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.