Local prediction of chaotic time series based on Gaussian processes

Prediction of chaotic time series has received much attention during the last decade. This paper reviews local linear model and support vector regression based predictors and proposes a method to improve phase space prediction of chaotic time series by applying the Gaussian processes locally. The proposed algorithm is applied to noisy Henon time series and a laser experiment respectively. It provides a relatively better prediction performance in comparison with the conventional method.