Selecting and ranking time series models using the NOEMON approach

Authors:
Ricardo B. C. Prudêncio;Teresa B. Ludermir
Affiliations:
Center of Informatics, Federal University of Pernambuco, Recife, PE, Brazil;Center of Informatics, Federal University of Pernambuco, Recife, PE, Brazil
Venue:
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Year:
2003

Citing 4
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Abstract

In this work, we proposed to use the NOEMON approach to rank and select time series models. Given a time series, the NOEMON approach provides a ranking of the candidate models to forecast that series, by combining the outputs of different learners. The best ranked models are then returned as the selected ones. In order to evaluate the proposed solution, we implemented a prototype that used MLP neural networks as the learners. Our experiments using this prototype revealed encouraging results.