Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/14848
Título: | Evolving sparsely connected neural networks for multi-step ahead forecasting |
Autor(es): | Peralta Donate, Juan Cortez, Paulo Gutierrez Sanchez, German Sanchis de Miguel, Araceli |
Palavras-chave: | Connectionism and neural nets Hybrid systems estimation distribution algorithm forecasting multilayer perceptron time series |
Data: | Jul-2011 |
Editora: | ACM |
Resumo(s): | Time Series Forecasting (TSF) is an important tool to sup- port decision making. Artificial Neural Networks (ANN) are innate candidates for TSF due to advantages such as nonlin- ear learning and noise tolerance. However, the search for the best ANN is a complex task that highly affects the forecast- ing performance. In this paper, we propose a novel Sparsely connected Evolutionary ANN (SEANN), which evolves more flexible ANN structures to perform multi-step ahead fore- casts. This approach is compared with a similar strategy but that only evolves fully connected ANNs (FEANN) and a conventional TSF method (i.e. ARIMA methodology). A set of six time series, from different real-world domains, was used in the comparison. Overall, the obtained results re- veal the proposed SEANN approach as the best forecasting method, optimizing more simpler structures and requiring less computational effort when compared with the fully con- nected evolutionary ANN strategy. |
Tipo: | Artigo em ata de conferência |
URI: | https://hdl.handle.net/1822/14848 |
ISBN: | 978-1-4503-0690-4 |
DOI: | 10.1145/2001858.2001982 |
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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pp191-Peralta.pdf | 111,02 kB | Adobe PDF | Ver/Abrir |