Adaptive Filtering: An Empirical Evaluation

Abstract

This paper compares the Makridakis and Wheelwright adaptive filtering forecast technique with the recursive least squares procedure, which assumes constant coefficients. A simulation study is performed to examine its relative forecast accuracy under several models of time-varying coefficients. It is shown that the choice of the learning constant in adaptive filtering is quite critical, and that only in cases with substantial coefficient variability will adaptive filtering lead to forecast improvements.