An efficient and versatile algorithm for computing the covariancefunction of an ARMA process

Authors:
T. Soderstrom;J. Jezek;V. Kucera
Affiliations:
Dept. of Technol., Uppsala Univ.;-;-
Venue:
IEEE Transactions on Signal Processing
Year:
1998

Citing 0
Cited 4

Exact maximum likelihood estimation of structured or unit root multivariate time series models

Computational Statistics & Data Analysis
Novel fast computation algorithm of the second-order statistics for autoregressive moving-average processes

IEEE Transactions on Signal Processing
A simple set of linear equations for computing autocorrelations of ARMA(p,q) signals with given model parameters

ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
Symmetric Matrix Polynomial Equation: Interpolation Results

Automatica (Journal of IFAC)

Quantified Score

Hi-index	35.69

Visualization

Abstract

Computation of an ARMA covariance function is a common ingredient in analysis and synthesis of various problems in stochastic control, estimation, and signal processing. Several approaches can be used for this purpose. In this paper, we present an algorithm based on simple polynomial calculations. Compared with alternative strategies, it has small computational load, shows good numerical robustness, and can be extended to handle multivariable ARMA processes, even with complex-valued coefficients