Abstract
This paper presents a comparison between direct and recursive prediction strategies. In order to perform the input selection, an approach based on mutual information is used. The mutual information is computed between all the possible input sets and the outputs. Least Squares Support Vector Machines are used as non-linear models to avoid local minima problems. Results are illustrated on the Poland electricity load benchmark and they show the superiority of the direct prediction strategy.
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Ji, Y., Hao, J., Reyhani, N., Lendasse, A. (2005). Direct and Recursive Prediction of Time Series Using Mutual Information Selection. In: Cabestany, J., Prieto, A., Sandoval, F. (eds) Computational Intelligence and Bioinspired Systems. IWANN 2005. Lecture Notes in Computer Science, vol 3512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494669_124
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DOI: https://doi.org/10.1007/11494669_124
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26208-4
Online ISBN: 978-3-540-32106-4
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