Bayesian learning using Gaussian process for time series prediction

Author

Brahim-Belhouari, Sofiane ; Vesin, Jean-Marc

Author_Institution

Signal Process. Lab., Swiss Fed. Inst. of Technol., Lausanne, Switzerland

fYear

2001

fDate

6/23/1905 12:00:00 AM

Firstpage

433

Lastpage

436

Abstract

In this paper, the problem of time series prediction is studied. A Bayesian procedure based on Gaussian process models is proposed and compared to the radial basis function networks. In our experiments, Gaussian process models show excellent prediction. The conceptual simplicity, and good performance of Gaussian process models should make them very attractive for a wide range of problems

Keywords

Bayes methods; Gaussian processes; learning (artificial intelligence); prediction theory; time series; Bayesian learning; Gaussian Process; prediction; time series; Bayesian methods; Computer networks; Gaussian noise; Gaussian processes; Laboratories; Neural networks; Predictive models; Radial basis function networks; Signal processing; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Statistical Signal Processing, 2001. Proceedings of the 11th IEEE Signal Processing Workshop on

Print_ISBN

0-7803-7011-2

Type

conf

DOI

10.1109/SSP.2001.955315

Filename

955315

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1609229