Prediction of chaotic time series based on the relevance vector machine

Abstract

The prediction of chaotic time series is performed by relevance vector machine (RVM), which is an inherent online machine learning technique utilizing a flexible and sparse function without additional regularization parameters. The main objective of this approach is to increase the accuracy of the chaotic time series prediction. The method is applied to Mackey-Glass and Lorenz equations, Henon mapping which produce the chaotic time series to evaluate the validity of the proposed technique. Numerical experimental results confirm that the proposed method can predict the chaotic time series more effectively and accurately when compared with the existing prediction methods.