followed by an optional quadratic transformation that was further used for data extension. These were used as a explanatory variables in a regularized logistic LASSO regression which tried to estimate Buy-Sell Index (BSI) from real stock market data.

A generalization of random matrix theory and its application to statistical physics.

PubMed

Wang, Duan; Zhang, Xin; Horvatic, Davor; Podobnik, Boris; Eugene Stanley, H

2017-02-01

To study the statistical structure of crosscorrelations in empirical data, we generalize random matrix theory and propose a new method of cross-correlation analysis, known as autoregressive random matrix theory (ARRMT). ARRMT takes into account the influence of auto-correlations in the study of cross-correlations in multiple time series. We first analytically and numerically determine how auto-correlations affect the eigenvalue distribution of the correlation matrix. Then we introduce ARRMT with a detailed procedure of how to implement the method. Finally, we illustrate the method using two examples taken from inflation rates for air pressure data for 95 US cities.

Forecasting conditional climate-change using a hybrid approach

USGS Publications Warehouse

Esfahani, Akbar Akbari; Friedel, Michael J.

2014-01-01

A novel approach is proposed to forecast the likelihood of climate-change across spatial landscape gradients. This hybrid approach involves reconstructing past precipitation and temperature using the self-organizing map technique; determining quantile trends in the climate-change variables by quantile regression modeling; and computing conditional forecasts of climate-change variables based on self-similarity in quantile trends using the fractionally differenced auto-regressive integrated moving average technique. The proposed modeling approach is applied to states (Arizona, California, Colorado, Nevada, New Mexico, and Utah) in the southwestern U.S., where conditional forecasts of climate-change variables are evaluated against recent (2012) observations, evaluated at a future time period (2030), and evaluated as future trends (2009–2059). These results have broad economic, political, and social implications because they quantify uncertainty in climate-change forecasts affecting various sectors of society. Another benefit of the proposed hybrid approach is that it can be extended to any spatiotemporal scale providing self-similarity exists.

Predictor-based multivariable closed-loop system identification of the EXTRAP T2R reversed field pinch external plasma response

NASA Astrophysics Data System (ADS)

Olofsson, K. Erik J.; Brunsell, Per R.; Rojas, Cristian R.; Drake, James R.; Hjalmarsson, Håkan

2011-08-01

The usage of computationally feasible overparametrized and nonregularized system identification signal processing methods is assessed for automated determination of the full reversed-field pinch external plasma response spectrum for the experiment EXTRAP T2R. No assumptions on the geometry of eigenmodes are imposed. The attempted approach consists of high-order autoregressive exogenous estimation followed by Markov block coefficient construction and Hankel matrix singular value decomposition. It is seen that the obtained 'black-box' state-space models indeed can be compared with the commonplace ideal magnetohydrodynamics (MHD) resistive thin-shell model in cylindrical geometry. It is possible to directly map the most unstable autodetected empirical system pole to the corresponding theoretical resistive shell MHD eigenmode.

Design of a compensation for an ARMA model of a discrete time system. M.S. Thesis

NASA Technical Reports Server (NTRS)

Mainemer, C. I.

1978-01-01

The design of an optimal dynamic compensator for a multivariable discrete time system is studied. Also the design of compensators to achieve minimum variance control strategies for single input single output systems is analyzed. In the first problem the initial conditions of the plant are random variables with known first and second order moments, and the cost is the expected value of the standard cost, quadratic in the states and controls. The compensator is based on the minimum order Luenberger observer and it is found optimally by minimizing a performance index. Necessary and sufficient conditions for optimality of the compensator are derived. The second problem is solved in three different ways; two of them working directly in the frequency domain and one working in the time domain. The first and second order moments of the initial conditions are irrelevant to the solution. Necessary and sufficient conditions are derived for the compensator to minimize the variance of the output.

A stochastic approach to noise modeling for barometric altimeters.

PubMed

Sabatini, Angelo Maria; Genovese, Vincenzo

2013-11-18

The question whether barometric altimeters can be applied to accurately track human motions is still debated, since their measurement performance are rather poor due to either coarse resolution or drifting behavior problems. As a step toward accurate short-time tracking of changes in height (up to few minutes), we develop a stochastic model that attempts to capture some statistical properties of the barometric altimeter noise. The barometric altimeter noise is decomposed in three components with different physical origin and properties: a deterministic time-varying mean, mainly correlated with global environment changes, and a first-order Gauss-Markov (GM) random process, mainly accounting for short-term, local environment changes, the effects of which are prominent, respectively, for long-time and short-time motion tracking; an uncorrelated random process, mainly due to wideband electronic noise, including quantization noise. Autoregressive-moving average (ARMA) system identification techniques are used to capture the correlation structure of the piecewise stationary GM component, and to estimate its standard deviation, together with the standard deviation of the uncorrelated component. M-point moving average filters used alone or in combination with whitening filters learnt from ARMA model parameters are further tested in few dynamic motion experiments and discussed for their capability of short-time tracking small-amplitude, low-frequency motions.

Order of stimulus presentation influences children's acquisition in receptive identification tasks.

PubMed

Petursdottir, Anna Ingeborg; Aguilar, Gabriella

2016-03-01

Receptive identification is usually taught in matching-to-sample format, which entails the presentation of an auditory sample stimulus and several visual comparison stimuli in each trial. Conflicting recommendations exist regarding the order of stimulus presentation in matching-to-sample trials. The purpose of this study was to compare acquisition in receptive identification tasks under 2 conditions: when the sample was presented before the comparisons (sample first) and when the comparisons were presented before the sample (comparison first). Participants included 4 typically developing kindergarten-age boys. Stimuli, which included birds and flags, were presented on a computer screen. Acquisition in the 2 conditions was compared in an adapted alternating-treatments design combined with a multiple baseline design across stimulus sets. All participants took fewer trials to meet the mastery criterion in the sample-first condition than in the comparison-first condition. © 2015 Society for the Experimental Analysis of Behavior.

A short-term ensemble wind speed forecasting system for wind power applications

NASA Astrophysics Data System (ADS)

Baidya Roy, S.; Traiteur, J. J.; Callicutt, D.; Smith, M.

2011-12-01

This study develops an adaptive, blended forecasting system to provide accurate wind speed forecasts 1 hour ahead of time for wind power applications. The system consists of an ensemble of 21 forecasts with different configurations of the Weather Research and Forecasting Single Column Model (WRFSCM) and a persistence model. The ensemble is calibrated against observations for a 2 month period (June-July, 2008) at a potential wind farm site in Illinois using the Bayesian Model Averaging (BMA) technique. The forecasting system is evaluated against observations for August 2008 at the same site. The calibrated ensemble forecasts significantly outperform the forecasts from the uncalibrated ensemble while significantly reducing forecast uncertainty under all environmental stability conditions. The system also generates significantly better forecasts than persistence, autoregressive (AR) and autoregressive moving average (ARMA) models during the morning transition and the diurnal convective regimes. This forecasting system is computationally more efficient than traditional numerical weather prediction models and can generate a calibrated forecast, including model runs and calibration, in approximately 1 minute. Currently, hour-ahead wind speed forecasts are almost exclusively produced using statistical models. However, numerical models have several distinct advantages over statistical models including the potential to provide turbulence forecasts. Hence, there is an urgent need to explore the role of numerical models in short-term wind speed forecasting. This work is a step in that direction and is likely to trigger a debate within the wind speed forecasting community.

Forecasting vegetation greenness with satellite and climate data

USGS Publications Warehouse

Ji, Lei; Peters, Albert J.

2004-01-01

A new and unique vegetation greenness forecast (VGF) model was designed to predict future vegetation conditions to three months through the use of current and historical climate data and satellite imagery. The VGF model is implemented through a seasonality-adjusted autoregressive distributed-lag function, based on our finding that the normalized difference vegetation index is highly correlated with lagged precipitation and temperature. Accurate forecasts were obtained from the VGF model in Nebraska grassland and cropland. The regression R2 values range from 0.97-0.80 for 2-12 week forecasts, with higher R2 associated with a shorter prediction. An important application would be to produce real-time forecasts of greenness images.

The relationship between trading volumes, number of transactions, and stock volatility in GARCH models

NASA Astrophysics Data System (ADS)

Takaishi, Tetsuya; Chen, Ting Ting

2016-08-01

We examine the relationship between trading volumes, number of transactions, and volatility using daily stock data of the Tokyo Stock Exchange. Following the mixture of distributions hypothesis, we use trading volumes and the number of transactions as proxy for the rate of information arrivals affecting stock volatility. The impact of trading volumes or number of transactions on volatility is measured using the generalized autoregressive conditional heteroscedasticity (GARCH) model. We find that the GARCH effects, that is, persistence of volatility, is not always removed by adding trading volumes or number of transactions, indicating that trading volumes and number of transactions do not adequately represent the rate of information arrivals.

Markov Switching Autoregressive Conditional Heteroscedasticity (SWARCH) Model to Detect Financial Crisis in Indonesia Based on Import and Export Indicators

NASA Astrophysics Data System (ADS)

Sugiyanto; Zukhronah, Etik; Susanti, Yuliana; Rahma Dwi, Sisca

2017-06-01

A country is said to be a crisis when the financial system is experiencing a disruption that affects systems that can not function efficiently. The performance efficiency of macroeconomic indicators especially in imports and exports can be used to detect the financial crisis in Indonesia. Based on the import and export indicators from 1987 to 2015, the movement of these indicators can be modelled using SWARCH three states. The results showed that SWARCH (3,1) model was able to detect the crisis that occurred in Indonesia in 1997 and 2008. Using this model, it can be concluded that Indonesia is prone to financial crisis in 2016.

Nonlinear GARCH model and 1 / f noise

NASA Astrophysics Data System (ADS)

Kononovicius, A.; Ruseckas, J.

2015-06-01

Auto-regressive conditionally heteroskedastic (ARCH) family models are still used, by practitioners in business and economic policy making, as a conditional volatility forecasting models. Furthermore ARCH models still are attracting an interest of the researchers. In this contribution we consider the well known GARCH(1,1) process and its nonlinear modifications, reminiscent of NGARCH model. We investigate the possibility to reproduce power law statistics, probability density function and power spectral density, using ARCH family models. For this purpose we derive stochastic differential equations from the GARCH processes in consideration. We find the obtained equations to be similar to a general class of stochastic differential equations known to reproduce power law statistics. We show that linear GARCH(1,1) process has power law distribution, but its power spectral density is Brownian noise-like. However, the nonlinear modifications exhibit both power law distribution and power spectral density of the 1 /fβ form, including 1 / f noise.

