In many practical situations, an experimenter is interested in the behavior of a process at different time scales T. In this paper it is shown that autoregressive (AR) parameter estimation and order selection can be reformulated to find the best model for the signal properties at interval T. A comparison between AR modeling at interval T and standard AR modeling shows that modeling at interval ...

Linear State Space Modeling determines the hidden autoregressive (AR) process in a noisy time series; for an AR process the time series’ current value is the sum of current stochastic “noise” and a linear combination of previous values. We present preliminary results from modeling a sample of 4 channel BATSE LAD lightcurves. We determine the order of the AR process necessary to model the bursts...

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...

Time series analysis has been applied to structural monitoring signals for system damage identification in a number of research literatures. Among various time series analysis tools, univariate autoregressive modeling (AR) is one of the most commonly used methods because of its innate computational efficiency. In this paper, three autoregressive damage features extracted directly from the ambie...

Autoregressive (AR) modeling has played an important role in many signal processing applications. This paper is concerned with identification of AR model parameters using observations corrupted with colored noise. A novel formulation of an auxiliary least-squares estimator is introduced so that the autocovariance functions of the colored observation noise can be estimated in a straightforward m...

ARfit is a collection of Matlab routines for modeling multivariate time series by autoregressive (AR) models. It provides tools for all stages of the model identiication process: statistics that aid in the selection of the model order, fast algorithms for least squares estimation of parameters, modules that produce approximate conndence regions for the estimated parameters, and routines for dia...

BACKGROUND Computer-assisted arrhythmia recognition is critical for the management of cardiac disorders. Various techniques have been utilized to classify arrhythmias. Generally, these techniques classify two or three arrhythmias or have significantly large processing times. A simpler autoregressive modeling (AR) technique is proposed to classify normal sinus rhythm (NSR) and various cardiac ar...

This paper proposes a robust algorithm for adaptive modelling of EEG signal classification using a modified Extended Kalman Filter (EKF). This modified EKF combines Radial Basis functions (RBF) and Autoregressive (AR) modeling and obtains better classification performance by truncating the filtering distribution when new observations are very informative.