Decomposition of the multi-dimensional time series identification problem

Authors:
V. V. Klimchenko
Affiliations:
Institute of Automation and Control Processes, Far-Eastern Branch, Russian Academy of Sciences, Vladivostok, Russia
Venue:
Automation and Remote Control
Year:
2008

Citing 5
Cited 0

Estimation of parameters and eigenmodes of multivariate autoregressive models

ACM Transactions on Mathematical Software (TOMS)
Algorithm 808: ARfit—a matlab package for the estimation of parameters and eigenmodes of multivariate autoregressive models

ACM Transactions on Mathematical Software (TOMS)
Time series: data analysis and theory

Time series: data analysis and theory
Time Series Analysis: Forecasting and Control

Time Series Analysis: Forecasting and Control
Accuracy and Stability of Numerical Algorithms

Accuracy and Stability of Numerical Algorithms

Quantified Score

Hi-index	0.00

Visualization

Abstract

The mathematical model of shaping filter is constructed for a stationary multidimensional time series. The identification procedure is simplified by dividing into several steps: each step results in a multidimensional filter such that the autocovariance generating function of the transformed time series has nonzero entries in only one row, one column and in the main diagonal. This decomposition allows to construct adequat models containing relatively small number of estimated parameters. The reasonable parametrizaion, in turn, contributes to the better quality of the model due to the better statistical accuracy of the parameter estimations.