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

Geoffroy Simon (1) , Amaury Lendasse (1) , Marie Cottrell (2, 3) , Jean-Claude Fort (2, 3) , Michel Verleysen (1, 2, 3)

1 DICE-MLG - Machine Learning Group
2 SAMOS - Statistique Appliquée et MOdélisation Stochastique
3 MATISSE - UMR 8595 - Modélisation Appliquée, Trajectoires Institutionnelles et Stratégies Socio-Économiques

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.

Mots clés: Time series; Kohonen Maps

Domaines: Apprentissage [cs.LG]; Statistiques [math.ST]

Geoffroy Simon, Amaury Lendasse, Marie Cottrell, Jean-Claude Fort, Michel Verleysen. Double quantization of the regressor space for long-term time series prediction: Method and proof of stability. Neural Networks, 2004, 17, pp.1169-1181. ⟨10.1016/j.NEUNET.2004.08.008⟩. ⟨hal-00115624⟩