Nonlinear prediction of chaotic time series using support vector machines

A novel method for regression has been recently proposed by Vapnik et al. (1995, 1996). The technique, called support vector machine (SVM), is very well founded from the mathematical point of view and seems to provide a new insight in function approximation. We implemented the SVM and tested it on a database of chaotic time series previously used to compare the performances of different approximation techniques, including polynomial and rational approximation, local polynomial techniques, radial basis functions, and neural networks. The SVM performs better than the other approaches. We also study, for a particular time series, the variability in performance with respect to the few free parameters of SVM.