Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/2223
Título: | Genetic and evolutionary algorithms for time series forecasting |
Autor(es): | Cortez, Paulo Rocha, Miguel Neves, José |
Palavras-chave: | Genetic and evolutionary algorithms Time series forecasting Time series analysis ARMA models |
Data: | 4-Jun-2001 |
Editora: | Springer |
Revista: | Lecture Notes in Computer Science (including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Citação: | INTERNATIONAL CONFERENCE ON INDUSTRIAL AND ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE AND EXPERT SYSTEMS (IEA/AIE), 14, Budapest, 2001 - "Engineering of intelligent systems : proceedings". Heidelberg : Springer, 2001. ISBN 3-540-42219-6. p. 393-402. |
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 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. On the other hand, Genetic and Evolutionary Algorithms (GEAs) are a novel technique increasingly used in Optimization and Machine Learning tasks. The present work reports on the forecast of several Time Series, by GEA based approaches, where Feature Analysis, based on statistical measures is used for dimensionality reduction. The handicap of the evolutionary approach is compared with conventional forecasting methods, being competitive. |
Tipo: | Artigo em ata de conferência |
URI: | https://hdl.handle.net/1822/2223 |
ISBN: | 3-540-42219-6 |
ISSN: | 0302-9743 |
Versão da editora: | The original publication is available at www.springerlink.com |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: | DI/CCTC - Artigos (papers) DSI - Engenharia da Programação e dos Sistemas Informáticos |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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geatsf.pdf | 212,16 kB | Adobe PDF | Ver/Abrir |