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TitleReal-Time Forecasting by Bio-Inspired Models
Author(s)Cortez, Paulo
Rocha, Miguel
Allegro, Fernando Sollari
Neves, José
KeywordsArtificial Neural Networks
Exponential Smoothing
Genetic and Evolutionary Algorithms
Real-Time Forecasting
Time Series
Issue date2002
CitationHAMZA, 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.
Abstract(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.)
TypeConference paper
AccessOpen access
Appears in Collections:CAlg - Artigos em livros de atas/Papers in proceedings
DI/CCTC - Artigos (papers)
DSI - Engenharia da Programação e dos Sistemas Informáticos

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