A new local linear prediction model for chaotic time series

A new local linear prediction model is proposed to predict chaotic time series in this Letter. We propose that the parameters—the embedding dimension and the time delay of the local linear prediction model—can take values which are different to those of the state space reconstruction in the procedure of finding the nearest neighbor points. We propose a criterion based on prediction power to determine the optimal parameters of the new local linear prediction model. Simulation results show that the new local linear prediction model can effectively predict chaotic time series and the prediction performance of the new local linear prediction model is superior to that of the local linear prediction.