Analysis of Fast Input Selection: Application in Time Series Prediction

In time series prediction, accuracy of predictions is often the primary goal. At the same time, however, it would be very desirable if we could give interpretation to the system under study. For this goal, we have devised a fast input selection algorithm to choose a parsimonious, or sparse set of input variables. The method is an algorithm in the spirit of backward selection used in conjunction with the resampling procedure. In this paper, our strategy is to select a sparse set of inputs using linear models and after that the selected inputs are also used in the nonlinear prediction based on multi-layer perceptron networks. We compare the prediction accuracy of our parsimonious non-linear models with the linear models and the regularized non-linear perceptron networks. Furthermore, we quantify the importance of the individual input variables in the non-linear models using the partial derivatives. The experiments in a problem of electricity load prediction demonstrate that the fast input selection method yields accurate and parsimonious prediction models giving insight to the original problem.