Modeling vector nonlinear time series using POLYMARS

Abstract

A modified multivariate adaptive regression splines method for modeling vector nonlinear time series is investigated. The method results in models that can capture certain types of vector self-exciting threshold autoregressive behavior, as well as provide good predictions for more general vector nonlinear time series. The effect of different model selection criteria on fitted models and predictions is evaluated through simulation. The method is illustrated for a real data example, to model a series of intra-day electricity loads in two neighboring Australian states. © 2002 Elsevier Science B.V. All rights reserved.