Arabica coffee price forecast: a neural network application CNN-BLSTM

Detalhes bibliográficos
Autor(a) principal: Santos, José Airton Azevedo dos
Data de Publicação: 2022
Tipo de documento: Artigo
Idioma: por
Título da fonte: Research, Society and Development
Texto Completo: https://rsdjournal.org/index.php/rsd/article/view/26101
Resumo: This work proposes the use of the CNN-BLSTM neural network as a tool to predict the price of arabica coffee. The database provided by CEPEA (Center for Advanced Studies in Applied Economics) presents a historical series of the price of arabica coffee, in the period between January 1997 and December 2021. Forecast models based on neural networks LSTM, BLSTM, CNN and CNN-BLSTM were implemented, in the Python language, using the Keras framework. Results obtained, from the four models, were compared using MAE, RMSE and MAPE metrics. It was verified, for a horizon of 6 months, that the CNN-BLSTM model presented better performance.
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spelling Arabica coffee price forecast: a neural network application CNN-BLSTMPredicción del precio del café arábica: una aplicación de red neuronal CNN-BLSTMPrevisão do preço do café arábica: uma aplicação de redes neurais CNN-BLSTMArtificial neural networksArabica coffeeKerasPython.Redes neuronales artificialesCafé arábicaKerasPython.Redes neurais artificiaisCafé arábicaKerasPython.This work proposes the use of the CNN-BLSTM neural network as a tool to predict the price of arabica coffee. The database provided by CEPEA (Center for Advanced Studies in Applied Economics) presents a historical series of the price of arabica coffee, in the period between January 1997 and December 2021. Forecast models based on neural networks LSTM, BLSTM, CNN and CNN-BLSTM were implemented, in the Python language, using the Keras framework. Results obtained, from the four models, were compared using MAE, RMSE and MAPE metrics. It was verified, for a horizon of 6 months, that the CNN-BLSTM model presented better performance.Este trabajo propone el uso de la red neuronal CNN-BLSTM como herramienta para predecir el precio del café arábica. La base de datos proporcionada por CEPEA (Centro de Estudios Avanzados en Economía Aplicada) presenta una serie histórica del precio del café arábica, en el período comprendido entre enero de 1997 y diciembre de 2021. Modelos de pronóstico basados en redes neuronales LSTM, BLSTM, CNN y CNN-BLSTM fueron implementados, en lenguaje Python, usando el framework Keras. Los resultados obtenidos, de los cuatro modelos, se compararon utilizando métricas MAE, RMSE y MAPE. Se verificó, para un horizonte de 6 meses, que el modelo CNN-BLSTM presentó mejor desempeño.Este trabalho propõe a utilização da rede neural CNN-BLSTM como ferramenta de previsão do preço do café arábica. A base de dados disponibilizada pelo CEPEA (Centro de Estudos Avançados em Economia Aplicada) apresenta uma série histórica, do preço do café arábica, no período entre janeiro de 1997 e dezembro de 2021. Modelos de previsão baseados em redes neurais LSTM, BLSTM, CNN e CNN-BLSTM foram implementados, na linguagem Python, utilizando o framework Keras. Resultados obtidos, dos quatro modelos, foram comparados por meio das métricas MAE, RMSE e MAPE. Verificou-se, para um horizonte de 6 meses, que o modelo CNN-BLSTM apresentou melhor desempenho.Research, Society and Development2022-02-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://rsdjournal.org/index.php/rsd/article/view/2610110.33448/rsd-v11i3.26101Research, Society and Development; Vol. 11 No. 3; e3511326101Research, Society and Development; Vol. 11 Núm. 3; e3511326101Research, Society and Development; v. 11 n. 3; e35113261012525-3409reponame:Research, Society and Developmentinstname:Universidade Federal de Itajubá (UNIFEI)instacron:UNIFEIporhttps://rsdjournal.org/index.php/rsd/article/view/26101/22964Copyright (c) 2022 José Airton Azevedo dos Santoshttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessSantos, José Airton Azevedo dos2022-03-09T13:44:38Zoai:ojs.pkp.sfu.ca:article/26101Revistahttps://rsdjournal.org/index.php/rsd/indexPUBhttps://rsdjournal.org/index.php/rsd/oairsd.articles@gmail.com2525-34092525-3409opendoar:2024-01-17T09:44:13.696379Research, Society and Development - Universidade Federal de Itajubá (UNIFEI)false
dc.title.none.fl_str_mv Arabica coffee price forecast: a neural network application CNN-BLSTM
Predicción del precio del café arábica: una aplicación de red neuronal CNN-BLSTM
Previsão do preço do café arábica: uma aplicação de redes neurais CNN-BLSTM
title Arabica coffee price forecast: a neural network application CNN-BLSTM
spellingShingle Arabica coffee price forecast: a neural network application CNN-BLSTM
Santos, José Airton Azevedo dos
Artificial neural networks
Arabica coffee
Keras
Python.