Simulation of high-energy radiation belt electron fluxes using NARMAX-VERB coupled codes

PubMed Central

Pakhotin, I P; Drozdov, A Y; Shprits, Y Y; Boynton, R J; Subbotin, D A; Balikhin, M A

2014-01-01

This study presents a fusion of data-driven and physics-driven methodologies of energetic electron flux forecasting in the outer radiation belt. Data-driven NARMAX (Nonlinear AutoRegressive Moving Averages with eXogenous inputs) model predictions for geosynchronous orbit fluxes have been used as an outer boundary condition to drive the physics-based Versatile Electron Radiation Belt (VERB) code, to simulate energetic electron fluxes in the outer radiation belt environment. The coupled system has been tested for three extended time periods totalling several weeks of observations. The time periods involved periods of quiet, moderate, and strong geomagnetic activity and captured a range of dynamics typical of the radiation belts. The model has successfully simulated energetic electron fluxes for various magnetospheric conditions. Physical mechanisms that may be responsible for the discrepancies between the model results and observations are discussed. PMID:26167432

Conditional parametric models for storm sewer runoff

NASA Astrophysics Data System (ADS)

Jonsdottir, H.; Nielsen, H. Aa; Madsen, H.; Eliasson, J.; Palsson, O. P.; Nielsen, M. K.

2007-05-01

The method of conditional parametric modeling is introduced for flow prediction in a sewage system. It is a well-known fact that in hydrological modeling the response (runoff) to input (precipitation) varies depending on soil moisture and several other factors. Consequently, nonlinear input-output models are needed. The model formulation described in this paper is similar to the traditional linear models like final impulse response (FIR) and autoregressive exogenous (ARX) except that the parameters vary as a function of some external variables. The parameter variation is modeled by local lines, using kernels for local linear regression. As such, the method might be referred to as a nearest neighbor method. The results achieved in this study were compared to results from the conventional linear methods, FIR and ARX. The increase in the coefficient of determination is substantial. Furthermore, the new approach conserves the mass balance better. Hence this new approach looks promising for various hydrological models and analysis.

Equations of condition for high order Runge-Kutta-Nystrom formulae

NASA Technical Reports Server (NTRS)

Bettis, D. G.

1974-01-01

Derivation of the equations of condition of order eight for a general system of second-order differential equations approximated by the basic Runge-Kutta-Nystrom algorithm. For this general case, the number of equations of condition is considerably larger than for the special case where the first derivative is not present. Specifically, it is shown that, for orders two through eight, the number of equations for each order is 1, 1, 1, 2, 3, 5, and 9 for the special case and is 1, 1, 2, 5, 13, 34, and 95 for the general case.

Trends and annual cycles in soundings of Arctic tropospheric ozone

NASA Astrophysics Data System (ADS)

Christiansen, Bo; Jepsen, Nis; Kivi, Rigel; Hansen, Georg; Larsen, Niels; Smith Korsholm, Ulrik

2017-08-01

Ozone soundings from nine Nordic stations have been homogenized and interpolated to standard pressure levels. The different stations have very different data coverage; the longest period with data is from the end of the 1980s to 2014. At each pressure level the homogenized ozone time series have been analysed with a model that includes both low-frequency variability in the form of a polynomial, an annual cycle with harmonics, the possibility for low-frequency variability in the annual amplitude and phasing, and either white noise or noise given by a first-order autoregressive process. The fitting of the parameters is performed with a Bayesian approach not only giving the mean values but also confidence intervals. The results show that all stations agree on a well-defined annual cycle in the free troposphere with a relatively confined maximum in the early summer. Regarding the low-frequency variability, it is found that Scoresbysund, Ny Ålesund, Sodankylä, Eureka, and Ørland show similar, significant signals with a maximum near 2005 followed by a decrease. This change is characteristic for all pressure levels in the free troposphere. A significant change in the annual cycle was found for Ny Ålesund, Scoresbysund, and Sodankylä. The changes at these stations are in agreement with the interpretation that the early summer maximum is appearing earlier in the year. The results are shown to be robust to the different settings of the model parameters such as the order of the polynomial, number of harmonics in the annual cycle, and the type of noise.

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

NASA Astrophysics Data System (ADS)

Kurniati, Devi; Hoyyi, Abdul; Widiharih, Tatik

2018-05-01

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

Use of linear regression models to determine influence factors on the concentration levels of radon in occupied houses

NASA Astrophysics Data System (ADS)

Buermeyer, Jonas; Gundlach, Matthias; Grund, Anna-Lisa; Grimm, Volker; Spizyn, Alexander; Breckow, Joachim

2016-09-01

This work is part of the analysis of the effects of constructional energy-saving measures to radon concentration levels in dwellings performed on behalf of the German Federal Office for Radiation Protection. In parallel to radon measurements for five buildings, both meteorological data outside the buildings and the indoor climate factors were recorded. In order to access effects of inhabited buildings, the amount of carbon dioxide (CO2) was measured. For a statistical linear regression model, the data of one object was chosen as an example. Three dummy variables were extracted from the process of the CO2 concentration to provide information on the usage and ventilation of the room. The analysis revealed a highly autoregressive model for the radon concentration with additional influence by the natural environmental factors. The autoregression implies a strong dependency on a radon source since it reflects a backward dependency in time. At this point of the investigation, it cannot be determined whether the influence by outside factors affects the source of radon or the habitant’s ventilation behavior resulting in variation of the occurring concentration levels. In any case, the regression analysis might provide further information that would help to distinguish these effects. In the next step, the influence factors will be weighted according to their impact on the concentration levels. This might lead to a model that enables the prediction of radon concentration levels based on the measurement of CO2 in combination with environmental parameters, as well as the development of advices for ventilation.

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

PubMed

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

2017-09-01

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

Decentralized Quasi-Newton Methods

NASA Astrophysics Data System (ADS)

Eisen, Mark; Mokhtari, Aryan; Ribeiro, Alejandro

2017-05-01

We introduce the decentralized Broyden-Fletcher-Goldfarb-Shanno (D-BFGS) method as a variation of the BFGS quasi-Newton method for solving decentralized optimization problems. The D-BFGS method is of interest in problems that are not well conditioned, making first order decentralized methods ineffective, and in which second order information is not readily available, making second order decentralized methods impossible. D-BFGS is a fully distributed algorithm in which nodes approximate curvature information of themselves and their neighbors through the satisfaction of a secant condition. We additionally provide a formulation of the algorithm in asynchronous settings. Convergence of D-BFGS is established formally in both the synchronous and asynchronous settings and strong performance advantages relative to first order methods are shown numerically.

Seismicity Along the Endeavour Segment of the Juan de Fuca Ridge: Automated Event Locations for an Ocean-Bottom Seismometer Network

NASA Astrophysics Data System (ADS)

Weekly, R. T.; Wilcock, W. S.; Hooft, E. E.; Toomey, D. R.; McGill, P. R.

2004-12-01

From 2003-2006, the W.M. Keck Foundation supported the operation of a network of eight ocean-bottom seismometers (OBSs) that were deployed with a remotely operated vehicle along the central portion of the Endeavour Segment of the Juan de Fuca mid-ocean ridge as part of a multidisciplinary prototype NEPTUNE experiment. Data from 2003-2004 were initially analyzed during a research apprenticeship class at the University of Washington's Friday Harbor Laboratories. Eight student analysts located ~13,000 earthquakes along the Endeavour Segment. Analysis of data from 2004-2005 has to date been limited to locating ~6,000 earthquakes associated with a swarm in February-March 2005 near the northern end of the Endeavour Segment. The remaining data includes several significant swarms and it is anticipated that tens of thousands of earthquakes still need to be located. In order to efficiently obtain a complete catalog of high-quality locations for the 3-year experiment, we are developing an automatic method for earthquake location. We first apply a 5-Hz high-pass filter and identify triggers when the ratio of the root-mean square (RMS) amplitudes in short- and long- term windows exceeds a specified threshold. We search for events that are characterized by triggers within a short time interval on the majority of stations and use the signal spectra to eliminate events that are the result of 20-Hz Fin and Blue whale vocalizations. An autoregressive technique is applied to a short time window centered on the trigger time to pick P-wave times on each station's vertical channel. We locate the earthquake with these picks and either attempt to repick or eliminate arrivals with unacceptable residuals. Preliminary S-wave picks are then made on the horizontal channels by applying a 5-12 Hz bandpass filter, identifying the peak RMS amplitude for a short running window, and making a pick at the time the RMS amplitude rises above 50% of this value. The picks are refined using the autoregressive technique. We then locate earthquakes using the P- and S-wave picks and either repick or discard S-wave picks with unacceptable residuals before obtaining the final location. We have tested the method using data segments from 2003-2004 and find the catalog completeness and quality of locations is comparable to that obtained with the student analysts.

Seismicity Along the Endeavour Segment of the Juan de Fuca Ridge: Automated Event Locations for an Ocean-Bottom Seismometer Network

NASA Astrophysics Data System (ADS)

Weekly, R. T.; Wilcock, W. S.; Hooft, E. E.; Toomey, D. R.; McGill, P. R.

2007-12-01

From 2003-2006, the W.M. Keck Foundation supported the operation of a network of eight ocean-bottom seismometers (OBSs) that were deployed with a remotely operated vehicle along the central portion of the Endeavour Segment of the Juan de Fuca mid-ocean ridge as part of a multidisciplinary prototype NEPTUNE experiment. Data from 2003-2004 were initially analyzed during a research apprenticeship class at the University of Washington's Friday Harbor Laboratories. Eight student analysts located ~13,000 earthquakes along the Endeavour Segment. Analysis of data from 2004-2005 has to date been limited to locating ~6,000 earthquakes associated with a swarm in February-March 2005 near the northern end of the Endeavour Segment. The remaining data includes several significant swarms and it is anticipated that tens of thousands of earthquakes still need to be located. In order to efficiently obtain a complete catalog of high-quality locations for the 3-year experiment, we are developing an automatic method for earthquake location. We first apply a 5-Hz high-pass filter and identify triggers when the ratio of the root-mean square (RMS) amplitudes in short- and long- term windows exceeds a specified threshold. We search for events that are characterized by triggers within a short time interval on the majority of stations and use the signal spectra to eliminate events that are the result of 20-Hz Fin and Blue whale vocalizations. An autoregressive technique is applied to a short time window centered on the trigger time to pick P-wave times on each station's vertical channel. We locate the earthquake with these picks and either attempt to repick or eliminate arrivals with unacceptable residuals. Preliminary S-wave picks are then made on the horizontal channels by applying a 5-12 Hz bandpass filter, identifying the peak RMS amplitude for a short running window, and making a pick at the time the RMS amplitude rises above 50% of this value. The picks are refined using the autoregressive technique. We then locate earthquakes using the P- and S-wave picks and either repick or discard S-wave picks with unacceptable residuals before obtaining the final location. We have tested the method using data segments from 2003-2004 and find the catalog completeness and quality of locations is comparable to that obtained with the student analysts.

