Predicting chaotic time series using relevance vector machine regression

A chaotic time series prediction method based on relevance vector machine regression (RVMR) is proposed in this paper. The RVMR model has a simpler model structure, a fewer number of control parameters, and a faster prediction speed with comparable approximate prediction accuracy in comparison with support vector machine regression (SVMR). In addition, the kernel function must not necessarily fulfill Mercer´s conditions in RVMR model. Two typical chaotic time series, namely, Logistic and Hénon map are used to evaluate the RVMR´s performance. The results show that the proposed method is effective in chaotic time series prediction.