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...
| Author: | |
|---|---|
| Format: | article |
| Status: | Published version |
| Publication Date: | 2022 |
| Country: | Brasil |
| Institution: | Universidade Federal de Itajubá (UNIFEI) |
| Repository: | Research, Society and Development |
| Language: | Portuguese |
| OAI Identifier: | oai:ojs.pkp.sfu.ca:article/26101 |
| Online Access: | https://rsdjournal.org/index.php/rsd/article/view/26101 |
| Access Level: | Open access |
| Keyword: | Artificial neural networks Arabica coffee Keras Python. Redes neuronales artificiales Café arábica Redes neurais artificiais |
| Summary: | 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. |
|---|