Title :
A Parameter Choosing Method of SVR for Time Series Prediction
Author :
Lin, Shukuan ; Zhang, Shaomin ; Qiao, Jianzhong ; Liu, Hualei ; Yu, Ge
Author_Institution :
Coll. of Inf. Sci. & Eng., Northeastern Univ., Shenyang
Abstract :
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.
Keywords :
prediction theory; regression analysis; support vector machines; time series; cross-validation method; data features; epsilon-weighed SVR; parameter choosing method; support vector regression; time series prediction; Educational institutions; Information science; Learning systems; Neural networks; Optimization methods; Predictive models; Risk management; Support vector machine classification; Support vector machines; Testing; Parameter choosing; SVR; epsilon-weighed; improved Cross-Validation; time series prediction;
Conference_Titel :
Young Computer Scientists, 2008. ICYCS 2008. The 9th International Conference for
Conference_Location :
Hunan
Print_ISBN :
978-0-7695-3398-8
Electronic_ISBN :
978-0-7695-3398-8
DOI :
10.1109/ICYCS.2008.393
