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

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 ha...

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Bibliographic Details
Main Authors: Puma-Villanueva, W.J., dos Santos, E.P., Von Zuben, F.J.
Format: Conference Proceeding
Language:English
Subjects:
Artificial neural networks
Input variables
Machine learning
Multi-layer neural network
Multilayer perceptrons
Neural networks
Proposals
Sequential analysis
Testing
USA Councils
Online Access:Request full text
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Summary: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.
ISSN:2161-4393
2161-4407
DOI:10.1109/IJCNN.2007.4371450