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 e ectiveness 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.

• Type: Journal
• Year: 2004
• Where: CSDA
• Authors: Sofiane Brahim-Belhouari, Amine Bermak
• Keywords: CSDA 2004, Gaussian Process Models, Nonstationary Covariance Function, Time Series Prediction