Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/5929
Título: | Time series forecasting by evolutionary neural networks |
Autor(es): | Cortez, Paulo Rocha, Miguel Neves, José |
Palavras-chave: | Data forecasting Neural networks Data mining Time series Knowledge discovery |
Data: | 2005 |
Editora: | IGI Global |
Citação: | P. Cortez, M. Rocha and J. Neves. Time Series Forecasting by Evolutionary Neural Networks. In J. Rubuñal and J. Dorado (Eds.), Artificial Neural Networks in Real-Life Applications. chapter III, pp. 47-70, Hershey, USA, 2005. Idea Group Publishing, ISBN:1-59140903-9. |
Resumo(s): | This chapter presents a hybrid Evolutionary Computation/Neural Network combination for time series prediction. Neural networks are innate candidates for the forecasting domain due to advantages such as nonlinear learning and noise tolerance. However, the search for the ideal network structure is a complex and crucial task. Under this context, Evolutionary Computation, guided by the Bayesian Information Criterion, makes a promising global search approach for feature and model selection. A set of ten time series, from different domains, were used to evaluate this strategy, comparing it with a heuristic model selection, as well as with conventional forecasting methods (e.g., Holt-Winters and Box-Jenkins methodology). |
Tipo: | Capítulo de livro |
URI: | https://hdl.handle.net/1822/5929 |
ISBN: | 1-59140-903-9 |
DOI: | 10.4018/978-1-59140-902-1.ch003 |
Arbitragem científica: | yes |
Acesso: | Acesso restrito UMinho |
Aparece nas coleções: | DSI - Engenharia da Programação e dos Sistemas Informáticos |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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CHAPTERIII.pdf Acesso restrito! | 1,26 MB | Adobe PDF | Ver/Abrir |