A temperature prediction method of Insulated Gate Bipolar Transistor (IGBT) module based on autoregressive moving average model is proposed. Historical and current temperature datum of IGBT module is indispensable to the ARMA method, temperature time series is obtained by uniform sampling, and autoregressive (AR) model is constructed. Temperature time series prediction of IGBT module is realize...

A time-varying autoregressive model with time-varying coefficients is introduced in this paper for parameter extraction from non-stationary vibration signals. With this model, the relationship between linear time-varying modal parameters, i.e., instantaneous frequencies and damping factors, and time-varying autoregressive model coefficients is established. The time-varying autoregressive modeli...

Abstract. This paper deals with the problem of testing a change in variance of the p-th order autoregressive process, AR(p), at an unknown change point τ . We propose a test based on maximum likelihood principle for detecting such type of change, find asymptotic distribution of the test statistic and compare it with the tests for detecting changes in both variance and autoregressive parameters ...

In this paper we present a Feed-Foward Neural Networks Autoregressive (FFNN-AR) model with genetic algorithms training optimization in order to predict the gross domestic product growth of six countries. Specifically we propose a kind of weighted regression, which can be used for econometric purposes, where the initial inputs are multiplied by the neural networks final optimum weights from inpu...

We model the time series of the S&P500 index by a combined process, the AR+GARCH process, where AR denotes the autoregressive process which we use to account for the short-range correlations in the index changes and GARCH denotes the generalized autoregressive conditional heteroskedastic process which takes into account the long-range correlations in the variance. We study the AR+GARCH process ...

We propose the multivariate autoregressive model for content based music auto-tagging. At the song level our approach leverages the multivariate autoregressive mixture (ARM) model, a generative time-series model for audio, which assumes each feature vector in an audio fragment is a linear function of previous feature vectors. To tackle tagmodel estimation, we propose an efficient hierarchical E...

In this article, we consider the problem of adaptive detection for a multichannel signal in the presence of spatially and temporally colored compound-Gaussian disturbance. By modeling the disturbance as a multichannel autoregressive (AR) process, we first derive a parametric generalized likelihood ratio test against compoundGaussian disturbance (CG-PGLRT) assuming that the true multichannel AR ...

Univariate autoregressive (AR) models can be extended to the multivariate case to study dynamic interrelationships among several variables, all viewed as endogenous. The resulting vector autoregression (V AR) models describe the evolution over time of a vector of n variables yt = (y1t y2t...ynt)0 as a function of its past realizations yt−1,yt−2, ... and a vector of stochastic terms ut = (u1t u2...

Recently, there has been a renewed interest in modeling economic time series by vector autoregressive moving-average models. However, this class of models has been unpopular in practice because of estimation problems and the complexity of the identification stage. These disadvantages could have led to the dominant use of vector autoregressive models in macroeconomic research. In this paper, sev...