Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/14849
Título: | Evolving time-lagged feedforward neural networks for time series forecasting |
Autor(es): | Peralta Donate, Juan Cortez, Paulo Gutierrez Sanchez, German Sanchis de Miguel, Araceli |
Palavras-chave: | Connectionism and neural nets Hybrid systems artificial neural networks estimation distribution algorithm forecasting time series |
Data: | Jul-2011 |
Editora: | ACM |
Resumo(s): | Time Series Forecasting (TSF) is an important tool to sup- port both individual and organizational decisions. In this work, we propose a novel automatic Evolutionary Time- Lagged Feedforward Network (ETLFN) approach for TSF, based on an Estimation Distribution Algorithm (EDA) that evolves not only Artificial Neural Network (ANN) parame- ters but also which set of time lags are fed into the fore- casting model. Such approach is compared with similar strategy that only selects ANN parameter and the conven- tional TSF ARIMA methodology. Several experiments were held by considering six time series from distinct domains. The obtained multi-step ahead forecasts were evaluated us- ing SMAPE error criteria. Overall, the proposed ETLFN method obtained the best forecasting results. Moreover, it favors simpler neural network models, thus requiring less computational effort. |
Tipo: | Artigo em ata de conferência |
URI: | https://hdl.handle.net/1822/14849 |
ISBN: | 978-1-4503-0690-4 |
DOI: | 10.1145/2001858.2001950 |
Versão da editora: | http://dl.acm.org/ |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: | DSI - Engenharia da Programação e dos Sistemas Informáticos |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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pp185-Peralta.pdf | 140,51 kB | Adobe PDF | Ver/Abrir |