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Forecasting the CATS benchmark with the Double Vector Quantization method
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DOI：10.1016/j.neucom.2005.12.137.png)
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摘要
En 中文
The Double Vector Quantization (DVQ) method, a long-term forecasting method based on the self-organizing maps algorithm, has been used to predict the 100 missing values of the CATS competition data set. An analysis of the proposed time series is provided to estimate the dimension of the auto-regressive part of this nonlinear auto-regressive forecasting method. Based on this analysis experimental results using the DVQ method are presented and discussed. As one of the features of the DVQ method is its ability to predict scalars as well as vectors of values, the number of iterative predictions needed to reach the prediction horizon is further observed. The method stability for the long term allows obtaining reliable values for a rather long-term forecasting horizon. (C) 2007 Elsevier B.V. All rights reserved.
Keyword:
time series prediction
CATS competition
Double Vector Quantization method
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6.5
论文数:
2.5W
被引数:
6.5W
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分享收藏Double quantization of the regressor space for long-term time series prediction: method and proof of stability.png)
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