Title: Application of the multi-model partitioning theory for simultaneous order and parameter estimation of multivariate ARMA models

Authors: Stylianos Sp. Pappas, Vassilios C. Moussas, Sokratis K. Katsikas

Addresses: Department of Information and Communication Systems Engineering, University of the Aegean, 83200, Karlovassi, Samos, Greece. ' School of Technological Applications (STEF), Technological Educational Institution (TEI) of Athens, Egaleo, Greece. ' Dept. of Technology Education & Digital Systems, University of Piraeus, 150 Androutsou St., Piraeus GR-18532, Greece

Abstract: In this paper, a study on how to perform simultaneous order and parameter estimation of multivariate (MV) ARMA (autoregressive moving average) models under the presence of noise is addressed. The proposed method, which is computationally efficient, is an extension of a previously presented method for MV AR models and is based on the well established and widely applied multi-model partitioning theory. A series of computer simulations indicate that the method is infallible in selecting the correct model order in very few steps. The simultaneous estimation of the multivariate ARMA parameters is also another benefit of the proposed method. The results are compared with two other established order selection criteria namely Akaike|s Information Criterion (AIC) and Schwarz|s Bayesian Information Criterion (BIC). Finally, it is shown that the method is also successful in tracking model order changes, in real time.

Keywords: order determination; parameter estimation; multivariate ARMA models; autoregressive moving average; multi-model partitioning; Kalman filter.

DOI: 10.1504/IJMIC.2008.021161

International Journal of Modelling, Identification and Control, 2008 Vol.4 No.3, pp.242 - 249

Published online: 07 Nov 2008 *

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