Methodology for Long-term Prediction of Time Series
Antti Sorjamaa, Jin Hao, Nima Reyhani, Yongnan Ji and Amaury Lendasse
Neurocomputing 2005.

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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