Four different error-forecast updating models are investigated in terms of their capability of providing real-time river flow forecast accuracy superior to that of rainfall-runoff models applied in the simulation (nonupdating) mode. The first and most widely used is the single autoregressive (AR) model, the second being an elaboration of that model, namely the autoregressive-threshold (AR-TS) u...

Order selection is a general problem in autoregressive (AR) power spectrum estimation of sleep EEG. In this paper we tried to determine a common AR model order for sleep EEG. Order selection criterias are applied and the most fitting orders are selected. The effect of sampling frequency on AR modeling is investigated with experiments on EEG and sinusoidals.

Autoregressive (AR) extrapolation of travel-time data is tested using several synthetic tomography examples using a cross-borehole geometry. Earlier studies have shown crossborehole tomography using travel-times can have reduced resolution because of the limitations on ray coverage. We apply AR extrapolation to partial travel-time data and then compare the tomographic inversions using the full ...

We expand Nakamura’s (2005) neural network based inflation forecasting experiment to an alternative non-linear model; a Markov switching autoregressive (MS-AR) model. The two non-linear models perform approximately on par and outperform the linear autoregressive model on short forecast horizons of one and two quarters. Furthermore, the MS-AR model is the best performer on longer horizons of thr...

It is proposed to jointly estimate the parameters of nonGaussian autoregressive (AR) processes in a Bayesian context using the Gibbs sampler. Using the Markov chains produced by the sampler an approximation to the vector MAP estimator is implemented. The results reported here used AR(4) models driven by noise sequences where each sample is iid as a two component Gaussian sum mixture. The result...

This paper describes a signal processing algorithm that utilizes wavelet transform and autoregressive modeling to identify heart sounds and to generate clinical features that characterize systolic and diastolic heart murmurs. A wavelet transform (WT) based on the Daubechies wavelet was adopted to facilitate the identification of the first and second heart sounds (S1 and S2) and to isolate systo...

This article explores the use of scalar and multivariate autoregressive (AR) models to extract features from the human electroencephalogram (EEG) with which mental tasks can be discriminated. This is part of a larger project to investigate the feasibility of using EEG to allow paralyzed persons to control a device like a wheelchair. EEG signals from four subjects were recorded while they perfor...