PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Methodology for Long-term Prediction of Time Series
Antti Sorjamaa, Jin Hao, Nima Reyhani, Yongnan Ji and Amaury Lendasse
Neurocomputing Volume 70, Number 16-18, pp. 2861-2869, 2007. ISSN 0925-2312

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.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:3722
Deposited By:Amaury Lendasse
Deposited On:15 February 2008