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

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Author Info

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

    Article provided by Elsevier in its journal International Journal of Forecasting.

    Volume (Year): 27 (2011)
    Issue (Month): 3 ()
    Pages: 725-739

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    Handle: RePEc:eee:intfor:v:27:y:2011:i:3:p:725-739

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    Web page: http://www.elsevier.com/locate/ijforecast

    Related research

    Keywords: Least squares support vector machines; Gaussian kernel parameters; Hyperparameters optimization; Time series prediction;

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    Cited by:
    1. Plakandaras, Vasilios & Gupta, Rangan & Papadimitriou, Theophilos & Gogas, Periklis, 2014. "Forecasting the U.S. Real House Price Index," DUTH Research Papers in Economics 10-2014, Democritus University of Thrace, Department of Economics.

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