Nonlinear Prediction of Chaotic Time Series UsingSupport Vector

A novel method for regression has been recently proposed by V. Vapnik et al. 8, 9]. 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 the same data base of chaotic time series that was used in 1] to compare the performances of diierent approximation techniques, including polynomial and rational approximation, local polynomial techniques, Radial Basis Functions, and Neu-ral Networks. The SVM performs better than the approaches presented in 1]. We also study, for a particular time series, the variability in performance with respect to the few free parameters of SVM.