Modelling and forecast of charcoal prices using a neuro-fuzzy system

Detalhes bibliográficos
Autor(a) principal: Araújo Júnior, Carlos Alberto
Data de Publicação: 2016
Outros Autores: Silva, Liniker Fernandes da, Leite, Helio Garcia, Valdetaro, Erlon Barbosa, Donato, Danilo Barros, Castro, Renato Vinícius Oliveira Castro
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UFLA
Texto Completo: http://repositorio.ufla.br/jspui/handle/1/14092
Resumo: Using a monthly time series of charcoal prices in Minas Gerais from January 2000 to September 2014, this study aimed to evaluate the use of neuro-fuzzy system to model the series and forecasting prices. We used four modeling structures for different prices lags (1, 2, 3, 4 and 5 lags). The structure most appropriate for neuro-fuzzy system was chosen based on the root mean square error, mean absolute error, mean squared error, mean absolute percentage error and maximum absolute percentage error for the forecasted period. With the results found, it is possible to conclude that a neuro-fuzzy system can be used properly to predict the charcoal prices.
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spelling Modelling and forecast of charcoal prices using a neuro-fuzzy systemModelagem e prognose do preço de carvão usando um sistema neuro-fuzzyTime seriesComputational intelligenceANFISAdaptive network-based fuzzy inference systemSérie temporalInteligência computacionalSistema de inferência neuro-fuzzy adaptávelUsing a monthly time series of charcoal prices in Minas Gerais from January 2000 to September 2014, this study aimed to evaluate the use of neuro-fuzzy system to model the series and forecasting prices. We used four modeling structures for different prices lags (1, 2, 3, 4 and 5 lags). The structure most appropriate for neuro-fuzzy system was chosen based on the root mean square error, mean absolute error, mean squared error, mean absolute percentage error and maximum absolute percentage error for the forecasted period. With the results found, it is possible to conclude that a neuro-fuzzy system can be used properly to predict the charcoal prices.Utilizando dados da série temporal mensal de preços de carvão vegetal em Minas Gerais no período de janeiro de 2000 à setembro de 2014, este estudo teve como objetivo avaliar o uso do sistema neuro-fuzzy para modelagem e previsão de preços. Foram utilizados quatro estruturas de modelagem considerando diferentes defasagens da variável preço (1, 2, 3, 4 e 5 defasagens). A estrutura mais adequada para o sistema neurofuzzy foi escolhido com base nos valores de raiz quadrada do erro médio quadrático, erro médio absoluto, erro médio quadrático, erro médio percentual absoluto e máximo erro percentual absoluto para o período de previsão. Com os resultados encontrados, é possível concluir que um sistema neuro-fuzzy pode ser usado para prever corretamente os preços do carvão vegetal.Universidade Federal de Lavras (UFLA)2016-06-082017-08-01T20:13:35Z2017-08-01T20:13:35Z2017-08-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfARAÚJO JÚNIOR, C. A. et al. Modelling and forecast of charcoal prices using a neuro-fuzzy system. CERNE, [S.l.], v. 22, n. 2, p. 151-158, 2016. DOI: 10.1590/0104776020162222103.http://repositorio.ufla.br/jspui/handle/1/140922317-63420104-7760reponame:Repositório Institucional da UFLAinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAenghttp://www.cerne.ufla.br/site/index.php/CERNE/article/view/1199/916Copyright (c) 2016 CERNEhttp://creativecommons.org/licenses/by/4.0/Attribution 4.0 Internationalinfo:eu-repo/semantics/openAccessAraújo Júnior, Carlos AlbertoSilva, Liniker Fernandes daLeite, Helio GarciaValdetaro, Erlon BarbosaDonato, Danilo BarrosCastro, Renato Vinícius Oliveira Castro2021-07-19T02:02:46Zoai:localhost:1/14092Repositório InstitucionalPUBhttp://repositorio.ufla.br/oai/requestnivaldo@ufla.br || repositorio.biblioteca@ufla.bropendoar:2021-07-19T02:02:46Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)false
dc.title.none.fl_str_mv Modelling and forecast of charcoal prices using a neuro-fuzzy system
Modelagem e prognose do preço de carvão usando um sistema neuro-fuzzy
