Utilize este identificador para referenciar este registo: http://hdl.handle.net/1822/352

TítuloReal-Time Forecasting by Bio-Inspired Models
Autor(es)Cortez, Paulo
Rocha, Miguel
Allegro, Fernando Sollari
Neves, José
Palavras-chaveArtificial Neural Networks
Exponential Smoothing
Genetic and Evolutionary Algorithms
Real-Time Forecasting
Time Series
Data2002
CitaçãoHAMZA, M. H., ed. lit. - “Artificial Intelligence and Applications : proceedings of the IASTED International Conference, 2, Málaga, Spain, 2002”. Anaheim ; Calgary ; Zurich : IASTED ACTA Press, 2002. p. 52-57.
Resumo(s)In recent years, bio-inspired methods for problem solving, such as Artificial Neural Networks (ANNs) or Genetic and Evolutionary Algorithms (GEAs), have gained an increasing acceptance as alternative approaches for forecasting, due to advantages such as nonlinear learning and adaptive search. The present work reports the use of these techniques for Real-Time Forecasting (RTF), where there is a need for an autonomous system capable of fast replies. Comparisons among bio-inspired and conventional approaches (e.g., Exponential Smoothing), revealed better forecasting performances for the evolutionary and connectionist models.)
TipoconferencePaper
URIhttp://hdl.handle.net/1822/352
Arbitragem científicayes
AcessoopenAccess
Aparece nas coleções:DSI - Engenharia da Programação e dos Sistemas Informáticos
DI/CCTC - Artigos (papers)
CAlg - Artigos em livros de atas/Papers in proceedings

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