A Parameter Choosing Method of SVR for Time Series Prediction
Shukuan Lin, Shaomin Zhang, Jianzhong Qiao, Hualei Liu, Ge Yu · 2008
It is important to choose good parameters in support vector regression (SVR) modeling. Choosing different parameters will influence the accuracy of SVR models. This paper proposes a parameter choosing method of SVR models for time series prediction. In the light of data features of time series, the paper improves the traditional cross-validation method, and combines the improved cross-validation with epsilon-weighed SVR in order to get good parameters of models. The experiments show that the method is effective for time series prediction.