A joint optimization algorithm and its application in chaotic time series prediction

To improve the prediction precision of the chaotic time series,a joint optimization algorithm for chaotic time series parameters was proposed based on the genetic algorithm,using the relationship between the phase space reconstruction and prediction model parameters.Firstly,the phase space reconstruction and prediction model parameters were taken as genetic algorithm individuals while the prediction accuracy of the chaotic time series was taken as the evaluation function of genetic algorithm.Secondly,the optimization parameters were obtained by selection,crossover and mutation of genetic algorithm.Finally,the joint optimization algorithm was tested by chaotic time series.The results show that the joint optimization algorithm improves the prediction precision compared with the traditional parameters optimization algorithm,and provides a new way for the chaotic time series prediction.