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

Authors:
Geoffroy Simon;Amaury Lendasse;Marie Cottrell;Jean-Claude Fort;Michel Verleysen
Affiliations:
Machine Learning Group (DICE), Université Catholique de Louvain, Place du Levant 3, B-1348 Louvain-la-Neuve, Belgium;Neural Networks Research Centre, Laboratory of Computer and Information Science, Helsinki University of Technology, P.O. Box 5400, FIN-02015 HUT, Finland;Université Paris I - Panthéon Sorbonne, SAMOS-MATISSE, UMR CNRS 8595, Rue de Tolbiac 90, F-75634 Paris Cedex 13, France;Université Paris I - Panthéon Sorbonne, SAMOS-MATISSE, UMR CNRS 8595, F-75634 Paris Cedex 13, France and Laboratoire de Statistiques et Probabilités, Université Paul Sabatler T ...;Machine Learning Group (DICE), Université Catholique de Louvain, B-1348 Louvain-la-Neuve, Belgium and Université Paris I - Panthéon Sorbonne, SAMOS-MATISSE, UMR CNRS 8595, F-75634 P ...
Venue:
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Year:
2004

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Abstract

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.