A heuristic method for parameter selection in LS-SVM: Application to time series prediction
Least Squares Support Vector Machines (LS-SVM) are the state of the art in kernel methods for regression. These models have been successfully applied for time series modelling and prediction. A critical issue for the performance of these models is the choice of the kernel parameters and the hyperparameters which define the function to be minimized. In this paper a heuristic method for setting both the σ parameter of the Gaussian kernel and the regularization hyperparameter based on information extracted from the time series to be modelled is presented and evaluated.
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Volume (Year): 27 (2011)
Issue (Month): 3 ()
Pages: 725-739
| Handle: | RePEc:eee:intfor:v:27:y:2011:i:3:p:725-739 |
| DOI: | 10.1016/j.ijforecast.2010.02.007 |
| Contact details of provider: | Web page: http://www.elsevier.com/locate/ijforecast
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