title Modelling and forecast of charcoal prices using a neuro-fuzzy system
spellingShingle Modelling and forecast of charcoal prices using a neuro-fuzzy system
Araújo Júnior, Carlos Alberto
Time series
Computational intelligence
ANFIS
Adaptive network-based fuzzy inference system
Série temporal
Inteligência computacional
Sistema de inferência neuro-fuzzy adaptável
title_short Modelling and forecast of charcoal prices using a neuro-fuzzy system
title_full Modelling and forecast of charcoal prices using a neuro-fuzzy system
title_fullStr Modelling and forecast of charcoal prices using a neuro-fuzzy system
title_full_unstemmed Modelling and forecast of charcoal prices using a neuro-fuzzy system
title_sort Modelling and forecast of charcoal prices using a neuro-fuzzy system
author Araújo Júnior, Carlos Alberto
author_facet Araújo Júnior, Carlos Alberto
Silva, Liniker Fernandes da
Leite, Helio Garcia
Valdetaro, Erlon Barbosa
Donato, Danilo Barros
Castro, Renato Vinícius Oliveira Castro
author_role author
author2 Silva, Liniker Fernandes da
Leite, Helio Garcia
Valdetaro, Erlon Barbosa
Donato, Danilo Barros
Castro, Renato Vinícius Oliveira Castro
author2_role author
author
author
author
author
dc.contributor.author.fl_str_mv Araújo Júnior, Carlos Alberto
Silva, Liniker Fernandes da
Leite, Helio Garcia
Valdetaro, Erlon Barbosa
Donato, Danilo Barros
Castro, Renato Vinícius Oliveira Castro
dc.subject.por.fl_str_mv Time series
Computational intelligence
ANFIS
Adaptive network-based fuzzy inference system
Série temporal
Inteligência computacional
Sistema de inferência neuro-fuzzy adaptável
topic Time series
Computational intelligence
ANFIS
Adaptive network-based fuzzy inference system
Série temporal
Inteligência computacional
Sistema de inferência neuro-fuzzy adaptável
description Using a monthly time series of charcoal prices in Minas Gerais from January 2000 to September 2014, this study aimed to evaluate the use of neuro-fuzzy system to model the series and forecasting prices. We used four modeling structures for different prices lags (1, 2, 3, 4 and 5 lags). The structure most appropriate for neuro-fuzzy system was chosen based on the root mean square error, mean absolute error, mean squared error, mean absolute percentage error and maximum absolute percentage error for the forecasted period. With the results found, it is possible to conclude that a neuro-fuzzy system can be used properly to predict the charcoal prices.
publishDate 2016
dc.date.none.fl_str_mv 2016-06-08
2017-08-01T20:13:35Z
2017-08-01T20:13:35Z
2017-08-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv ARAÚJO JÚNIOR, C. A. et al. Modelling and forecast of charcoal prices using a neuro-fuzzy system. CERNE, [S.l.], v. 22, n. 2, p. 151-158, 2016. DOI: 10.1590/0104776020162222103.
http://repositorio.ufla.br/jspui/handle/1/14092
identifier_str_mv ARAÚJO JÚNIOR, C. A. et al. Modelling and forecast of charcoal prices using a neuro-fuzzy system. CERNE, [S.l.], v. 22, n. 2, p. 151-158, 2016. DOI: 10.1590/0104776020162222103.
url http://repositorio.ufla.br/jspui/handle/1/14092
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv http://www.cerne.ufla.br/site/index.php/CERNE/article/view/1199/916
dc.rights.driver.fl_str_mv Copyright (c) 2016 CERNE
http://creativecommons.org/licenses/by/4.0/
Attribution 4.0 International
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2016 CERNE
http://creativecommons.org/licenses/by/4.0/
Attribution 4.0 International
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Universidade Federal de Lavras (UFLA)
publisher.none.fl_str_mv Universidade Federal de Lavras (UFLA)
dc.source.none.fl_str_mv 2317-6342
0104-7760
reponame:Repositório Institucional da UFLA
instname:Universidade Federal de Lavras (UFLA)
instacron:UFLA
instname_str Universidade Federal de Lavras (UFLA)
instacron_str UFLA
institution UFLA
reponame_str Repositório Institucional da UFLA
collection Repositório Institucional da UFLA
repository.name.fl_str_mv Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)
repository.mail.fl_str_mv nivaldo@ufla.br || repositorio.biblioteca@ufla.br
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