Multivariate analysis and neural networks application to price forecasting in the Brazilian agricultural market

Detalhes bibliográficos
Autor(a) principal: Pinheiro,Carlos Alberto Orge
Data de Publicação: 2017
Outros Autores: Senna,Valter de
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Ciência Rural
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782017000100931
Resumo: ABSTRACT: The purpose of this study is to apply the methodology proposed by PINHEIRO & SENNA (2015) to a set of agricultural products traded in Brazil. The multivariate and nonlinear character of this methodology has shown to be suitable, as compared to the neural network model, since it allows for a better predictive performance. Results obtained in an out-of-sample period, by using the calculated error and statistical test, confirmed this statement. This study will be useful to farmers as price forecasting based on their tendency is relevant.
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spelling Multivariate analysis and neural networks application to price forecasting in the Brazilian agricultural marketneural networksmultivariate analysisagricultural productsforecastingABSTRACT: The purpose of this study is to apply the methodology proposed by PINHEIRO & SENNA (2015) to a set of agricultural products traded in Brazil. The multivariate and nonlinear character of this methodology has shown to be suitable, as compared to the neural network model, since it allows for a better predictive performance. Results obtained in an out-of-sample period, by using the calculated error and statistical test, confirmed this statement. This study will be useful to farmers as price forecasting based on their tendency is relevant.Universidade Federal de Santa Maria2017-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782017000100931Ciência Rural v.47 n.1 2017reponame:Ciência Ruralinstname:Universidade Federal de Santa Maria (UFSM)instacron:UFSM10.1590/0103-8478cr20160077info:eu-repo/semantics/openAccessPinheiro,Carlos Alberto OrgeSenna,Valter deeng2016-12-06T00:00:00ZRevista
dc.title.none.fl_str_mv Multivariate analysis and neural networks application to price forecasting in the Brazilian agricultural market
title Multivariate analysis and neural networks application to price forecasting in the Brazilian agricultural market
spellingShingle Multivariate analysis and neural networks application to price forecasting in the Brazilian agricultural market
Pinheiro,Carlos Alberto Orge
neural networks
multivariate analysis
agricultural products
forecasting
title_short Multivariate analysis and neural networks application to price forecasting in the Brazilian agricultural market
title_full Multivariate analysis and neural networks application to price forecasting in the Brazilian agricultural market
title_fullStr Multivariate analysis and neural networks application to price forecasting in the Brazilian agricultural market
title_full_unstemmed Multivariate analysis and neural networks application to price forecasting in the Brazilian agricultural market
title_sort Multivariate analysis and neural networks application to price forecasting in the Brazilian agricultural market
author Pinheiro,Carlos Alberto Orge
author_facet Pinheiro,Carlos Alberto Orge
Senna,Valter de
author_role author
author2 Senna,Valter de
author2_role author
dc.contributor.author.fl_str_mv Pinheiro,Carlos Alberto Orge
Senna,Valter de
dc.subject.por.fl_str_mv neural networks
multivariate analysis
agricultural products
forecasting
topic neural networks
multivariate analysis
agricultural products
forecasting
description ABSTRACT: The purpose of this study is to apply the methodology proposed by PINHEIRO & SENNA (2015) to a set of agricultural products traded in Brazil. The multivariate and nonlinear character of this methodology has shown to be suitable, as compared to the neural network model, since it allows for a better predictive performance. Results obtained in an out-of-sample period, by using the calculated error and statistical test, confirmed this statement. This study will be useful to farmers as price forecasting based on their tendency is relevant.
publishDate 2017
dc.date.none.fl_str_mv 2017-01-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782017000100931
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782017000100931
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/0103-8478cr20160077
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Universidade Federal de Santa Maria
publisher.none.fl_str_mv Universidade Federal de Santa Maria
dc.source.none.fl_str_mv Ciência Rural v.47 n.1 2017
reponame:Ciência Rural
instname:Universidade Federal de Santa Maria (UFSM)
instacron:UFSM
instname_str Universidade Federal de Santa Maria (UFSM)
instacron_str UFSM
institution UFSM
reponame_str Ciência Rural
collection Ciência Rural
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1749140550882164736

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