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

fYear

2008

fDate

18-21 Nov. 2008

Firstpage

130

Lastpage

135

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/ICYCS.2008.393

Filename

4708961