Direct and Recursive Prediction of Time Series Using Mutual Information Selection | Zendy

Yongnan Ji | Zendy; Hao Jin | Zendy; Nima Reyhani | Zendy; Amaury Lendasse | Zendy

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Direct and Recursive Prediction of Time Series Using Mutual Information Selection

Author(s) -

Yongnan Ji,

Hao Jin,

Nima Reyhani,

Amaury Lendasse

Publication year - 2005

Publication title -

lecture notes in computer science

Language(s) - English

Resource type - Book series

SCImago Journal Rank - 0.249

H-Index - 400

eISSN - 1611-3349

pISSN - 0302-9743

ISBN - 3-540-26208-3

DOI - 10.1007/11494669_124

Subject(s) - mutual information , computer science , maxima and minima , benchmark (surveying) , selection (genetic algorithm) , series (stratigraphy) , artificial intelligence , data mining , time series , recursion (computer science) , algorithm , mathematical optimization , machine learning , mathematics , mathematical analysis , paleontology , geodesy , biology , geography

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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