Double quantization of the regressor space for long-term time series prediction: method and proof of stability
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 pr...
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| Veröffentlicht in: | Neural networks 2004-10, Vol.17 (8), p.1169-1181 |
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| container_title | Neural networks |
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| creator | Simon, Geoffroy Lendasse, Amaury Cottrell, Marie Fort, Jean-Claude Verleysen, Michel |
| description | 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. |
| doi_str_mv | 10.1016/j.neunet.2004.08.008 |
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| ispartof | Neural networks, 2004-10, Vol.17 (8), p.1169-1181 |
| issn | 0893-6080 1879-2782 |
| language | eng |
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| source | MEDLINE; Elsevier ScienceDirect Journals |
| subjects | Computer Science Forecasting Long term forecasting Machine Learning Mathematics Method stability proof Monte Carlo Method Neural Networks (Computer) Poland Power Plants SOM Statistics Time series Trend prediction |
| title | Double quantization of the regressor space for long-term time series prediction: method and proof of stability |
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