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A heuristic method for parameter selection in LS-SVM: Application to time series prediction

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  • Rubio, Ginés
  • Pomares, Héctor
  • Rojas, Ignacio
  • Herrera, Luis Javier

Abstract

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 [sigma] 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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  • Rubio, Ginés & Pomares, Héctor & Rojas, Ignacio & Herrera, Luis Javier, 2011. "A heuristic method for parameter selection in LS-SVM: Application to time series prediction," International Journal of Forecasting, Elsevier, vol. 27(3), pages 725-739, July.
    • Handle: RePEc:eee:intfor:v:27:y::i:3:p:725-739
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