On the existence of touch points for first-order state inequality constraints

NASA Technical Reports Server (NTRS)

Seywald, Hans; Cliff, Eugene M.

1993-01-01

The appearance of touch points in state constrained optimal control problems with general vector-valued control is studied. Under the assumption that the Hamiltonian is regular, touch points for first-order state inequalities are shown to exist only under very special conditions. In many cases of practical importance these conditions can be used to exclude touch points a priori without solving an optimal control problem. The results are demonstrated on a simple example.

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

PubMed

Wagner, Jenny; Lüdtke, Oliver; Trautwein, Ulrich

2016-08-01

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

Autoregressive modelling of species richness in the Brazilian Cerrado.

PubMed

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

2008-05-01

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

Longitudinal Relationship between Self-efficacy and Posttraumatic Stress Symptoms 8 Years after a Violent Assault: An Autoregressive Cross-Lagged Model

PubMed Central

Nygaard, Egil; Johansen, Venke A.; Siqveland, Johan; Hussain, Ajmal; Heir, Trond

2017-01-01

Self-efficacy is assumed to promote posttraumatic adaption, and several cross-sectional studies support this notion. However, there is a lack of prospective longitudinal studies to further illuminate the temporal relationship between self-efficacy and posttraumatic stress symptoms. Thus, an important unresolved research question is whether posttraumatic stress disorder (PTSD) symptoms affect the level of self-efficacy or vice versa or whether they mutually influence each other. The present prospective longitudinal study investigated the reciprocal relationship between general self-efficacy (GSE) and posttraumatic stress symptoms in 143 physical assault victims. We used an autoregressive cross-lagged model across four assessment waves: within 4 months after the assault (T1) and then 3 months (T2), 12 months (T3) and 8 years (T4) after the first assessment. Stress symptoms at T1 and T2 predicted subsequent self-efficacy, while self-efficacy at T1 and T2 was not related to subsequent stress symptoms. These relationships were reversed after T3; higher levels of self-efficacy at T3 predicted lower levels of posttraumatic stress symptoms at T4, while posttraumatic tress symptoms at T3 did not predict self-efficacy at T4. In conclusion, posttraumatic stress symptoms may have a deteriorating effect on self-efficacy in the early phase after physical assault, whereas self-efficacy may promote recovery from posttraumatic stress symptoms over the long term. PMID:28620334

Longitudinal Relationship between Self-efficacy and Posttraumatic Stress Symptoms 8 Years after a Violent Assault: An Autoregressive Cross-Lagged Model.

PubMed

Nygaard, Egil; Johansen, Venke A; Siqveland, Johan; Hussain, Ajmal; Heir, Trond

2017-01-01

Self-efficacy is assumed to promote posttraumatic adaption, and several cross-sectional studies support this notion. However, there is a lack of prospective longitudinal studies to further illuminate the temporal relationship between self-efficacy and posttraumatic stress symptoms. Thus, an important unresolved research question is whether posttraumatic stress disorder (PTSD) symptoms affect the level of self-efficacy or vice versa or whether they mutually influence each other. The present prospective longitudinal study investigated the reciprocal relationship between general self-efficacy (GSE) and posttraumatic stress symptoms in 143 physical assault victims. We used an autoregressive cross-lagged model across four assessment waves: within 4 months after the assault (T1) and then 3 months (T2), 12 months (T3) and 8 years (T4) after the first assessment. Stress symptoms at T1 and T2 predicted subsequent self-efficacy, while self-efficacy at T1 and T2 was not related to subsequent stress symptoms. These relationships were reversed after T3; higher levels of self-efficacy at T3 predicted lower levels of posttraumatic stress symptoms at T4, while posttraumatic tress symptoms at T3 did not predict self-efficacy at T4. In conclusion, posttraumatic stress symptoms may have a deteriorating effect on self-efficacy in the early phase after physical assault, whereas self-efficacy may promote recovery from posttraumatic stress symptoms over the long term.

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.

Second-order numerical solution of time-dependent, first-order hyperbolic equations

NASA Technical Reports Server (NTRS)

Shah, Patricia L.; Hardin, Jay

1995-01-01

A finite difference scheme is developed to find an approximate solution of two similar hyperbolic equations, namely a first-order plane wave and spherical wave problem. Finite difference approximations are made for both the space and time derivatives. The result is a conditionally stable equation yielding an exact solution when the Courant number is set to one.

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

PubMed

Moran, John L; Solomon, Patricia J

2013-05-24

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

Multiple points of equilibrium for active magnetic regenerators using first order magnetocaloric material

NASA Astrophysics Data System (ADS)

Niknia, I.; Trevizoli, P. V.; Govindappa, P.; Christiaanse, T. V.; Teyber, R.; Rowe, A.

2018-05-01

First order transition material (FOM) usually exhibits magnetocaloric effects in a narrow temperature range which complicates their use in an active magnetic regenerator (AMR) refrigerator. In addition, the magnetocaloric effect in first order materials can vary with field and temperature history of the material. This study examines the behavior of a MnFe(P,Si) FOM sample in an AMR cycle using a numerical model and experimental measurements. For certain operating conditions, multiple points of equilibrium (MPE) exist for a fixed hot rejection temperature. Stable and unstable points of equilibriums (PEs) are identified and the impacts of heat loads, operating conditions, and configuration losses on the number of PEs are discussed. It is shown that the existence of multiple PEs can affect the performance of an AMR significantly for certain operating conditions. In addition, the points where MPEs exist appear to be linked to the device itself, not just the material, suggesting the need to layer a regenerator in a way that avoids MPE conditions and to layer with a specific device in mind.

A comparison of zero-order, first-order, and monod biotransformation models

USGS Publications Warehouse

Bekins, B.A.; Warren, E.; Godsy, E.M.

1998-01-01

Under some conditions, a first-order kinetic model is a poor representation of biodegradation in contaminated aquifers. Although it is well known that the assumption of first-order kinetics is valid only when substrate concentration, S, is much less than the half-saturation constant, K(s), this assumption is often made without verification of this condition. We present a formal error analysis showing that the relative error in the first-order approximation is S/K(S) and in the zero-order approximation the error is K(s)/S. We then examine the problems that arise when the first-order approximation is used outside the range for which it is valid. A series of numerical simulations comparing results of first- and zero-order rate approximations to Monod kinetics for a real data set illustrates that if concentrations observed in the field are higher than K(s), it may better to model degradation using a zero-order rate expression. Compared with Monod kinetics, extrapolation of a first-order rate to lower concentrations under-predicts the biotransformation potential, while extrapolation to higher concentrations may grossly over-predict the transformation rate. A summary of solubilities and Monod parameters for aerobic benzene, toluene, and xylene (BTX) degradation shows that the a priori assumption of first-order degradation kinetics at sites contaminated with these compounds is not valid. In particular, out of six published values of KS for toluene, only one is greater than 2 mg/L, indicating that when toluene is present in concentrations greater than about a part per million, the assumption of first-order kinetics may be invalid. Finally, we apply an existing analytical solution for steady-state one-dimensional advective transport with Monod degradation kinetics to a field data set.A formal error analysis is presented showing that the relative error in the first-order approximation is S/KS and in the zero-order approximation the error is KS/S where S is the substrate concentration and KS is the half-saturation constant. The problems that arise when the first-order approximation is used outside the range for which it is valid are examined. A series of numerical simulations comparing results of first- and zero-order rate approximations to Monod kinetics for a real data set illustrates that if concentrations observed in the field are higher than KS, it may be better to model degradation using a zero-order rate expression.

Rogue waves generation in a left-handed nonlinear transmission line with series varactor diodes

NASA Astrophysics Data System (ADS)

Onana Essama, B. G.; Atangana, J.; Biya Motto, F.; Mokhtari, B.; Cherkaoui Eddeqaqi, N.; Kofane, Timoleon C.

2014-07-01

We investigate the electromagnetic wave behavior and its characterization using collective variables technique. Second-order dispersion, first- and second-order nonlinearities, which strongly act in a left-handed nonlinear transmission line with series varactor diodes, are taken into account. Four frequency ranges have been found. The first one gives the so-called energetic soliton due to a perfect combination of second-order dispersion and first-order nonlinearity. The second frequency range presents a dispersive soliton leading to the collapse of the electromagnetic wave at the third frequency range. But the fourth one shows physical conditions which are able to provoke the appearance of wave trains generation with some particular waves, the rogue waves. Moreover, we demonstrate that the number of rogue waves increases with frequency. The soliton, thereafter, gains a relative stability when second-order nonlinearity comes into play with some specific values in the fourth frequency range. Furthermore, the stability conditions of the electromagnetic wave at high frequencies have been also discussed.

Along the Rainfall-Runoff Chain: From Scaling of Greatest Point Rainfall to Global Change Attribution

NASA Astrophysics Data System (ADS)

Fraedrich, K.

2014-12-01

Processes along the continental rainfall-runoff chain cover a wide range of time and space scales which are presented here combining observations (ranging from minutes to decades) and minimalist concepts. (i) Rainfall, which can be simulated by a censored first-order autoregressive process (vertical moisture fluxes), exhibits 1/f-spectra if presented as binary events (tropics), while extrema world wide increase with duration according to Jennings' scaling law. (ii) Runoff volatility (Yangtze) shows data collapse which, linked to an intra-annual 1/f-spectrum, is represented by a single function not unlike physical systems at criticality and the short and long return times of extremes are Weibull-distributed. Atmospheric and soil moisture variabilities are also discussed. (iii) Soil moisture (in a bucket), whose variability is interpreted by a biased coinflip Ansatz for rainfall events, adds an equation of state to energy and water flux balances comprising Budyko's frame work for quasi-stationary watershed analysis. Eco-hydrologic state space presentations in terms of surface flux ratios of energy excess (loss by sensible heat over supply by net radiation) versus water excess (loss by discharge over gain by precipitation) allow attributions of state change to external (or climate) and internal (or anthropogenic) causes. Including the vegetation-greenness index (NDVI) as an active tracer extends the eco-hydrologic state space analysis to supplement the common geographical presentations. Two examples demonstrate the approach combining ERA and MODIS data sets: (a) global geobotanic classification by combining first and second moments of the dryness ratio (net radiation over precipitation) and (b) regional attributions (Tibetan Plateau) of vegetation changes.

