This paper considers an exploratory modeling strategy applied to a large scale reallife problem of power load forecasting. Different model structures are considered, including Autoregressive models with eXogenous inputs (ARX), Nonlinear Autoregressive models with eXogenous inputs (NARX), both of which are also extended to incorporate residuals that follow an Autoregressive (AR) process (AR-(N)A...

In general, high-speed network traffic is a complex, nonlinear, nonstationary process and is significantly affected by immeasurable parameters and variables. Thus, a precise model of this process becomes increasingly difficult as the complexity of the process increases. Recently, fuzzy modeling has been found to be a powerful method to effectively describe a real, complex, and unknown process w...

Speaker recognition in noisy environments is challenging when there is a mis-match in the data used for enrollment and verification. In this paper, we propose a robust feature extraction scheme based on spectro-temporal modulation filtering using two-dimensional (2-D) autoregressive (AR) models. The first step is the AR modeling of the sub-band temporal envelopes by the application of the linea...

This study presents alpha-stable autoregressive (AR) modeling of the dynamics Chua's circuit in presence heavy-tailed noise. The parameters AR time series are estimated using covariation-based Yule-Walker method, and distributed residuals calculated regression type method. Visual depictions model distributions presented. medians presented for noise with various stability index parameters. Thus,...

In this paper a noise robust feature extraction algorithm using joint wavelet packet decomposition (WPD) and an autoregressive (AR) modeling of the speech signal is presented. In opposition to the short time Fourier transform (STFT) based time-frequency signal representation, a computationally efficient WPD can lead to better representation of non-stationary parts of the speech signal (consonan...

Obstructive Sleep Apnea is a frequent disorder with detrimental health, performance and safety effects. The diagnosis of the disorder is cumbersome and expensive. New methods for screening and diagnosis are needed. The method we describe in this work is based on detection of four main features of respiratory signal. The automatic signal classification starts by extracting signal features from a...

The purpose of this paper is to use Bahadur’s asymptotic relative efficiency measure to compare the performance of various tests of autoregressive (AR) versus moving average (MA) error processes in regression models. Tests to be examined include non-nested procedures of the models against each other, and classical procedures based upon testing both the AR and MA error processes against the more...

In psychology, the use of intensive longitudinal data has steeply increased during the past decade. As a result, studying temporal dependencies in such data with autoregressive modeling is becoming common practice. However, standard autoregressive models are often suboptimal as they assume that parameters are time-invariant. This is problematic if changing dynamics (e.g., changes in the tempora...

The purpose of the work described in this paper is to investigate the use of autoregressive (AR) model by using maximum likelihood estimation (MLE) also interpretation and performance of this method to extract classifiable features from human electroencephalogram (EEG) by using Artificial Neural Networks (ANNs). ANNs are evaluated for accuracy, specificity, and sensitivity on classification of ...

When modeling time series data using autoregressive-moving average processes, it is a common practice to presume that the residuals are normally distributed. However, sometimes we encounter non-normal residuals and asymmetry of data marginal distribution. Despite widespread use of pure autoregressive processes for modeling non-normal time series, the autoregressive-moving average models have le...