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.