Actas de congresos
Long-term Time Series Prediction Using Wrappers For Variable Selection And Clustering For Data Partition
Registro en:
142441380X; 9781424413805
Ieee International Conference On Neural Networks - Conference Proceedings. , v. , n. , p. 3068 - 3073, 2007.
10987576
10.1109/IJCNN.2007.4371450
2-s2.0-51749092208
Autor
Puma-Villanueva W.J.
Dos Santos E.P.
Von Zuben F.J.
Institución
Resumen
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. ©2007 IEEE.
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