论文信息 - Gaussian process for nonstationary time series prediction

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 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.

Amine Bermak | Sofiane Brahim-Belhouari | A. Bermak | S. Brahim-Belhouari

[1] David Mackay,et al. Gaussian Processes - A Replacement for Supervised Neural Networks? , 1997 .

[2] Heekuck Oh,et al. Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[3] Radford M. Neal. Monte Carlo Implementation of Gaussian Process Models for Bayesian Regression and Classification , 1997, physics/9701026.

[4] Geoffrey E. Hinton,et al. Bayesian Learning for Neural Networks , 1995 .

[5] J.M. Vesin,et al. Bayesian learning using Gaussian process for time series prediction , 2001, Proceedings of the 11th IEEE Signal Processing Workshop on Statistical Signal Processing (Cat. No.01TH8563).