Chaotic Time Series Prediction by Fusing Local Methods

Yong Wang, Shiqiang Hu* School of Aeronautics and Astronautics Shanghai Jiao Tong University, Shanghai [email protected], [email protected] Abstract—In this paper, a novel prediction algorithm is proposed to predict chaotic time series. The chaotic time series can be embedded into state space by Takens embedding theorem. The one dimensional data is mapped to a higher dimensional space that provides precise information about the chaotic time series. The upsampling algorithm is used to find more precise nearest neighboring points. Two prediction algorithms which provide accurate prediction results without the knowledge of the underlying dynamics and fuzzy fusion algorithm are employed for one-step and multi-steps ahead forecasting. Simulation results from three typical chaotic time series demonstrate that our method is effective for chaotic time series prediction.