Gaussian process for nonstationary time series prediction

Sofiane Brahim-Belhouari, Amine Bermak

Research output: Contribution to journalArticle

145 Citations (Scopus)

Abstract

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 effectiveness 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.

Original languageEnglish
Pages (from-to)705-712
Number of pages8
JournalComputational Statistics and Data Analysis
Volume47
Issue number4
DOIs
Publication statusPublished - 1 Nov 2004

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Keywords

  • Bayesian learning
  • Gaussian processes
  • Prediction theory
  • Time series

ASJC Scopus subject areas

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics

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