A Meta-Genetic Algorithm for Time Series Forecasting

Bibliographic Details
Main Author: Cortez, Paulo
Publication Date: 2001
Other Authors: Rocha, Miguel, Neves, José
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/1822/121
Summary: Alternative approaches for Time Series Forecasting (TSF) emerged from the Artificial Intelligence arena, where optimization algorithms inspired on natural selection processes, such as Genetic Algorithms (GAs) are popular. The present work reports on a two-level architecture, where a (meta-level) binary GA will search for the best TSF model, being the parameters optimized by a (low-level) GA, which encodes real values. The machine's performance of this approach was compared with conventional forecasting methods, exhibiting good results, specially when trended and nonlinear series are considered.
id RCAP_f57d20da6d595593522f14d483280df3
oai_identifier_str oai:repositorium.sdum.uminho.pt:1822/121
network_acronym_str RCAP
network_name_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository_id_str https://opendoar.ac.uk/repository/7160
spelling A Meta-Genetic Algorithm for Time Series ForecastingARMA Models(Meta-)Genetic AlgorithmsModel SelectionTime Series ForecastingAlternative approaches for Time Series Forecasting (TSF) emerged from the Artificial Intelligence arena, where optimization algorithms inspired on natural selection processes, such as Genetic Algorithms (GAs) are popular. The present work reports on a two-level architecture, where a (meta-level) binary GA will search for the best TSF model, being the parameters optimized by a (low-level) GA, which encodes real values. The machine's performance of this approach was compared with conventional forecasting methods, exhibiting good results, specially when trended and nonlinear series are considered.The work of Paulo Cortez was supported by the Portuguese Foundation of Science & Technology through the PRAXIS XXI/BD/13793/97 grant. The work of José Neves was supported by the PRAXIS project PRAXIS/P/EEI/13096/98.Universidade do MinhoCortez, PauloRocha, MiguelNeves, José2001-122001-12-01T00:00:00Zconference paperinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/1822/121engIn Luís Torgo Ed., Proceedings of Workshop on Artificial Intelligence Techniques for Financial Time Series Analysis (AIFTSA -01), 10th Portuguese Conference on Artificial Intelligence (EPIA'01), Porto, Portugal, pp. 21-31info: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:RCAAP2024-05-11T04:15:24Zoai:repositorium.sdum.uminho.pt:1822/121Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T14:43:33.846215Repositórios 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 A Meta-Genetic Algorithm for Time Series Forecasting
title A Meta-Genetic Algorithm for Time Series Forecasting
spellingShingle A Meta-Genetic Algorithm for Time Series Forecasting
Cortez, Paulo
ARMA Models
(Meta-)Genetic Algorithms
Model Selection
Time Series Forecasting
title_short A Meta-Genetic Algorithm for Time Series Forecasting
title_full A Meta-Genetic Algorithm for Time Series Forecasting
title_fullStr A Meta-Genetic Algorithm for Time Series Forecasting
title_full_unstemmed A Meta-Genetic Algorithm for Time Series Forecasting
title_sort A Meta-Genetic Algorithm for Time Series Forecasting
author Cortez, Paulo
author_facet Cortez, Paulo
Rocha, Miguel
Neves, José
author_role author
author2 Rocha, Miguel
Neves, José
author2_role author
author
dc.contributor.none.fl_str_mv Universidade do Minho
dc.contributor.author.fl_str_mv Cortez, Paulo
Rocha, Miguel
Neves, José
dc.subject.por.fl_str_mv ARMA Models
(Meta-)Genetic Algorithms
Model Selection
Time Series Forecasting
topic ARMA Models
(Meta-)Genetic Algorithms
Model Selection
Time Series Forecasting
description Alternative approaches for Time Series Forecasting (TSF) emerged from the Artificial Intelligence arena, where optimization algorithms inspired on natural selection processes, such as Genetic Algorithms (GAs) are popular. The present work reports on a two-level architecture, where a (meta-level) binary GA will search for the best TSF model, being the parameters optimized by a (low-level) GA, which encodes real values. The machine's performance of this approach was compared with conventional forecasting methods, exhibiting good results, specially when trended and nonlinear series are considered.
publishDate 2001
dc.date.none.fl_str_mv 2001-12
2001-12-01T00:00:00Z
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/1822/121
url http://hdl.handle.net/1822/121
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv In Luís Torgo Ed., Proceedings of Workshop on Artificial Intelligence Techniques for Financial Time Series Analysis (AIFTSA -01), 10th Portuguese Conference on Artificial Intelligence (EPIA'01), Porto, Portugal, pp. 21-31
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
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
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
_version_ 1833594845013213184

Similar Items