High Incidence of Breast Cancer in Light-Polluted Areas with Spatial Effects in Korea.

PubMed

Kim, Yun Jeong; Park, Man Sik; Lee, Eunil; Choi, Jae Wook

2016-01-01

We have reported a high prevalence of breast cancer in light-polluted areas in Korea. However, it is necessary to analyze the spatial effects of light polluted areas on breast cancer because light pollution levels are correlated with region proximity to central urbanized areas in studied cities. In this study, we applied a spatial regression method (an intrinsic conditional autoregressive [iCAR] model) to analyze the relationship between the incidence of breast cancer and artificial light at night (ALAN) levels in 25 regions including central city, urbanized, and rural areas. By Poisson regression analysis, there was a significant correlation between ALAN, alcohol consumption rates, and the incidence of breast cancer. We also found significant spatial effects between ALAN and the incidence of breast cancer, with an increase in the deviance information criterion (DIC) from 374.3 to 348.6 and an increase in R2 from 0.574 to 0.667. Therefore, spatial analysis (an iCAR model) is more appropriate for assessing ALAN effects on breast cancer. To our knowledge, this study is the first to show spatial effects of light pollution on breast cancer, despite the limitations of an ecological study. We suggest that a decrease in ALAN could reduce breast cancer more than expected because of spatial effects.

Higher derivative field theories: degeneracy conditions and classes

NASA Astrophysics Data System (ADS)

Crisostomi, Marco; Klein, Remko; Roest, Diederik

2017-06-01

We provide a full analysis of ghost free higher derivative field theories with coupled degrees of freedom. Assuming the absence of gauge symmetries, we derive the degeneracy conditions in order to evade the Ostrogradsky ghosts, and analyze which (non)trivial classes of solutions this allows for. It is shown explicitly how Lorentz invariance avoids the propagation of "half" degrees of freedom. Moreover, for a large class of theories, we construct the field redefinitions and/or (extended) contact transformations that put the theory in a manifestly first order form. Finally, we identify which class of theories cannot be brought to first order form by such transformations.

GOBF-ARMA based model predictive control for an ideal reactive distillation column.

PubMed

Seban, Lalu; Kirubakaran, V; Roy, B K; Radhakrishnan, T K

2015-11-01

This paper discusses the control of an ideal reactive distillation column (RDC) using model predictive control (MPC) based on a combination of deterministic generalized orthonormal basis filter (GOBF) and stochastic autoregressive moving average (ARMA) models. Reactive distillation (RD) integrates reaction and distillation in a single process resulting in process and energy integration promoting green chemistry principles. Improved selectivity of products, increased conversion, better utilization and control of reaction heat, scope for difficult separations and the avoidance of azeotropes are some of the advantages that reactive distillation offers over conventional technique of distillation column after reactor. The introduction of an in situ separation in the reaction zone leads to complex interactions between vapor-liquid equilibrium, mass transfer rates, diffusion and chemical kinetics. RD with its high order and nonlinear dynamics, and multiple steady states is a good candidate for testing and verification of new control schemes. Here a combination of GOBF-ARMA models is used to catch and represent the dynamics of the RDC. This GOBF-ARMA model is then used to design an MPC scheme for the control of product purity of RDC under different operating constraints and conditions. The performance of proposed modeling and control using GOBF-ARMA based MPC is simulated and analyzed. The proposed controller is found to perform satisfactorily for reference tracking and disturbance rejection in RDC. Copyright © 2015 Elsevier Inc. All rights reserved.

Multivariate spatial models of excess crash frequency at area level: case of Costa Rica.

PubMed

Aguero-Valverde, Jonathan

2013-10-01

Recently, areal models of crash frequency have being used in the analysis of various area-wide factors affecting road crashes. On the other hand, disease mapping methods are commonly used in epidemiology to assess the relative risk of the population at different spatial units. A natural next step is to combine these two approaches to estimate the excess crash frequency at area level as a measure of absolute crash risk. Furthermore, multivariate spatial models of crash severity are explored in order to account for both frequency and severity of crashes and control for the spatial correlation frequently found in crash data. This paper aims to extent the concept of safety performance functions to be used in areal models of crash frequency. A multivariate spatial model is used for that purpose and compared to its univariate counterpart. Full Bayes hierarchical approach is used to estimate the models of crash frequency at canton level for Costa Rica. An intrinsic multivariate conditional autoregressive model is used for modeling spatial random effects. The results show that the multivariate spatial model performs better than its univariate counterpart in terms of the penalized goodness-of-fit measure Deviance Information Criteria. Additionally, the effects of the spatial smoothing due to the multivariate spatial random effects are evident in the estimation of excess equivalent property damage only crashes. Copyright © 2013 Elsevier Ltd. All rights reserved.

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

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

Abbas, Nikhar; Tom, Nathan M

2017-06-03

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

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

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

Abbas, Nikhar; Tom, Nathan

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

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

NASA Astrophysics Data System (ADS)

Pal, Debdatta; Mitra, Subrata Kumar

2018-01-01

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

The discrete one-sided Lipschitz condition for convex scalar conservation laws

NASA Technical Reports Server (NTRS)

Brenier, Yann; Osher, Stanley

1986-01-01

Physical solutions to convex scalar conservation laws satisfy a one-sided Lipschitz condition (OSLC) that enforces both the entropy condition and their variation boundedness. Consistency with this condition is therefore desirable for a numerical scheme and was proved for both the Godunov and the Lax-Friedrichs scheme--also, in a weakened version, for the Roe scheme, all of them being only first order accurate. A new, fully second order scheme is introduced here, which is consistent with the OSLC. The modified equation is considered and shows interesting features. Another second order scheme is then considered and numerical results are discussed.

Modeling and simulation of continuous wave velocity radar based on third-order DPLL

NASA Astrophysics Data System (ADS)

Di, Yan; Zhu, Chen; Hong, Ma

2015-02-01

Second-order digital phase-locked-loop (DPLL) is widely used in traditional Continuous wave (CW) velocity radar with poor performance in high dynamic conditions. Using the third-order DPLL can improve the performance. Firstly, the echo signal model of CW radar is given. Secondly, theoretical derivations of the tracking performance in different velocity conditions are given. Finally, simulation model of CW radar is established based on Simulink tool. Tracking performance of the two kinds of DPLL in different acceleration and jerk conditions is studied by this model. The results show that third-order PLL has better performance in high dynamic conditions. This model provides a platform for further research of CW radar.

A novel method for pediatric heart sound segmentation without using the ECG.

PubMed

Sepehri, Amir A; Gharehbaghi, Arash; Dutoit, Thierry; Kocharian, Armen; Kiani, A

2010-07-01

In this paper, we propose a novel method for pediatric heart sounds segmentation by paying special attention to the physiological effects of respiration on pediatric heart sounds. The segmentation is accomplished in three steps. First, the envelope of a heart sounds signal is obtained with emphasis on the first heart sound (S(1)) and the second heart sound (S(2)) by using short time spectral energy and autoregressive (AR) parameters of the signal. Then, the basic heart sounds are extracted taking into account the repetitive and spectral characteristics of S(1) and S(2) sounds by using a Multi-Layer Perceptron (MLP) neural network classifier. In the final step, by considering the diastolic and systolic intervals variations due to the effect of a child's respiration, a complete and precise heart sounds end-pointing and segmentation is achieved. Copyright 2009 Elsevier Ireland Ltd. All rights reserved.

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

PubMed Central

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

2012-01-01

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

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.

Nonlinear System Identification for Aeroelastic Systems with Application to Experimental Data

NASA Technical Reports Server (NTRS)

Kukreja, Sunil L.

2008-01-01

Representation and identification of a nonlinear aeroelastic pitch-plunge system as a model of the Nonlinear AutoRegressive, Moving Average eXogenous (NARMAX) class is considered. A nonlinear difference equation describing this aircraft model is derived theoretically and shown to be of the NARMAX form. Identification methods for NARMAX models are applied to aeroelastic dynamics and its properties demonstrated via continuous-time simulations of experimental conditions. Simulation results show that (1) the outputs of the NARMAX model closely match those generated using continuous-time methods, and (2) NARMAX identification methods applied to aeroelastic dynamics provide accurate discrete-time parameter estimates. Application of NARMAX identification to experimental pitch-plunge dynamics data gives a high percent fit for cross-validated data.

Dedollarization in Turkey after decades of dollarization: A myth or reality?

NASA Astrophysics Data System (ADS)

Metin-Özcan, Kıvılcım; Us, Vuslat

2007-11-01

The paper analyzes dollarization in the Turkish economy given the evidence on dedollarization signals. On conducting a Vector Autoregression (VAR) model, the empirical evidence suggests that dollarization has mostly been shaped by macroeconomic imbalances as measured by exchange rate depreciation volatility, inflation volatility and expectations. Furthermore, the generalized impulse response function (IRF) analysis, in addition to the analysis of variance decomposition (VDC) gives support to the notion that dollarization seems to sustain its persistent nature, thus hysteresis still prevails. Hence, unfavorable macroeconomic conditions apparently contribute to dollarization while dollarization itself contains inertia. Furthermore, dedollarization that presumably started after 2001 has lost headway after May 2006. Thus, it seems too early to conclude that dollarization changed its route to dedollarization.

Effective refractive index and first-order-mode cutoff conditions in InGaAsP/InP DH laser structures /lambda = 1.2-1.6 microns/

NASA Technical Reports Server (NTRS)

Botez, D.

1982-01-01

A highly accurate analytical expression for the effective refractive index in In GaAsP/InP DH lasers emitting in the 1.2-1.6 micron range is presented. This closed-form expression is used to derive simple wavelength-independent expressions for the first-order mode cutoff conditions of various lateral waveguides. The effective refractive index is a function of emission wavelength and active layer thickness, and the mode cutoff conditions are compared to experimental data from mode-stabilized 1.3 and 1.55 micron DH lasers.

