Arabica coffee price forecast: a neural network application CNN-BLSTM
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...
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| Tipo de documento: | Artigo |
| Estado: | Published version |
| Data de publicação: | 2022 |
| País: | Brasil |
| Recursos: | UNIFEI |
| Repositório: | Research, Society and Development |
| Idioma: | Português |
| OAI Identifier: | oai:ojs.pkp.sfu.ca:article/26101 |
| Acesso em linha: | https://rsdjournal.org/index.php/rsd/article/view/26101 |
| Nível de acesso: | Acesso aberto |
| Palavra-chave: | Artificial neural networks Arabica coffee Keras Python. Redes neuronales artificiales Café arábica Redes neurais artificiais |
| 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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