PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Sequential Input Selection Algorithm for Long-term prediction of Time Series
Jarkko Tikka and Jaakko Hollmen
Neurocomputing Volume 71, Number 13-15, pp. 2604-2615, 2008. ISSN 0925-2312

Abstract

In time series prediction, making accurate predictions is often the primary goal. At the same time, interpretability of the models would be desirable. For the latter goal, we have devised a sequential input selection algorithm (SISAL) to choose a parsimonious, or sparse, set of input variables. Our proposed algorithm is a sequential backward selection type algorithm based on a cross-validation resampling procedure. Our strategy is to use a filter approach in the prediction: first we select a sparse set of inputs using linear models and then the selected inputs are used in the nonlinear prediction conducted with multilayer-perceptron networks. Furthermore, we perform a sensitivity analysis by quantifying the importance of the individual input variables in the nonlinear models using a method based on partial derivatives. Experiments are done with the Santa Fe laser data set that exhibits very nonlinear behavior and a data set in a problem of electricity load prediction. The results in the prediction problems of varying difficulty highlight the range of applicability of our proposed algorithm. In summary, our SISAL yields accurate and parsimonious prediction models giving insight to the original problem.

PDF - PASCAL Members only - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
ID Code:4201
Deposited By:Jaakko Hollmen
Deposited On:21 November 2008