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ESANN
2006

LS-SVM functional network for time series prediction

14 years 25 days ago
LS-SVM functional network for time series prediction
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.
Tuomas Kärnä, Fabrice Rossi, Amaury Lend
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2006
Where ESANN
Authors Tuomas Kärnä, Fabrice Rossi, Amaury Lendasse
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