Gaussian process for nonstationary time series prediction

In this paper, the problem of time series prediction is studied. A Bayesian procedure based on Gaussian process models using a nonstationary covariance function is proposed. Experiments proved the approach e4ectiveness with an excellent prediction and a good tracking. The conceptual simplicity, and good performance of Gaussian process models should make them very attractive for a wide range of problems. c © 2004 Elsevier B.V. All rights reserved.