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  • 标题:Previso do preo do café arábica: uma aplicao de redes neurais CNN-BLSTM
  • 本地全文:下载
  • 作者:José Airton Azevedo dos Santos
  • 期刊名称:Research, Society and Development
  • 电子版ISSN:2525-3409
  • 出版年度:2022
  • 卷号:11
  • 期号:3
  • 页码:1-11
  • DOI:10.33448/rsd-v11i3.26101
  • 语种:English
  • 出版社:Grupo de Pesquisa Metodologias em Ensino e Aprendizagem em Ciências
  • 摘要: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.
  • 关键词:Artificial neural networks;Arabica coffee;Keras;Python.
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