Chaos Optimization Method of SVM Parameters Selection for Chaotic Time Series Forecasting

For support vector regression (SVR), the setting of key parameters is very important, which determines the regression accuracy and generalization performance of SVR model. In this paper, an optimal selection approach for SVR parameters was put forward based on mutative scale optimization algorithm(MSCOA), the key parameters C and ɛ of SVM and the radial basis kernel parameter g were optimized within the global scopes. 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 reachedRMSE = 3.0335 × 10−3. Simulation results show that the optimal selection approach based on MSCOA is an effective approach and the MSCOA-SVR model has a good performance for chaotic time series forecasting.