Parameters Joint Optimization of Chaotic Time Series Prediction Model

To improve the prediction accuracy of the chaotic time series prediction model,a joint optimization method is proposed for phase space reconstruction and least square support vector machine(LSSVM) parameters.The main idea of the joint optimization method is that phase space reconstruction and LSSVM parameters are jointly designed using uniform design firstly,then the parameters are jointly optimized by a self-calling LSSVM,and the joint optimization method is tested by chaotic time series lastly.The experiment results show that the proposed method obtains better prediction accuracy and higher optimization speed than other prediction methods.