Chaotic Time Series Prediction Based On Binary Particle Swarm Optimization

Abstract

Prediction of chaotic time series based on the phase space reconstruction theory has been applied in many research fields. Local linear model is widely used in chaos prediction due to its versatility and small computation amount. The embedding dimension and time delay parameters of the local linear prediction model can take different values with those of the phase space reconstruction. The Binary Particle Swarm Optimization (BPSO) is applied to choose the optimal parameters of the new local linear prediction model for its strong search ability. The main objective of this approach is to increase the predictive accuracy of the local linear model. In this paper the local linear one-step and multi-step predictive model predicts the chaotic time series respectively. Simulation results show the feasibility and effectiveness of the proposed method.