Time series forecasting using a weighted cross-validation evolutionary artificial neural network ensemble
Autor(a) principal: | |
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Data de Publicação: | 2013 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://hdl.handle.net/1822/24678 |
Resumo: | The ability to forecast the future based on past data is a key tool to support individual and organizational decision making. In particular, the goal of Time Series Forecasting (TSF) is to predict the behavior of complex systems by looking only at past patterns of the same phenomenon. In recent years, several works in the literature have adopted Evolutionary Artificial Neural Networks (EANNs) for TSF. In this work, we propose a novel EANN approach, where a weighted n-fold validation fitness scheme is used to build an ensemble of neural networks, under four different combination methods: mean, median, softmax and rank-based. Several experiments were held, using six real-world time series with different characteristics and from distinct domains. Overall, the proposed approach achieved competitive results when compared with a non-weighted n-fold EANN ensemble, the simpler 0-fold EANN and also the popular Holt–Winters statistical method. |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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7160 |
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Time series forecasting using a weighted cross-validation evolutionary artificial neural network ensembleEnsemblesEvolutionary computationGenetic algorithmsMultilayer perceptronTime series forecastingScience & TechnologyThe ability to forecast the future based on past data is a key tool to support individual and organizational decision making. In particular, the goal of Time Series Forecasting (TSF) is to predict the behavior of complex systems by looking only at past patterns of the same phenomenon. In recent years, several works in the literature have adopted Evolutionary Artificial Neural Networks (EANNs) for TSF. In this work, we propose a novel EANN approach, where a weighted n-fold validation fitness scheme is used to build an ensemble of neural networks, under four different combination methods: mean, median, softmax and rank-based. Several experiments were held, using six real-world time series with different characteristics and from distinct domains. Overall, the proposed approach achieved competitive results when compared with a non-weighted n-fold EANN ensemble, the simpler 0-fold EANN and also the popular Holt–Winters statistical method.This work was supported by University Carlos III of Madrid and by Community of Madrid under project CCG10-UC3M/TIC-5174. The work of P. Cortez was funded by FEDER (program COMPETE and FCT) under project FCOMP-01-0124-FEDER-022674.Elsevier 1Universidade do MinhoPeralta Donate, JuanCortez, PauloGutierrez Sanchez, GermanSanchis de Miguel, Araceli2013-062013-06-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/1822/24678eng0925-231210.1016/j.neucom.2012.02.053http://dx.doi.org/10.1016/j.neucom.2012.02.053info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-05-25T01:53:30Zoai:repositorium.sdum.uminho.pt:1822/24678Portal AgregadorONGhttps://www.rcaap.pt/oai/openairemluisa.alvim@gmail.comopendoar:71602024-05-25T01:53:30Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Time series forecasting using a weighted cross-validation evolutionary artificial neural network ensemble |
title |
Time series forecasting using a weighted cross-validation evolutionary artificial neural network ensemble |
spellingShingle |
Time series forecasting using a weighted cross-validation evolutionary artificial neural network ensemble Peralta Donate, Juan Ensembles Evolutionary computation Genetic algorithms Multilayer perceptron Time series forecasting Science & Technology |
title_short |
Time series forecasting using a weighted cross-validation evolutionary artificial neural network ensemble |
title_full |
Time series forecasting using a weighted cross-validation evolutionary artificial neural network ensemble |
title_fullStr |
Time series forecasting using a weighted cross-validation evolutionary artificial neural network ensemble |
title_full_unstemmed |
Time series forecasting using a weighted cross-validation evolutionary artificial neural network ensemble |
title_sort |
Time series forecasting using a weighted cross-validation evolutionary artificial neural network ensemble |
author |
Peralta Donate, Juan |
author_facet |
Peralta Donate, Juan Cortez, Paulo Gutierrez Sanchez, German Sanchis de Miguel, Araceli |
author_role |
author |
author2 |
Cortez, Paulo Gutierrez Sanchez, German Sanchis de Miguel, Araceli |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Peralta Donate, Juan Cortez, Paulo Gutierrez Sanchez, German Sanchis de Miguel, Araceli |
dc.subject.por.fl_str_mv |
Ensembles Evolutionary computation Genetic algorithms Multilayer perceptron Time series forecasting Science & Technology |
topic |
Ensembles Evolutionary computation Genetic algorithms Multilayer perceptron Time series forecasting Science & Technology |
description |
The ability to forecast the future based on past data is a key tool to support individual and organizational decision making. In particular, the goal of Time Series Forecasting (TSF) is to predict the behavior of complex systems by looking only at past patterns of the same phenomenon. In recent years, several works in the literature have adopted Evolutionary Artificial Neural Networks (EANNs) for TSF. In this work, we propose a novel EANN approach, where a weighted n-fold validation fitness scheme is used to build an ensemble of neural networks, under four different combination methods: mean, median, softmax and rank-based. Several experiments were held, using six real-world time series with different characteristics and from distinct domains. Overall, the proposed approach achieved competitive results when compared with a non-weighted n-fold EANN ensemble, the simpler 0-fold EANN and also the popular Holt–Winters statistical method. |
publishDate |
2013 |
dc.date.none.fl_str_mv |
2013-06 2013-06-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://hdl.handle.net/1822/24678 |
url |
https://hdl.handle.net/1822/24678 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
0925-2312 10.1016/j.neucom.2012.02.053 http://dx.doi.org/10.1016/j.neucom.2012.02.053 |
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.publisher.none.fl_str_mv |
Elsevier 1 |
publisher.none.fl_str_mv |
Elsevier 1 |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
mluisa.alvim@gmail.com |
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1817544466791137280 |