Prediction of the Chaotic Time Series Based on Chaotic Simulated Annealing and Support Vector Machine

The regression accuracy and generalization performance of the support vector regression (SVR) model depend on a proper setting of its parameters. An optimal selection approach of SVR parameters was put forward based on chaotic simulated annealing algorithm (CSAA), the key parameters C and ɛ 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 testify the effectiveness of the model. The root mean square error of prediction reached8.756 × 10-4. Simulation results show that the optimal selection approach based on CSAA is available and the CSAA-SVR model can predict the chaotic time series accurately.