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. © Springer-Verlag Berlin Heidelberg 2003.
CITATION STYLE
Prudêncio, R. B. C., & Ludermir, T. B. (2003). Selecting and ranking time series models using the NOEMON approach. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2714, 654–661. https://doi.org/10.1007/3-540-44989-2_78
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