High-order finite-volume solutions of the steady-state advection-diffusion equation with nonlinear Robin boundary conditions

NASA Astrophysics Data System (ADS)

Lin, Zhi; Zhang, Qinghai

2017-09-01

We propose high-order finite-volume schemes for numerically solving the steady-state advection-diffusion equation with nonlinear Robin boundary conditions. Although the original motivation comes from a mathematical model of blood clotting, the nonlinear boundary conditions may also apply to other scientific problems. The main contribution of this work is a generic algorithm for generating third-order, fourth-order, and even higher-order explicit ghost-filling formulas to enforce nonlinear Robin boundary conditions in multiple dimensions. Under the framework of finite volume methods, this appears to be the first algorithm of its kind. Numerical experiments on boundary value problems show that the proposed fourth-order formula can be much more accurate and efficient than a simple second-order formula. Furthermore, the proposed ghost-filling formulas may also be useful for solving other partial differential equations.

Identifying effective connectivity parameters in simulated fMRI: a direct comparison of switching linear dynamic system, stochastic dynamic causal, and multivariate autoregressive models

PubMed Central

Smith, Jason F.; Chen, Kewei; Pillai, Ajay S.; Horwitz, Barry

2013-01-01

The number and variety of connectivity estimation methods is likely to continue to grow over the coming decade. Comparisons between methods are necessary to prune this growth to only the most accurate and robust methods. However, the nature of connectivity is elusive with different methods potentially attempting to identify different aspects of connectivity. Commonalities of connectivity definitions across methods upon which base direct comparisons can be difficult to derive. Here, we explicitly define “effective connectivity” using a common set of observation and state equations that are appropriate for three connectivity methods: dynamic causal modeling (DCM), multivariate autoregressive modeling (MAR), and switching linear dynamic systems for fMRI (sLDSf). In addition while deriving this set, we show how many other popular functional and effective connectivity methods are actually simplifications of these equations. We discuss implications of these connections for the practice of using one method to simulate data for another method. After mathematically connecting the three effective connectivity methods, simulated fMRI data with varying numbers of regions and task conditions is generated from the common equation. This simulated data explicitly contains the type of the connectivity that the three models were intended to identify. Each method is applied to the simulated data sets and the accuracy of parameter identification is analyzed. All methods perform above chance levels at identifying correct connectivity parameters. The sLDSf method was superior in parameter estimation accuracy to both DCM and MAR for all types of comparisons. PMID:23717258

A Unified Estimation Framework for State-Related Changes in Effective Brain Connectivity.

PubMed

Samdin, S Balqis; Ting, Chee-Ming; Ombao, Hernando; Salleh, Sh-Hussain

2017-04-01

This paper addresses the critical problem of estimating time-evolving effective brain connectivity. Current approaches based on sliding window analysis or time-varying coefficient models do not simultaneously capture both slow and abrupt changes in causal interactions between different brain regions. To overcome these limitations, we develop a unified framework based on a switching vector autoregressive (SVAR) model. Here, the dynamic connectivity regimes are uniquely characterized by distinct vector autoregressive (VAR) processes and allowed to switch between quasi-stationary brain states. The state evolution and the associated directed dependencies are defined by a Markov process and the SVAR parameters. We develop a three-stage estimation algorithm for the SVAR model: 1) feature extraction using time-varying VAR (TV-VAR) coefficients, 2) preliminary regime identification via clustering of the TV-VAR coefficients, 3) refined regime segmentation by Kalman smoothing and parameter estimation via expectation-maximization algorithm under a state-space formulation, using initial estimates from the previous two stages. The proposed framework is adaptive to state-related changes and gives reliable estimates of effective connectivity. Simulation results show that our method provides accurate regime change-point detection and connectivity estimates. In real applications to brain signals, the approach was able to capture directed connectivity state changes in functional magnetic resonance imaging data linked with changes in stimulus conditions, and in epileptic electroencephalograms, differentiating ictal from nonictal periods. The proposed framework accurately identifies state-dependent changes in brain network and provides estimates of connectivity strength and directionality. The proposed approach is useful in neuroscience studies that investigate the dynamics of underlying brain states.

Depression and smoking: a 5-year prospective study of patients with major depressive disorder.

PubMed

Holma, Irina A K; Holma, K Mikael; Melartin, Tarja K; Ketokivi, Mikko; Isometsä, Erkki T

2013-06-01

Major depressive disorder (MDD) and smoking are major public health problems and epidemiologically strongly associated. However, the relationship between smoking and depression and whether this is influenced by common confounding factors remain unclear, in part due to limited longitudinal data on covariation. In the Vantaa Depression Study, psychiatric out- and inpatients with DSM-IV MDD and aged 20-59 years at were followed from baseline to 6 months, 18 months, and 5 years. We investigated course of depression, smoking, and comorbid alcohol-use disorders among the 214 patients (79.6% of 269) participating at least three time points; differences between smoking versus nonsmoking patients, and covariation of MDD, smoking, and alcohol-use disorders. Overall, 31.3% of the patients smoked regularly, 41.1% intermittently, and 27.6% never. Smokers were younger, had more alcohol-use disorders and Cluster B and C personality disorder symptoms, a higher frequency of lifetime suicide attempts, higher neuroticism, smaller social networks, and lower perceived social support than never smokers. Smoking and depression had limited longitudinal covariation. Depression, smoking, and alcohol-use disorders all exhibited strong autoregressive tendencies. Among adult psychiatric MDD patients, smoking is strongly associated with substance-use and personality disorders, which may confound research on the impact of smoking. Rather than depression or smoking covarying or predicting each other, depression, smoking, and alcohol-use disorders each have strong autoregressive tendencies. These findings are more consistent with common factors causing their association than either of the conditions strongly predisposing to the other. © 2013 Wiley Periodicals, Inc.

Assessing artificial neural networks and statistical methods for infilling missing soil moisture records

NASA Astrophysics Data System (ADS)

Dumedah, Gift; Walker, Jeffrey P.; Chik, Li

2014-07-01

Soil moisture information is critically important for water management operations including flood forecasting, drought monitoring, and groundwater recharge estimation. While an accurate and continuous record of soil moisture is required for these applications, the available soil moisture data, in practice, is typically fraught with missing values. There are a wide range of methods available to infilling hydrologic variables, but a thorough inter-comparison between statistical methods and artificial neural networks has not been made. This study examines 5 statistical methods including monthly averages, weighted Pearson correlation coefficient, a method based on temporal stability of soil moisture, and a weighted merging of the three methods, together with a method based on the concept of rough sets. Additionally, 9 artificial neural networks are examined, broadly categorized into feedforward, dynamic, and radial basis networks. These 14 infilling methods were used to estimate missing soil moisture records and subsequently validated against known values for 13 soil moisture monitoring stations for three different soil layer depths in the Yanco region in southeast Australia. The evaluation results show that the top three highest performing methods are the nonlinear autoregressive neural network, rough sets method, and monthly replacement. A high estimation accuracy (root mean square error (RMSE) of about 0.03 m/m) was found in the nonlinear autoregressive network, due to its regression based dynamic network which allows feedback connections through discrete-time estimation. An equally high accuracy (0.05 m/m RMSE) in the rough sets procedure illustrates the important role of temporal persistence of soil moisture, with the capability to account for different soil moisture conditions.

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

PubMed

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

2010-04-01

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

A statistical approach for generating synthetic tip stress data from limited CPT soundings

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

Basalams, M.K.

CPT tip stress data obtained from a Uranium mill tailings impoundment are treated as time series. A statistical class of models that was developed to model time series is explored to investigate its applicability in modeling the tip stress series. These models were developed by Box and Jenkins (1970) and are known as Autoregressive Moving Average (ARMA) models. This research demonstrates how to apply the ARMA models to tip stress series. Generation of synthetic tip stress series that preserve the main statistical characteristics of the measured series is also investigated. Multiple regression analysis is used to model the regional variationmore » of the ARMA model parameters as well as the regional variation of the mean and the standard deviation of the measured tip stress series. The reliability of the generated series is investigated from a geotechnical point of view as well as from a statistical point of view. Estimation of the total settlement using the measured and the generated series subjected to the same loading condition are performed. The variation of friction angle with depth of the impoundment materials is also investigated. This research shows that these series can be modeled by the Box and Jenkins ARMA models. A third degree Autoregressive model AR(3) is selected to represent these series. A theoretical double exponential density function is fitted to the AR(3) model residuals. Synthetic tip stress series are generated at nearby locations. The generated series are shown to be reliable in estimating the total settlement and the friction angle variation with depth for this particular site.« less

Sufficient conditions for asymptotic stability and stabilization of autonomous fractional order systems

NASA Astrophysics Data System (ADS)

Lenka, Bichitra Kumar; Banerjee, Soumitro

2018-03-01

We discuss the asymptotic stability of autonomous linear and nonlinear fractional order systems where the state equations contain same or different fractional orders which lie between 0 and 2. First, we use the Laplace transform method to derive some sufficient conditions which ensure asymptotic stability of linear fractional order systems. Then by using the obtained results and linearization technique, a stability theorem is presented for autonomous nonlinear fractional order system. Finally, we design a control strategy for stabilization of autonomous nonlinear fractional order systems, and apply the results to the chaotic fractional order Lorenz system in order to verify its effectiveness.

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

USGS Publications Warehouse

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

1999-01-01

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

Absorbing boundary conditions for second-order hyperbolic equations

NASA Technical Reports Server (NTRS)

Jiang, Hong; Wong, Yau Shu

1989-01-01

A uniform approach to construct absorbing artificial boundary conditions for second-order linear hyperbolic equations is proposed. The nonlocal boundary condition is given by a pseudodifferential operator that annihilates travelling waves. It is obtained through the dispersion relation of the differential equation by requiring that the initial-boundary value problem admits the wave solutions travelling in one direction only. Local approximation of this global boundary condition yields an nth-order differential operator. It is shown that the best approximations must be in the canonical forms which can be factorized into first-order operators. These boundary conditions are perfectly absorbing for wave packets propagating at certain group velocities. A hierarchy of absorbing boundary conditions is derived for transonic small perturbation equations of unsteady flows. These examples illustrate that the absorbing boundary conditions are easy to derive, and the effectiveness is demonstrated by the numerical experiments.

