Modeling general distributed nonstationary process and identifying time-varying autoregressive system by wavelets: theory and application

摘要

In this paper, some new techniques for time-varying parametric autoregressive (AR) system identification by wavelets are presented. Firstly, we derive a new multiresolution least squares (MLS) algorithm for Gaussian time-varying AR model identification employing wavelet operator matrix representation. This method can optimally balance between the over-fitted solution and the poorly represented indentification.