Utilize este identificador para referenciar este registo: http://hdl.handle.net/1822/2221

TítuloEvolving time series forecasting ARMA models
Autor(es)Cortez, Paulo
Rocha, Miguel
Neves, José
Palavras-chaveARMA models
Evolutionary algorithms
Bayesian information criterion
Model selection
Time series analysis
RevistaJournal of Heuristics
Citação"Journal of heuristics" Amsterdam. ISSN 381-1231. 10:4 (July 2004). p. 415-429.
Resumo(s)Nowadays, the ability to forecast the future, based only on past data, leads to strategic advantages, which may be the key to success in organizations. Time Series Forecasting (TSF) allows the modeling of complex systems as ``black-boxes'', being a focus of attention in several research arenas such as Operational Research, Statistics or Computer Science. Alternative TSF approaches emerged from the Artificial Intelligence arena, where optimization algorithms inspired on natural selection processes, such as Evolutionary Algorithms (EAs), are popular. The present work reports on a two-level architecture, where a (meta-level) binary EA will search for the best AutoRegressive Moving-Average (ARMA) 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.
Versão da editoraThe original publication is available at www.springerlink.com
Arbitragem científicayes
Aparece nas coleções:DSI - Engenharia da Programação e dos Sistemas Informáticos
DI/CCTC - Artigos (papers)
CAlg - Artigos em revistas internacionais/Papers in international journals

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