Estimates of Zenith Total Delay trends from GPS reprocessing with autoregressive process

NASA Astrophysics Data System (ADS)

Klos, Anna; Hunegnaw, Addisu; Teferle, Felix Norman; Ebuy Abraha, Kibrom; Ahmed, Furqan; Bogusz, Janusz

2017-04-01

Nowadays, near real-time Zenith Total Delay (ZTD) estimates from Global Positioning System (GPS) observations are routinely assimilated into numerical weather prediction (NWP) models to improve the reliability of forecasts. On the other hand, ZTD time series derived from homogeneously re-processed GPS observations over long periods have the potential to improve our understanding of climate change on various temporal and spatial scales. With such time series only recently reaching somewhat adequate time spans, the application of GPS-derived ZTD estimates to climate monitoring is still to be developed further. In this research, we examine the character of noise in ZTD time series for 1995-2015 in order to estimate more realistic magnitudes of trend and its uncertainty than would be the case if the stochastic properties are not taken into account. Furthermore, the hourly sampled, homogeneously re-processed and carefully homogenized ZTD time series from over 700 globally distributed stations were classified into five major climate zones. We found that the amplitudes of annual signals reach values of 10-150 mm with minimum values for the polar and Alpine zones. The amplitudes of daily signals were estimated to be 0-12 mm with maximum values found for the dry zone. We examined seven different noise models for the residual ZTD time series after modelling all known periodicities. This identified a combination of white plus autoregressive process of fourth order to be optimal to match all changes in power of the ZTD data. When the stochastic properties are neglected, ie. a pure white noise model is employed, only 11 from 120 trends were insignificant. Using the optimum noise model more than half of the 120 examined trends became insignificant. We show that the uncertainty of ZTD trends is underestimated by a factor of 3-12 when the stochastic properties of the ZTD time series are ignored and we conclude that it is essential to properly model the noise characteristics of such time series when interpretations in terms of climate change are to be performed.

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

NASA Astrophysics Data System (ADS)

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

2012-06-01

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

Prediction of MeV electron fluxes throughout the outer radiation belt using multivariate autoregressive models

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

Sakaguchi, Kaori; Nagatsuma, Tsutomu; Reeves, Geoffrey D.

The Van Allen radiation belts surrounding the Earth are filled with MeV-energy electrons. This region poses ionizing radiation risks for spacecraft that operate within it, including those in geostationary orbit (GEO) and medium Earth orbit. In order to provide alerts of electron flux enhancements, 16 prediction models of the electron log-flux variation throughout the equatorial outer radiation belt as a function of the McIlwain L parameter were developed using the multivariate autoregressive model and Kalman filter. Measurements of omnidirectional 2.3 MeV electron flux from the Van Allen Probes mission as well as >2 MeV electrons from the GOES 15 spacecraftmore » were used as the predictors. Furthermore, we selected model explanatory parameters from solar wind parameters, the electron log-flux at GEO, and geomagnetic indices. For the innermost region of the outer radiation belt, the electron flux is best predicted by using the Dst index as the sole input parameter. For the central to outermost regions, at L≥4.8 and L ≥5.6, the electron flux is predicted most accurately by including also the solar wind velocity and then the dynamic pressure, respectively. The Dst index is the best overall single parameter for predicting at 3 ≤ L ≤ 6, while for the GEO flux prediction, the K P index is better than Dst. Finally, a test calculation demonstrates that the model successfully predicts the timing and location of the flux maximum as much as 2 days in advance and that the electron flux decreases faster with time at higher L values, both model features consistent with the actually observed behavior.« less

Prediction of MeV electron fluxes throughout the outer radiation belt using multivariate autoregressive models

DOE PAGES

Sakaguchi, Kaori; Nagatsuma, Tsutomu; Reeves, Geoffrey D.; ...

2015-12-22

The Van Allen radiation belts surrounding the Earth are filled with MeV-energy electrons. This region poses ionizing radiation risks for spacecraft that operate within it, including those in geostationary orbit (GEO) and medium Earth orbit. In order to provide alerts of electron flux enhancements, 16 prediction models of the electron log-flux variation throughout the equatorial outer radiation belt as a function of the McIlwain L parameter were developed using the multivariate autoregressive model and Kalman filter. Measurements of omnidirectional 2.3 MeV electron flux from the Van Allen Probes mission as well as >2 MeV electrons from the GOES 15 spacecraftmore » were used as the predictors. Furthermore, we selected model explanatory parameters from solar wind parameters, the electron log-flux at GEO, and geomagnetic indices. For the innermost region of the outer radiation belt, the electron flux is best predicted by using the Dst index as the sole input parameter. For the central to outermost regions, at L≥4.8 and L ≥5.6, the electron flux is predicted most accurately by including also the solar wind velocity and then the dynamic pressure, respectively. The Dst index is the best overall single parameter for predicting at 3 ≤ L ≤ 6, while for the GEO flux prediction, the K P index is better than Dst. Finally, a test calculation demonstrates that the model successfully predicts the timing and location of the flux maximum as much as 2 days in advance and that the electron flux decreases faster with time at higher L values, both model features consistent with the actually observed behavior.« less

Robust Prediction for Stationary Processes. 2D Enriched Version.

DTIC Science & Technology

1987-11-24

the absence of data outliers. Important performance characteristics studied include the breakdown point and the influence function . Included are numerical results, for some autoregressive nominal processes.

Analyzing brain networks with PCA and conditional Granger causality.

PubMed

Zhou, Zhenyu; Chen, Yonghong; Ding, Mingzhou; Wright, Paul; Lu, Zuhong; Liu, Yijun

2009-07-01

Identifying directional influences in anatomical and functional circuits presents one of the greatest challenges for understanding neural computations in the brain. Granger causality mapping (GCM) derived from vector autoregressive models of data has been employed for this purpose, revealing complex temporal and spatial dynamics underlying cognitive processes. However, the traditional GCM methods are computationally expensive, as signals from thousands of voxels within selected regions of interest (ROIs) are individually processed, and being based on pairwise Granger causality, they lack the ability to distinguish direct from indirect connectivity among brain regions. In this work a new algorithm called PCA based conditional GCM is proposed to overcome these problems. The algorithm implements the following two procedures: (i) dimensionality reduction in ROIs of interest with principle component analysis (PCA), and (ii) estimation of the direct causal influences in local brain networks, using conditional Granger causality. Our results show that the proposed method achieves greater accuracy in detecting network connectivity than the commonly used pairwise Granger causality method. Furthermore, the use of PCA components in conjunction with conditional GCM greatly reduces the computational cost relative to the use of individual voxel time series. Copyright 2009 Wiley-Liss, Inc

How are warm and cool years in the California Current related to ENSO?

NASA Astrophysics Data System (ADS)

Fiedler, Paul C.; Mantua, Nathan J.

2017-07-01

The tropical El Niño-Southern Oscillation (ENSO) is a dominant mode of interannual variability that impacts climate throughout the Pacific. The California Current System (CCS) in the northeast Pacific warms and cools from year to year, with or without a corresponding tropical El Niño or La Niña event. We update the record of warm and cool events in the CCS for 1950-2016 and use composite sea level pressure (SLP) and surface wind anomalies to explore the atmospheric forcing mechanisms associated with tropical and CCS warm and cold events. CCS warm events are associated with negative SLP anomalies in the NE Pacific—a strong and southeastward displacement of the wintertime Aleutian Low, a weak North Pacific High, and a regional pattern of cyclonic wind anomalies that are poleward over the CCS. We use a first-order autoregressive model to show that regional North Pacific forcing is predominant in SST variations throughout most of the CCS, while remote tropical forcing is more important in the far southern portion of the CCS. In our analysis, cool events in the CCS tend to be more closely associated with tropical La Niña than are warm events in the CCS with tropical El Niño; the forcing of co-occurring cool events is analogous, but nearly opposite, to that of warm events.

Subject-level reliability analysis of fast fMRI with application to epilepsy.

PubMed

Hao, Yongfu; Khoo, Hui Ming; von Ellenrieder, Nicolas; Gotman, Jean

2017-07-01

Recent studies have applied the new magnetic resonance encephalography (MREG) sequence to the study of interictal epileptic discharges (IEDs) in the electroencephalogram (EEG) of epileptic patients. However, there are no criteria to quantitatively evaluate different processing methods, to properly use the new sequence. We evaluated different processing steps of this new sequence under the common generalized linear model (GLM) framework by assessing the reliability of results. A bootstrap sampling technique was first used to generate multiple replicated data sets; a GLM with different processing steps was then applied to obtain activation maps, and the reliability of these maps was assessed. We applied our analysis in an event-related GLM related to IEDs. A higher reliability was achieved by using a GLM with head motion confound regressor with 24 components rather than the usual 6, with an autoregressive model of order 5 and with a canonical hemodynamic response function (HRF) rather than variable latency or patient-specific HRFs. Comparison of activation with IED field also favored the canonical HRF, consistent with the reliability analysis. The reliability analysis helps to optimize the processing methods for this fast fMRI sequence, in a context in which we do not know the ground truth of activation areas. Magn Reson Med 78:370-382, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

Statistical significance of seasonal warming/cooling trends

NASA Astrophysics Data System (ADS)

Ludescher, Josef; Bunde, Armin; Schellnhuber, Hans Joachim

2017-04-01

The question whether a seasonal climate trend (e.g., the increase of summer temperatures in Antarctica in the last decades) is of anthropogenic or natural origin is of great importance for mitigation and adaption measures alike. The conventional significance analysis assumes that (i) the seasonal climate trends can be quantified by linear regression, (ii) the different seasonal records can be treated as independent records, and (iii) the persistence in each of these seasonal records can be characterized by short-term memory described by an autoregressive process of first order. Here we show that assumption ii is not valid, due to strong intraannual correlations by which different seasons are correlated. We also show that, even in the absence of correlations, for Gaussian white noise, the conventional analysis leads to a strong overestimation of the significance of the seasonal trends, because multiple testing has not been taken into account. In addition, when the data exhibit long-term memory (which is the case in most climate records), assumption iii leads to a further overestimation of the trend significance. Combining Monte Carlo simulations with the Holm-Bonferroni method, we demonstrate how to obtain reliable estimates of the significance of the seasonal climate trends in long-term correlated records. For an illustration, we apply our method to representative temperature records from West Antarctica, which is one of the fastest-warming places on Earth and belongs to the crucial tipping elements in the Earth system.

