Singular value decomposition-based ARMA model parameter estimation ofNon-gaussian processes

Abstract EN

Autoregressivemoving 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.