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| HAL: hal-00115624, version 1 |
| DOI: 10.1016/j.NEUNET.2004.08.008 |
| Detailed view |  Export this paper |
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| Neural Networks 17 (2004) 1169-1181 |
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| Double quantization of the regressor space for long-term time series prediction: Method and proof of stability |
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| Geoffroy Simon 1Amaury Lendasse 1 |
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| (2004) |
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| 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. |
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| 1: | Machine Learning Group (DICE-MLG) |
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Université Catholique de Louvain |
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| 2: | Statistique Appliquée et MOdélisation Stochastique (SAMOS) |
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Université Paris I - Panthéon Sorbonne |
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| 3: | Modélisation Appliquée, Trajectoires Institutionnelles et Stratégies Socio-Économiques (MATISSE) |
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CNRS : UMR8595 – Université Paris I - Panthéon Sorbonne |
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| SAMOS-MATISSE http://samos.univ-paris1.fr |
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| Subject | : | Computer Science/Learning Mathematics/Statistics |
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| Time series – Kohonen Maps |
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| hal-00115624, version 1 | |
| http://hal.archives-ouvertes.fr/hal-00115624 | |
| oai:hal.archives-ouvertes.fr:hal-00115624 | |
| From: Michel Verleysen | |
| Submitted on: Thursday, 23 November 2006 23:10:16 | |
| Updated on: Thursday, 4 January 2007 15:58:34 | |
