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

作者：Rubio Gines^*; Pomares Hector; Rojas Ignacio; Javier Herrera Luis

来源：International Journal of Forecasting, 2011, 27(3): 725-739.

DOI：10.1016/j.ijforecast.2010.02.007

• Least squares support vector machines
• Gaussian kernel parameters
• Hyperparameters optimization
• 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 a parameter of the Gaussian kernel and the regularization hyperparameter based on information extracted from the time series to be modelled is presented and evaluated.

• 出版日期2011-9