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Abstract
Time Series Forecasting (TSF) allows the modeling of complex systems as “black-boxes”, being a focus of attention in several research arenas such asOperational Research,Statistics orComputer Science. AlternativeTSF approaches emerged from the Artificial Intelligence arena, where optimization algorithms inspired on natural selection processes, such asEvolutionary Algorithms (EAs), are popular. The present work reports on a two-level architecture, where a (meta-level) binaryEA will search for the bestARMA model, being the parameters optimized by a (low-level)EA, which encodes real values. The handicap of this approach is compared with conventional forecasting methods, being competitive.
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Departamento de Sistemas de Informação, Campus de Azurém, Universidade do Minho, 4800-058, Guimarães, Portugal
Paulo Cortez
Departamento de Informática, Campus de Gualtar, Universidade do Minho, 4710-057, Braga, Portugal
Miguel Rocha & José Neves
- Paulo Cortez
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- Miguel Rocha
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- José Neves
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Cortez, P., Rocha, M. & Neves, J. Evolving Time Series Forecasting ARMA Models.Journal of Heuristics10, 415–429 (2004). https://doi.org/10.1023/B:HEUR.0000034714.09838.1e
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