Real-Time Forecasting by Bio-Inspired Models

Detalhes bibliográficos
Autor(a) principal: Cortez, Paulo
Data de Publicação: 2002
Outros Autores: Rocha, Miguel, Allegro, Fernando Sollari, Neves, José
Idioma: eng
Título da fonte: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Texto Completo: http://hdl.handle.net/1822/352
Resumo: 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.)
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spelling Real-Time Forecasting by Bio-Inspired ModelsArtificial Neural NetworksExponential SmoothingGenetic and Evolutionary AlgorithmsReal-Time ForecastingTime SeriesIn 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.)Universidade do MinhoCortez, PauloRocha, MiguelAllegro, Fernando SollariNeves, José20022002-01-01T00:00:00Zconference paperinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/1822/352engHAMZA, 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.info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-11T06:33:45Zoai:repositorium.sdum.uminho.pt:1822/352Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T15:57:03.673277Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Real-Time Forecasting by Bio-Inspired Models
title Real-Time Forecasting by Bio-Inspired Models
spellingShingle Real-Time Forecasting by Bio-Inspired Models
Cortez, Paulo
Artificial Neural Networks
Exponential Smoothing
Genetic and Evolutionary Algorithms
Real-Time Forecasting
Time Series
title_short Real-Time Forecasting by Bio-Inspired Models
title_full Real-Time Forecasting by Bio-Inspired Models
title_fullStr Real-Time Forecasting by Bio-Inspired Models
title_full_unstemmed Real-Time Forecasting by Bio-Inspired Models
title_sort Real-Time Forecasting by Bio-Inspired Models
author Cortez, Paulo
author_facet Cortez, Paulo
Rocha, Miguel
Allegro, Fernando Sollari
Neves, José
author_role author
author2 Rocha, Miguel
Allegro, Fernando Sollari
Neves, José
author2_role author
author
author
dc.contributor.none.fl_str_mv Universidade do Minho
dc.contributor.author.fl_str_mv Cortez, Paulo
Rocha, Miguel
Allegro, Fernando Sollari
Neves, José
dc.subject.por.fl_str_mv Artificial Neural Networks
Exponential Smoothing
Genetic and Evolutionary Algorithms
Real-Time Forecasting
Time Series
topic Artificial Neural Networks
Exponential Smoothing
Genetic and Evolutionary Algorithms
Real-Time Forecasting
Time Series
description 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.)
publishDate 2002
dc.date.none.fl_str_mv 2002
2002-01-01T00:00:00Z
dc.type.driver.fl_str_mv conference paper
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/1822/352
url http://hdl.handle.net/1822/352
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv HAMZA, 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.
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
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dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
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reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
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repository.mail.fl_str_mv info@rcaap.pt
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