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: Cerne (Online)
Texto Completo: https://cerne.ufla.br/site/index.php/CERNE/article/view/1199
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 SYSTEMTime seriesComputational intelligenceANFISUsing 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.CERNECERNE2016-06-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://cerne.ufla.br/site/index.php/CERNE/article/view/1199CERNE; Vol. 22 No. 2 (2016); 151-158CERNE; v. 22 n. 2 (2016); 151-1582317-63420104-7760reponame:Cerne (Online)instname:Universidade Federal de Lavras (UFLA)instacron:UFLAenghttps://cerne.ufla.br/site/index.php/CERNE/article/view/1199/916Copyright (c) 2016 CERNEinfo:eu-repo/semantics/openAccessAraújo Júnior, Carlos AlbertoSilva, Liniker Fernandes daLeite, Helio GarciaValdetaro, Erlon BarbosaDonato, Danilo BarrosCastro, Renato Vinícius Oliveira Castro2016-07-08T11:19:26Zoai:cerne.ufla.br:article/1199Revistahttps://cerne.ufla.br/site/index.php/CERNEPUBhttps://cerne.ufla.br/site/index.php/CERNE/oaicerne@dcf.ufla.br||cerne@dcf.ufla.br2317-63420104-7760opendoar:2024-05-21T19:54:27.803511Cerne (Online) - Universidade Federal de Lavras (UFLA)true
dc.title.none.fl_str_mv MODELLING AND FORECAST OF CHARCOAL PRICES USING A NEURO-FUZZY SYSTEM
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
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
topic Time series
Computational intelligence
ANFIS
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
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 https://cerne.ufla.br/site/index.php/CERNE/article/view/1199
url https://cerne.ufla.br/site/index.php/CERNE/article/view/1199
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://cerne.ufla.br/site/index.php/CERNE/article/view/1199/916
dc.rights.driver.fl_str_mv Copyright (c) 2016 CERNE
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2016 CERNE
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv CERNE
CERNE
publisher.none.fl_str_mv CERNE
CERNE
dc.source.none.fl_str_mv CERNE; Vol. 22 No. 2 (2016); 151-158
CERNE; v. 22 n. 2 (2016); 151-158
2317-6342
0104-7760
reponame:Cerne (Online)
instname:Universidade Federal de Lavras (UFLA)
instacron:UFLA
instname_str Universidade Federal de Lavras (UFLA)
instacron_str UFLA
institution UFLA
reponame_str Cerne (Online)
collection Cerne (Online)
repository.name.fl_str_mv Cerne (Online) - Universidade Federal de Lavras (UFLA)
repository.mail.fl_str_mv cerne@dcf.ufla.br||cerne@dcf.ufla.br
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