Chaotic time series prediction using knowledge based Green’s Kernel and least-squares support vector machines

This paper proposes a novel prior knowledge based Green´s kernel for long term chaotic time series prediction. A mathematical framework is presented to obtain the domain knowledge about the magnitude of the Fourier transform of the function to be predicted and design a prior knowledge based Green´s kernel that exhibits optimal regularization properties by using the concept of matched filters. The matched filter behavior of the proposed kernel function provides the optimal regularization. Simulation results on a chaotic benchmark time series indicate that the knowledge based Green´s kernel shows good prediction performance compared to the other existing support vector kernels for the time series prediction task considered in this paper.