Redes neuronales artificiales
Café arábica
Keras
Python.
Redes neurais artificiais
Café arábica
Keras
Python.
title_short Arabica coffee price forecast: a neural network application CNN-BLSTM
title_full Arabica coffee price forecast: a neural network application CNN-BLSTM
title_fullStr Arabica coffee price forecast: a neural network application CNN-BLSTM
title_full_unstemmed Arabica coffee price forecast: a neural network application CNN-BLSTM
title_sort Arabica coffee price forecast: a neural network application CNN-BLSTM
author Santos, José Airton Azevedo dos
author_facet Santos, José Airton Azevedo dos
author_role author
dc.contributor.author.fl_str_mv Santos, José Airton Azevedo dos
dc.subject.por.fl_str_mv Artificial neural networks
Arabica coffee
Keras
Python.
Redes neuronales artificiales
Café arábica
Keras
Python.
Redes neurais artificiais
Café arábica
Keras
Python.
topic Artificial neural networks
Arabica coffee
Keras
Python.
Redes neuronales artificiales
Café arábica
Keras
Python.
Redes neurais artificiais
Café arábica
Keras
Python.
description This work proposes the use of the CNN-BLSTM neural network as a tool to predict the price of arabica coffee. The database provided by CEPEA (Center for Advanced Studies in Applied Economics) presents a historical series of the price of arabica coffee, in the period between January 1997 and December 2021. Forecast models based on neural networks LSTM, BLSTM, CNN and CNN-BLSTM were implemented, in the Python language, using the Keras framework. Results obtained, from the four models, were compared using MAE, RMSE and MAPE metrics. It was verified, for a horizon of 6 months, that the CNN-BLSTM model presented better performance.
publishDate 2022
dc.date.none.fl_str_mv 2022-02-10
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://rsdjournal.org/index.php/rsd/article/view/26101
10.33448/rsd-v11i3.26101
url https://rsdjournal.org/index.php/rsd/article/view/26101
identifier_str_mv 10.33448/rsd-v11i3.26101
dc.language.iso.fl_str_mv por
language por
dc.relation.none.fl_str_mv https://rsdjournal.org/index.php/rsd/article/view/26101/22964
dc.rights.driver.fl_str_mv Copyright (c) 2022 José Airton Azevedo dos Santos
https://creativecommons.org/licenses/by/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2022 José Airton Azevedo dos Santos
https://creativecommons.org/licenses/by/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Research, Society and Development
publisher.none.fl_str_mv Research, Society and Development
dc.source.none.fl_str_mv Research, Society and Development; Vol. 11 No. 3; e3511326101
Research, Society and Development; Vol. 11 Núm. 3; e3511326101
Research, Society and Development; v. 11 n. 3; e3511326101
2525-3409
reponame:Research, Society and Development
instname:Universidade Federal de Itajubá (UNIFEI)
instacron:UNIFEI
instname_str Universidade Federal de Itajubá (UNIFEI)
instacron_str UNIFEI
institution UNIFEI
reponame_str Research, Society and Development
collection Research, Society and Development
repository.name.fl_str_mv Research, Society and Development - Universidade Federal de Itajubá (UNIFEI)
repository.mail.fl_str_mv rsd.articles@gmail.com
_version_ 1797052704101498880

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