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

NASA Astrophysics Data System (ADS)

Jin, Yishuai; Rong, Xinyao; Liu, Zhengyu

2017-12-01

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

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

PubMed

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

2000-02-21

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

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

NASA Astrophysics Data System (ADS)

Yu, Bing; Shu, Wenjun; Cao, Can

2018-05-01

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

Second-Order Conditioning of Human Causal Learning

ERIC Educational Resources Information Center

Jara, Elvia; Vila, Javier; Maldonado, Antonio

2006-01-01

This article provides the first demonstration of a reliable second-order conditioning (SOC) effect in human causal learning tasks. It demonstrates the human ability to infer relationships between a cause and an effect that were never paired together during training. Experiments 1a and 1b showed a clear and reliable SOC effect, while Experiments 2a…

zeldovich-PLT: Zel'dovich approximation initial conditions generator

NASA Astrophysics Data System (ADS)

Eisenstein, Daniel; Garrison, Lehman

2016-05-01

zeldovich-PLT generates Zel'dovich approximation (ZA) initial conditions (i.e. first-order Lagrangian perturbation theory) for cosmological N-body simulations, optionally applying particle linear theory (PLT) corrections.

The theoretical accuracy of Runge-Kutta time discretizations for the initial boundary value problem: A careful study of the boundary error

NASA Technical Reports Server (NTRS)

Carpenter, Mark H.; Gottlieb, David; Abarbanel, Saul; Don, Wai-Sun

1993-01-01

The conventional method of imposing time dependent boundary conditions for Runge-Kutta (RK) time advancement reduces the formal accuracy of the space-time method to first order locally, and second order globally, independently of the spatial operator. This counter intuitive result is analyzed in this paper. Two methods of eliminating this problem are proposed for the linear constant coefficient case: (1) impose the exact boundary condition only at the end of the complete RK cycle, (2) impose consistent intermediate boundary conditions derived from the physical boundary condition and its derivatives. The first method, while retaining the RK accuracy in all cases, results in a scheme with much reduced CFL condition, rendering the RK scheme less attractive. The second method retains the same allowable time step as the periodic problem. However it is a general remedy only for the linear case. For non-linear hyperbolic equations the second method is effective only for for RK schemes of third order accuracy or less. Numerical studies are presented to verify the efficacy of each approach.

The role of global economic policy uncertainty in long-run volatilities and correlations of U.S. industry-level stock returns and crude oil.

PubMed

Yu, Honghai; Fang, Libing; Sun, Boyang

2018-01-01

We investigate how Global Economic Policy Uncertainty (GEPU) drives the long-run components of volatilities and correlations in crude oil and U.S. industry-level stock markets. Using the modified generalized autoregressive conditional heteroskedasticity mixed data sampling (GARCH-MIDAS) and dynamic conditional correlation mixed data sampling (DCC-MIDAS) specifications, we find that GEPU is positively related to the long-run volatility of Financials and Consumer Discretionary industries; however, it is negatively related to Information Technology, Materials, Telecommunication Services and Energy. Unlike the mixed role of GEPU in the long-run volatilities, the long-run correlations are all positively related to GEPU across the industries. Additionally, the rankings of the correlations of Energy and Materials are time-invariant and classified as high, with the little exception of the latter. The Consumer Staples industry is time-invariant in the low-ranking group. Our results are helpful to policy makers and investors with long-term concerns.

The role of global economic policy uncertainty in long-run volatilities and correlations of U.S. industry-level stock returns and crude oil

PubMed Central

Yu, Honghai; Sun, Boyang

2018-01-01

We investigate how Global Economic Policy Uncertainty (GEPU) drives the long-run components of volatilities and correlations in crude oil and U.S. industry-level stock markets. Using the modified generalized autoregressive conditional heteroskedasticity mixed data sampling (GARCH-MIDAS) and dynamic conditional correlation mixed data sampling (DCC-MIDAS) specifications, we find that GEPU is positively related to the long-run volatility of Financials and Consumer Discretionary industries; however, it is negatively related to Information Technology, Materials, Telecommunication Services and Energy. Unlike the mixed role of GEPU in the long-run volatilities, the long-run correlations are all positively related to GEPU across the industries. Additionally, the rankings of the correlations of Energy and Materials are time-invariant and classified as high, with the little exception of the latter. The Consumer Staples industry is time-invariant in the low-ranking group. Our results are helpful to policy makers and investors with long-term concerns. PMID:29420645

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

PubMed

Acuña, Gonzalo; Ramirez, Cristian; Curilem, Millaray

2014-01-01

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

Relations Among Intimate Partner Violence, Maternal Depressive Symptoms, and Maternal Parenting Behaviors

PubMed Central

Gustafsson, Hanna C.; Cox, Martha J.

2013-01-01

The authors examined the relations among intimate partner violence (IPV), maternal depressive symptoms, and maternal harsh intrusive parenting. Using a cross-lagged, autoregressive path model, they sought to clarify the directionality of the relations among these 3 variables over the first 2 years of the child’s life. The results indicated that, in this diverse sample of families living in predominantly low-income rural communities (N = 705), higher levels of early IPV were associated with increases in maternal depressive symptoms, which in turn were associated with increases in maternal harsh intrusive parenting behaviors. These findings suggest that interventions aimed at improving the parenting of women exposed to domestic violence may want to simultaneously target IPV and depressive symptomatology. PMID:23869110

Teacher performance goal practices and elementary students’ behavioral engagement: A developmental perspective

PubMed Central

Hughes, Jan N.; Wu, Wei; West, Stephen G.

2010-01-01

We investigated growth trajectories for classroom performance goal practices and for student behavioral engagement across grades 2 to 5 for 497 academically at-risk elementary students. This study is the first longitudinal investigation of performance goal practices in the early elementary years. On average, teacher use of performance goal practices increased and students’ behavioral engagement declined across the four years. Using autoregressive latent trajectory (ALT) models, we examined the synchronous relations between teacher-reported performance goal practices and teacher-reported student behavioral engagement. As expected, as students move into classrooms with a new teacher with less emphasis on performance goal practices, they become more behaviorally engaged in school. Gender did not moderate these results. Implications for teacher professional development are discussed. PMID:21215834

Domain Decomposition Algorithms for First-Order System Least Squares Methods

NASA Technical Reports Server (NTRS)

Pavarino, Luca F.

1996-01-01

Least squares methods based on first-order systems have been recently proposed and analyzed for second-order elliptic equations and systems. They produce symmetric and positive definite discrete systems by using standard finite element spaces, which are not required to satisfy the inf-sup condition. In this paper, several domain decomposition algorithms for these first-order least squares methods are studied. Some representative overlapping and substructuring algorithms are considered in their additive and multiplicative variants. The theoretical and numerical results obtained show that the classical convergence bounds (on the iteration operator) for standard Galerkin discretizations are also valid for least squares methods.

International Trade, Pollution Accumulation and Sustainable Growth: A VAR Estimation from the Pearl River Delta Region

NASA Astrophysics Data System (ADS)

Zuo, Hui; Tian, Lu

2018-03-01

In order to investigate international trade influence in the regional environment. This paper constructs a vector auto-regression (VAR) model and estimates the equations with the environment and trade data of the Pearl River Delta Region. The major mechanisms to the lag are discussed and the fit simulation of the environmental change by the international impulse is given. The result shows that impulse of pollution-intensive export deteriorates the environment continuously and impulse of such import improves it. These effects on the environment are insignificantly correlated with contemporary regional income but significantly correlative to early-stage trade feature. To a typical trade-dependent economy, both export and import have hysteresis influence in the regional environment. The lagged impulse will change environmental development in the turning point, maximal pollution level and convergence.

Application of tripolar concentric electrodes and prefeature selection algorithm for brain-computer interface.

PubMed

Besio, Walter G; Cao, Hongbao; Zhou, Peng

2008-04-01

For persons with severe disabilities, a brain-computer interface (BCI) may be a viable means of communication. Lapalacian electroencephalogram (EEG) has been shown to improve classification in EEG recognition. In this work, the effectiveness of signals from tripolar concentric electrodes and disc electrodes were compared for use as a BCI. Two sets of left/right hand motor imagery EEG signals were acquired. An autoregressive (AR) model was developed for feature extraction with a Mahalanobis distance based linear classifier for classification. An exhaust selection algorithm was employed to analyze three factors before feature extraction. The factors analyzed were 1) length of data in each trial to be used, 2) start position of data, and 3) the order of the AR model. The results showed that tripolar concentric electrodes generated significantly higher classification accuracy than disc electrodes.

Thermal signature identification system (TheSIS): a spread spectrum temperature cycling method

NASA Astrophysics Data System (ADS)

Merritt, Scott

2015-03-01

NASA GSFC's Thermal Signature Identification System (TheSIS) 1) measures the high order dynamic responses of optoelectronic components to direct sequence spread-spectrum temperature cycling, 2) estimates the parameters of multiple autoregressive moving average (ARMA) or other models the of the responses, 3) and selects the most appropriate model using the Akaike Information Criterion (AIC). Using the AIC-tested model and parameter vectors from TheSIS, one can 1) select high-performing components on a multivariate basis, i.e., with multivariate Figures of Merit (FOMs), 2) detect subtle reversible shifts in performance, and 3) investigate irreversible changes in component or subsystem performance, e.g. aging. We show examples of the TheSIS methodology for passive and active components and systems, e.g. fiber Bragg gratings (FBGs) and DFB lasers with coupled temperature control loops, respectively.

Alteration of Box-Jenkins methodology by implementing genetic algorithm method

NASA Astrophysics Data System (ADS)

Ismail, Zuhaimy; Maarof, Mohd Zulariffin Md; Fadzli, Mohammad

2015-02-01

A time series is a set of values sequentially observed through time. The Box-Jenkins methodology is a systematic method of identifying, fitting, checking and using integrated autoregressive moving average time series model for forecasting. Box-Jenkins method is an appropriate for a medium to a long length (at least 50) time series data observation. When modeling a medium to a long length (at least 50), the difficulty arose in choosing the accurate order of model identification level and to discover the right parameter estimation. This presents the development of Genetic Algorithm heuristic method in solving the identification and estimation models problems in Box-Jenkins. Data on International Tourist arrivals to Malaysia were used to illustrate the effectiveness of this proposed method. The forecast results that generated from this proposed model outperformed single traditional Box-Jenkins model.

