Double quantization of the regressor space for long-term time series prediction: method and proof of stability

被引：11

作者：

Simon, G

Lendasse, A

Cottrell, M

Fort, JC

Verleysen, M

机构：

[1] Catholic Univ Louvain, Machine Learning Grp DICE, B-1348 Louvain, Belgium

[2] Aalto Univ, Neural Networks Res Ctr, Lab Comp & Informat Sci, FIN-02015 Helsinki, Finland

[3] Univ Paris 01, SAMOS MATISSE, CNRS, UMR 8595, F-75634 Paris 13, France

[4] Univ Toulouse 3, Lab Stat & Probabilites, CNRS C55830, F-31062 Toulouse, France

来源：

NEURAL NETWORKS | 2004年 / 17卷 / 8-9期

关键词：

time series; long term forecasting; trend prediction; SOM; method stability proof;

D O I：

10.1016/j.neunet.2004.08.008

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The Kohonen self-organization map is Usually considered as a classification or Clustering tool, with only a few applications in time series prediction. In this paper. a particular time series forecasting method based on Kohonen maps is described. This method has been specifically designed for the prediction of long-term trends. The proof of the stability of the method for long-term forecasting is given, as well as illustrations of the utilization of the method both in the scalar and vectorial cases. (C) 2004 Elsevier Ltd. All rights reserved.

引用

页码：1169 / 1181

页数：13

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