Phase Space Reconstruction of Nonlinear Time Series Based on Kernel Method

A phase space reconstruction method KPCA-CA was proposed based on Kernel Principal Component Analysis (KPCA) and Correlation Analysis (CA) for nonlinear time series. On the basis of KPCA, the correlation was analyzed between every kernel principal component and output variable, and some kernel principal components were discontinuously chosen according to their correlation degree to form the phase space of nonlinear time series. The method was compared with other methods of phase space reconstruction. The experimental results show that modeling accuracy for nonlinear time series is highest based on the phase space reconstruction method proposed by the paper, proving the efficiency of the method.