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
收藏
页码：1169 / 1181
页数：13
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