Gaussian process for nonstationary time series prediction

Sofiane Brahim-Belhouari; Amine Bermak

doi:10.1016/j.csda.2004.02.006

Gaussian process for nonstationary time series prediction

Sofiane Brahim-Belhouari^*, Amine Bermak

^*Corresponding author for this work

Hong Kong University of Science and Technology

Research output: Contribution to journal › Article › peer-review

239 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 language	English
Pages (from-to)	705-712
Number of pages	8
Journal	Computational Statistics and Data Analysis
Volume	47
Issue number	4
DOIs	https://doi.org/10.1016/j.csda.2004.02.006
Publication status	Published - 1 Nov 2004
Externally published	Yes

Keywords

Bayesian learning
Gaussian processes
Prediction theory
Time series

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10.1016/j.csda.2004.02.006

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title = "Gaussian process for nonstationary time series prediction",

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.",

keywords = "Bayesian learning, Gaussian processes, Prediction theory, Time series",

author = "Sofiane Brahim-Belhouari and Amine Bermak",

year = "2004",

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AU - Bermak, Amine

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AB - 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.

KW - Bayesian learning

KW - Gaussian processes

KW - Prediction theory

KW - Time series

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M3 - Article

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SP - 705

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JO - Computational Statistics and Data Analysis

JF - Computational Statistics and Data Analysis

IS - 4

ER -

Gaussian process for nonstationary time series prediction

Abstract

Keywords

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