Methodology for long-term prediction of time series

被引：281

作者：

Sorjamaa, Antti ^{[1
]}

Hao, Jin ^{[1
]}

Reyhani, Nima ^{[1
]}

Ji, Yongnan ^{[1
]}

Lendasse, Amaury ^{[1
]}

机构：

[1] Aalto Univ, Adapt Informat Res Ctr, FIN-02015 Espoo, Finland

来源：

NEUROCOMPUTING | 2007年 / 70卷 / 16-18期

关键词：

time series prediction; input selection; k-Nearest neighbors; mutual information; nonparametric noise estimation; recursive prediction; direct prediction; least squares support vector machines;

D O I：

10.1016/j.neucom.2006.06.015

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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. (c) 2007 Elsevier B.V. All rights reserved.

引用

页码：2861 / 2869

页数：9

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