Support vector machine with composite kernels for time series prediction

Abstract

In Support Vector Machine (SVM), Kernels are employed to map the nonlinear model into a higher dimensional feature space where the linear learning is adopted. The characteristics of kernels have great impacts on learning and predictive results of SVM. Considering the characteristics for fitting and generalization of two kinds of typical kernels-global kernel (polynomial kernel) and local kernel (RBF kernel), a new kind of SVM modeling method based on composite kernels is proposed. In order to evaluate the reasonable fitness of kernel functions, the particle swarm optimization (PSO) algorithm is used to adaptively evolve SVM to obtain the best prediction performance, in which each particle represented as a real vector corresponds to a set of the candidate parameters of SVM. Experiments in time series prediction demonstrate that the SVM with composite kernels has the better performance than with a single kernel. © Springer-Verlag Berlin Heidelberg 2007.