Double quantization of the regressor space for long-term time series prediction: Method and proof of stability
Simon, Geoffroy ; Lendasse, Amaury ; Cottrell, Marie ; Fort, Jean-Claude ; Verleysen, Michel
HAL, hal-00115624 / Harvested from HAL
Text on HAL
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.
Publié le : 2004-07-05
Classification:  Time series,  Kohonen Maps,  [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG],  [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST] 
@article{hal-00115624,
     author = {Simon, Geoffroy and Lendasse, Amaury and Cottrell, Marie and Fort, Jean-Claude and Verleysen, Michel},
     title = {Double quantization of the regressor space for long-term time series prediction: Method and proof of stability},
     journal = {HAL},
     volume = {2004},
     number = {0},
     year = {2004},
     language = {en},
     url = {http://dml.mathdoc.fr/item/hal-00115624}
}

Simon, Geoffroy; Lendasse, Amaury; Cottrell, Marie; Fort, Jean-Claude; Verleysen, Michel. Double quantization of the regressor space for long-term time series prediction: Method and proof of stability. HAL, Tome 2004 (2004) no. 0, . http://gdmltest.u-ga.fr/item/hal-00115624/