Double quantization of the regressor space for long-term time series prediction: Method and proof of stability - Archive ouverte HAL Access content directly
Journal Articles Neural Networks Year : 2004

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

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.
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Dates and versions

hal-00115624 , version 1 (23-11-2006)

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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⟩
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