DocumentCode
423609
Title
Fast bootstrap applied to LS-SVM for long term prediction of time series
Author
Lendasse, Amaury ; Wertz, Vincent ; Simon, Geoffroy ; Verleysen, Michel
Author_Institution
CIS, HUT, Finland
Volume
1
fYear
2004
fDate
25-29 July 2004
Lastpage
710
Abstract
Time series forecasting is usually limited to one-step ahead prediction. This goal is extended here to longer-term prediction, obtained using the least-square support vector machines model. The influence of the model parameters is observed when the time horizon of the prediction is increased and for various prediction methods. The model selection to optimize the design parameters is performed using the fast bootstrap methodology introduced in previous works.
Keywords
least squares approximations; support vector machines; time series; LS-SVM; fast bootstrap; least-square support vector machines model; time series forecasting; time series prediction; Computational Intelligence Society; Design optimization; Finance; Floods; Lagrangian functions; Load forecasting; Prediction methods; Predictive models; Rivers; Support vector machines;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on
ISSN
1098-7576
Print_ISBN
0-7803-8359-1
Type
conf
DOI
10.1109/IJCNN.2004.1380003
Filename
1380003
Link To Document