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10

CSDA
2004

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Gaussian process for nonstationary time series prediction

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Gaussian process for nonstationary time series prediction

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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.

Sofiane Brahim-Belhouari, Amine Bermak

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CSDA 2004 | Gaussian Process Models | Nonstationary Covariance Function | Time Series Prediction |

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Added	17 Dec 2010
Updated	17 Dec 2010
Type	Journal
Year	2004
Where	CSDA
Authors	Sofiane Brahim-Belhouari, Amine Bermak

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