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Long-term time series prediction using wrappers for variable selection and clustering for data partition

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3 Author(s)

In an attempt to implement long-term time series prediction based on the recursive application of a one-step-ahead multilayer neural network predictor, we have considered the eleven short time series provided by the organizers of the Special Session NN3 Neural Network Forecasting Competition, and have proposed a joint application of a variable selection technique and a clustering procedure. The purpose was to define unbiased partition subsets and predictors with high generalization capability, based on a wrapper methodology. The proposed approach overcomes the performance of the predictor that considers all the lags in the regression vector. After obtaining the eleven long-term predictors, we conclude the paper presenting the eighteen multi-step predictions for each time series, as requested in the competition.

Published in:

Neural Networks, 2007. IJCNN 2007. International Joint Conference on

Date of Conference:

12-17 Aug. 2007

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