Title :
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
fDate :
6/23/1905 12:00:00 AM
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;
Conference_Titel :
Statistical Signal Processing, 2001. Proceedings of the 11th IEEE Signal Processing Workshop on
Print_ISBN :
0-7803-7011-2
DOI :
10.1109/SSP.2001.955315
