Artificial Neural Network-Based Algorithm for ARMA Model Order Estimation
This paper presents a new algorithm for the determination of the Autoregressive Moving Average (ARMA) model order based on Artificial Neural Network (ANN). The basic idea is to apply ANN to a special matrix constructed from the Minimum Eginevalue (MEV) criterion. The MEV criterion is based on a covariance matrix derived from the observed output data only. The input signal is unobservable. The proposed algorithm is based on training the MEV covariance matrix dataset using the back-propagation technique. Our goal is to develop a system based on ANN; hence, the model order can be selected automatically without the need of prior knowledge about the model or any human intervention. Examples are given to illustrate the significant improvement results.
KeywordsArtificial Neural Networks ANN ARMA Back-Propagation Simulation Eginevalue System Identification Signal Processing Time Series
Unable to display preview. Download preview PDF.
- 1.Chen, Y.W., Chou, C.-C.: Correlation based Traffic Modeling of Sub-networks. Journal of Internet Technology, 277–283 (October 2003)Google Scholar
- 3.Akaike, H.: Statistical Predictor Identification. Ann. Inst. Statist. Math., 203–217Google Scholar
- 4.Akaike, H.: A new look at statistical model identification. IEEE Trans. Automat. Contr. AC-19, 716–723 (1974/1970)Google Scholar
- 10.Demuth, H., Beale, M.: Neural Network Toolbox For Use with MATLAB, Version 5. The Math. Works, Inc. (2007)Google Scholar
- 12.Groupe, D.: Principles of Artificial Neural Networks, 2nd edn. World Scientific Publishing, New Jersey (2007)Google Scholar
- 13.Hannan, E.J.: The estimation of the order of an ARMA process. Ann. Stat. 8 (1980)Google Scholar