Methodology for long-term prediction of time series

Authors:
Antti Sorjamaa;Jin Hao;Nima Reyhani;Yongnan Ji;Amaury Lendasse
Affiliations:
Helsinki University of Technology, Adaptive Informatics Research Centre, P.O. Box 5400, 02015 Espoo, Finland;Helsinki University of Technology, Adaptive Informatics Research Centre, P.O. Box 5400, 02015 Espoo, Finland;Helsinki University of Technology, Adaptive Informatics Research Centre, P.O. Box 5400, 02015 Espoo, Finland;Helsinki University of Technology, Adaptive Informatics Research Centre, P.O. Box 5400, 02015 Espoo, Finland;Helsinki University of Technology, Adaptive Informatics Research Centre, P.O. Box 5400, 02015 Espoo, Finland
Venue:
Neurocomputing
Year:
2007

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Abstract

In this paper, a global methodology for the long-term prediction of time series is proposed. This methodology combines direct prediction strategy and sophisticated input selection criteria: k-nearest neighbors approximation method (k-NN), mutual information (MI) and nonparametric noise estimation (NNE). A global input selection strategy that combines forward selection, backward elimination (or pruning) and forward-backward selection is introduced. This methodology is used to optimize the three input selection criteria (k-NN, MI and NNE). The methodology is successfully applied to a real life benchmark: the Poland Electricity Load dataset.