Time series prediction with linear and nonlinear adaptive networks

Abstract

Backpropagation networks with a single hidden layer were trained to perform one-step prediction on a variety of scalar time series. The performance of such nets typically equals or exceeds that of the linear adaptive predictor of the same order. Comparisons of the linear and nonlinear predictors were made with periodic, chaotic, and random time series, including broadband ocean acoustic ambient noise.<>