Notes on Digital Signal Processing: Practical Recipes for Design, Analysis and Implementation

Note 69. Fitting AR Models to Stochastic Signals: The Yule-Walker Method

This note describes the Yule-Walker method for finding the parameters needed to fit an autoregressive model to a finite sequence of samples obtained from a stochastic signal. This method is conceptually straightforward; it is based on the simple idea of substituting an estimated autocorrelation sequence (ACS) for the true ACS in the Yule-Walker equations that are presented in Note 68. Other methods for fitting an AR model to a finite sequence of signal samples are presented in Notes 72 and 73.

The Yule-Walker method is a technique for fitting an autoregressive model to a stochastic signal that is assumed to be auto regressive, but where knowledge about the signal is limited ...