Direct and recursive prediction of time series using mutual information selection
Y Ji, J Hao, N Reyhani, A Lendasse - … , IWANN 2005, Vilanova i la Geltrú …, 2005 - Springer
Y Ji, J Hao, N Reyhani, A Lendasse
Computational Intelligence and Bioinspired Systems: 8th International Work …, 2005•SpringerThis 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.
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.
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.
Springer
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