LS-SVM functional network for time series prediction
Tuomas Kärnä, Fabrice Rossi and Amaury Lendasse
In: ESANN 2006, European Symposium on Artificial Neural Networks, 26-28 April 2006, Bruges (Belgium).
Usually time series prediction is done with regularly sampled
data. In practice, however, the data available may be irregularly sampled. In this case the conventional prediction methods cannot be used. One solution is to use Functional Data Analysis (FDA). In FDA an interpolating function is fitted to the data and the fitting coefficients are being analyzed instead of the original data points. In this paper, we propose a
functional approach to time series prediction. Radial Basis Function Network (RBFN) is used for the interpolation. The interpolation parameters are optimized with a k-Nearest Neighbors (k-NN) model. Least Squares Support Vector Machine (LS-SVM) is used for the prediction.