Prediction of the chaotic time series based on simulated annealing algorithm and support vector machine

The regression accuracy and generalization performance of the support vector regression (SVR) model depended on a proper parameter set. The optimal parameter selection method of SVR was put forward based on simulated annealing algorithm ( SAA) . The key parameters C and ε of SVM and the radial basis kernel parameter g were optimized within the global scope. The support vector regression model was established for chaotic time series prediction by using the optimum parameters. The time series of Lorenz system was used to validate the effectiveness of the model, and the RMSE of prediction reached 5.424 × 10^-6. The results show that the optimal parameter selection method based on SAA is available and the SAA-SVR model can predict the chaotic time series accurately.