A New First Break Picking for Three-Component VSP Data Using Gesture Sensor and Polarization Analysis

PubMed Central

Li, Huailiang; Tuo, Xianguo; Shen, Tong; Wang, Ruili; Courtois, Jérémie; Yan, Minhao

2017-01-01

A new first break picking for three-component (3C) vertical seismic profiling (VSP) data is proposed to improve the estimation accuracy of first arrivals, which adopts gesture detection calibration and polarization analysis based on the eigenvalue of the covariance matrix. This study aims at addressing the problem that calibration is required for VSP data using the azimuth and dip angle of geophones, due to the direction of geophones being random when applied in a borehole, which will further lead to the first break picking possibly being unreliable. Initially, a gesture-measuring module is integrated in the seismometer to rapidly obtain high-precision gesture data (including azimuth and dip angle information). Using re-rotating and re-projecting using earlier gesture data, the seismic dataset of each component will be calibrated to the direction that is consistent with the vibrator shot orientation. It will promote the reliability of the original data when making each component waveform calibrated to the same virtual reference component, and the corresponding first break will also be properly adjusted. After achieving 3C data calibration, an automatic first break picking algorithm based on the autoregressive-Akaike information criterion (AR-AIC) is adopted to evaluate the first break. Furthermore, in order to enhance the accuracy of the first break picking, the polarization attributes of 3C VSP recordings is applied to constrain the scanning segment of AR-AIC picker, which uses the maximum eigenvalue calculation of the covariance matrix. The contrast results between pre-calibration and post-calibration using field data show that it can further improve the quality of the 3C VSP waveform, which is favorable to subsequent picking. Compared to the obtained short-term average to long-term average (STA/LTA) and the AR-AIC algorithm, the proposed method, combined with polarization analysis, can significantly reduce the picking error. Applications of actual field experiments have also confirmed that the proposed method may be more suitable for the first break picking of 3C VSP. Test using synthesized 3C seismic data with low SNR indicates that the first break is picked with an error between 0.75 ms and 1.5 ms. Accordingly, the proposed method can reduce the picking error for 3C VSP data. PMID:28925981

On the effects of grid ill-conditioning in three dimensional finite element vector potential magnetostatic field computations

NASA Technical Reports Server (NTRS)

Wang, R.; Demerdash, N. A.

1990-01-01

The effects of finite element grid geometries and associated ill-conditioning were studied in single medium and multi-media (air-iron) three dimensional magnetostatic field computation problems. The sensitivities of these 3D field computations to finite element grid geometries were investigated. It was found that in single medium applications the unconstrained magnetic vector potential curl-curl formulation in conjunction with first order finite elements produce global results which are almost totally insensitive to grid geometries. However, it was found that in multi-media (air-iron) applications first order finite element results are sensitive to grid geometries and consequent elemental shape ill-conditioning. These sensitivities were almost totally eliminated by means of the use of second order finite elements in the field computation algorithms. Practical examples are given in this paper to demonstrate these aspects mentioned above.

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

NASA Astrophysics Data System (ADS)

Ferdous, Nazneen; Bhat, Chandra R.

2013-01-01

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

Spectrum Modal Analysis for the Detection of Low-Altitude Windshear with Airborne Doppler Radar

NASA Technical Reports Server (NTRS)

Kunkel, Matthew W.

1992-01-01

A major obstacle in the estimation of windspeed patterns associated with low-altitude windshear with an airborne pulsed Doppler radar system is the presence of strong levels of ground clutter which can strongly bias a windspeed estimate. Typical solutions attempt to remove the clutter energy from the return through clutter rejection filtering. Proposed is a method whereby both the weather and clutter modes present in a return spectrum can be identified to yield an unbiased estimate of the weather mode without the need for clutter rejection filtering. An attempt will be made to show that modeling through a second order extended Prony approach is sufficient for the identification of the weather mode. A pattern recognition approach to windspeed estimation from the identified modes is derived and applied to both simulated and actual flight data. Comparisons between windspeed estimates derived from modal analysis and the pulse-pair estimator are included as well as associated hazard factors. Also included is a computationally attractive method for estimating windspeeds directly from the coefficients of a second-order autoregressive model. Extensions and recommendations for further study are included.

Identification and modeling of the electrohydraulic systems of the main gun of a main battle tank

NASA Astrophysics Data System (ADS)

Campos, Luiz C. A.; Menegaldo, Luciano L.

2012-11-01

The black-box mathematical models of the electrohydraulic systems responsible for driving the two degrees of freedom (elevation and azimuth) of the main gun of a main battle tank (MBT) were identified. Such systems respond to gunner's inputs while acquiring and tracking targets. Identification experiments were designed to collect simultaneous data from two inertial measurement units (IMU) installed at the gunner's handle (input) and at the center of rotation of the turret (output), for the identification of the azimuth system. For the elevation system, IMUs were installed at the gunner's handle (input) and at the breech of the gun (output). Linear accelerations and angular rates were collected for both input and output. Several black-box model architectures were investigated. As a result, nonlinear autoregressive with exogenous variables (NARX) second order model and nonlinear finite impulse response (NFIR) fourth order model, demonstrate to best fit the experimental data, with low computational costs. The derived models are being employed in a broader research, aiming to reproduce such systems in a laboratory virtual main gun simulator.

Improved first-order uncertainty method for water-quality modeling

USGS Publications Warehouse

Melching, C.S.; Anmangandla, S.

1992-01-01

Uncertainties are unavoidable in water-quality modeling and subsequent management decisions. Monte Carlo simulation and first-order uncertainty analysis (involving linearization at central values of the uncertain variables) have been frequently used to estimate probability distributions for water-quality model output due to their simplicity. Each method has its drawbacks: Monte Carlo simulation's is mainly computational time; and first-order analysis are mainly questions of accuracy and representativeness, especially for nonlinear systems and extreme conditions. An improved (advanced) first-order method is presented, where the linearization point varies to match the output level whose exceedance probability is sought. The advanced first-order method is tested on the Streeter-Phelps equation to estimate the probability distribution of critical dissolved-oxygen deficit and critical dissolved oxygen using two hypothetical examples from the literature. The advanced first-order method provides a close approximation of the exceedance probability for the Streeter-Phelps model output estimated by Monte Carlo simulation using less computer time - by two orders of magnitude - regardless of the probability distributions assumed for the uncertain model parameters.

Time-stable boundary conditions for finite-difference schemes solving hyperbolic systems: Methodology and application to high-order compact schemes

NASA Technical Reports Server (NTRS)

Carpenter, Mark H.; Gottlieb, David; Abarbanel, Saul

1993-01-01

We present a systematic method for constructing boundary conditions (numerical and physical) of the required accuracy, for compact (Pade-like) high-order finite-difference schemes for hyperbolic systems. First, a roper summation-by-parts formula is found for the approximate derivative. A 'simultaneous approximation term' (SAT) is then introduced to treat the boundary conditions. This procedure leads to time-stable schemes even in the system case. An explicit construction of the fourth-order compact case is given. Numerical studies are presented to verify the efficacy of the approach.

Sea-ice induced growth decline in Arctic shrubs.

PubMed

Forchhammer, Mads

2017-08-01

Measures of increased tundra plant productivity have been associated with the accelerating retreat of the Arctic sea-ice. Emerging studies document opposite effects, advocating for a more complex relationship between the shrinking sea-ice and terrestrial plant productivity. I introduce an autoregressive plant growth model integrating effects of biological and climatic conditions for analysing individual ring-width growth time series. Using 128 specimens of Salix arctica , S. glauca and Betula nana sampled across Greenland to Svalbard, an overall negative effect of the retreating June sea-ice extent was found on the annual growth. The negative effect of the retreating June sea-ice was observed for younger individuals with large annual growth allocations and with little or no trade-off between previous and current year's growth. © 2017 The Author(s).

Stability analysis of fractional-order Hopfield neural networks with time delays.

PubMed

Wang, Hu; Yu, Yongguang; Wen, Guoguang

2014-07-01

This paper investigates the stability for fractional-order Hopfield neural networks with time delays. Firstly, the fractional-order Hopfield neural networks with hub structure and time delays are studied. Some sufficient conditions for stability of the systems are obtained. Next, two fractional-order Hopfield neural networks with different ring structures and time delays are developed. By studying the developed neural networks, the corresponding sufficient conditions for stability of the systems are also derived. It is shown that the stability conditions are independent of time delays. Finally, numerical simulations are given to illustrate the effectiveness of the theoretical results obtained in this paper. Copyright © 2014 Elsevier Ltd. All rights reserved.

Protein synthesis in the basolateral amygdala complex is required for consolidation of a first-order fear memory, but not for consolidation of a higher-order fear memory.

PubMed

Leidl, Dana M; Lay, Belinda P P; Chakouch, Cassandra; Westbrook, R Frederick; Holmes, Nathan M

2018-04-12

The present series of experiments pursued our recent findings that consolidation of a second-order fear memory requires neuronal activity, but not de novo protein synthesis, in the basolateral amygdala complex (BLA). It used a modified second-order conditioning protocol in which rats were exposed to S1-shock pairings in stage 1 and pairings of the serial S2-S1 compound and shock in stage 2. Experiment 1 showed that responding (freezing) to S2 in this protocol is conditional on its compounding with S1 in stage 2 (Experiment 1), and therefore, the result of associative formation. The remaining experiments then showed that the protein synthesis requirement for consolidation of new learning about S2 varied with the training afforded S1. When S1 was trained in stage 1 and present in stage 2, consolidation of the new S2 fear memory was unaffected by pre- or post-stage 2 infusions of the protein synthesis inhibitor, cycloheximide, into the BLA (Experiments 2 and 5). This result was observed independently of the number of S1-shock pairings in stage 1 (even a single pairing produced the result), and alongside demonstrations that cycloheximide infusions disrupt consolidation of a first-order fear memory (Experiments 2 and 5). However, when S1 was not conditioned in stage 1 (Experiment 3) or was omitted from conditioning in stage 2 (Experiment 4), consolidation of the new S2 fear memory was disrupted by post-stage 2 cycloheximide infusions into the BLA. These results were taken to imply that the consolidation of a higher-order fear memory exploits molecular events associated with consolidation of a reactivated first-order fear memory; hence it occurs independently of de novo protein synthesis in the BLA. Alternatively, the nature of the association formed in higher-order conditioning may be such as to not require de novo protein synthesis for its consolidation. Copyright © 2018 Elsevier Inc. All rights reserved.