PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Input selection for long-term prediction of time series
Jarkko Jarkko, Jaakko Hollmen and Amaury Lendasse
In: Computational Intelligence and Bioinspired Systems: 8th International Workshop on Artificial Neural Networks, IWANN 2005, Vilanova i la Geltrú, Barcelona, Spain, June 8-10, 2005. Proceedings Lecture Notes in Computer Science , 3512 (XXV). (2005) Springer-Verlag GmbH , Germany , pp. 1002-1009. ISBN 3-540-26208-3

Abstract

Prediction of time series is an important problem in many areas of science and engineering. Extending the horizon of predictions further to the future is the challenging and difficult task of long-term prediction. In this paper, we investigate the problem of selecting non-contiguous input variables for an autoregressive prediction model in order to improve the prediction ability. We present an algorithm in the spirit of backward selection which removes variables sequentially from the prediction models based on the significance of the individual regressors. We successfully test the algorithm with a non-linear system by selecting inputs with a linear model and finally train a non-linear predictor with the selected variables on Santa Fe laser data set.

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EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:1680
Deposited By:Amaury Lendasse
Deposited On:28 November 2005