Singular Value Decomposition-Based ARMA Model Parameter Estimation of Non-Gaussian Processes

 Adnan M. Al-Smadi


Autoregressive moving average (ARMA) modeling has been used in many fields. This paper presents an approach to time series analysis of a general ARMA model parameters estimation. The proposed
technique is based on the singular value decomposition (SVD) of a covariance matrix of a third order cumulants from only the output sequence. The observed data sequence is corrupted by additive Gaussian noise. The system is driven by a zero-mean independent and identically distributed (i.i.d.) non-Gaussian sequence. Simulations
verify the performance of the proposed method.


Time series forecasting, singular value decomposition, ARMA model, non-Gaussian process, parameters estimation

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