Bayesian learning using Gaussian process for time series prediction

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.