A Circular Lattice-Smoothness Priors Multichannel Time Varying Autoregressive Model

Abstract

Abstract A method for the multichannel autoregressive modeling of nonstationary covariance time series data is shown. The multichannel modeling is achieved doing things one channel at-a-time using only scalar computations. Our method exploits the smoothness priors modeling (Gersch and Kitagawa 1988 ) of partial correlation coefficients in a time-varying linear regression model and the lattice-form structure (Sakai 1982) for multichannel stationary time series modeling. The circular lattice structure permits the multichannel AR model to be realized one-channel at-a- time. Smoothness priors is a Bayesian stochastic linear regression approach that permits the multichannel time varying AR model, a model with more parameters than data, to be fitted. Examples are shown.