We propose the use of multivariate autoregressive (MAR) models of functional magnetic resonance imaging time series to make inferences about functional integration within the human brain. The method is demonstrated with synthetic and real data showing how such models are able to characterize interregional dependence. We extend linear MAR models to accommodate nonlinear interactions to model top...

A robust prediction model invoking the Takens embedding theorem, whose working hypothesis is obtained via an inference procedure based on the minimum Fisher information principle, is presented. The coefficients of the ansatz, central to the working hypothesis satisfy a time independent Schrödinger-like equation in a vector setting. The inference of i) the probability density function of the coe...

Logistic regression models-or "sightability models"-fit to detection/non-detection data from marked individuals are often used to adjust for visibility bias in later detection-only surveys, with population abundance estimated using a modified Horvitz-Thompson (mHT) estimator. More recently, a model-based alternative for analyzing combined detection/non-detection and detection-only data was deve...

one of the water resources modeling requirements is sufficient knowledge of long-term series of meteorological and hydrological parameters. in this study the nearest neighbor resampling method presented by lall and sharma was developed. in the developed model, the knn regression was used for time series forecasting instead of local polynomial used in the developed algorithm by prairie. in this ...

improving time series forecastingaccuracy is an important yet often difficult task.both theoretical and empirical findings haveindicated that integration of several models is an effectiveway to improve predictive performance, especiallywhen the models in combination are quite different. in this paper,a model of the hybrid artificial neural networks andfuzzy model is proposed for time series for...

Dynamics of complex systems is studied by first considering a chaotic time series generated by Lorenz equations and adding noise to it. The trend (smooth behavior) is separated from fluctuations at different scales using wavelet analysis and a prediction method proposed by Lorenz is applied to make out of sample predictions at different regions of the time series. The prediction capability of t...

This is an early version of a set of notes for the course on Time Series Analysis offered at Lund University. Any and all comments and suggestions are most welcome and appreciated. Glossary and Abbreviations 1-D, 2-D, etc. One-dimensional, two-dimensional, etc. ACF Auto-covariance function AR Autoregressive ARIMA Integrated ARMA ARMA Autoregressive moving average ARMAX ARMA with exogenous input...