Forecasting Brazilian ethanol spot prices using LSTM

• Gustavo Carvalho Santos ^[1] ; Flavio Barboza ^[1] ; Antônio Cláudio Paschoarelli Veiga ^[1] ; Mateus Ferreira Silva ^[1]
  1. [1] Universidade Federal de Uberlândia

     Universidade Federal de Uberlândia

     Brasil

     
• Localización: Artificial intelligence in the energy industry / Ana Belén Gil González (ed. lit.), 2022, ISBN 978-3-0365-4605-6, págs. 67-81
• Idioma: inglés
• Enlaces
  • Texto Completo Libro (pdf)
• Resumen

  • Ethanol is one of the most used fuels in Brazil, which is the second-largest producer of thisbiofuel in the world. The uncertainty of price direction in the future increases the risk for agentsoperating in this market and can affect a dependent price chain, such as food and gasoline. This paperuses the architecture of recurrent neural networks—Long short-term memory (LSTM)—to predictBrazilian ethanol spot prices for three horizon-times (12, 6 and 3 months ahead). The proposed modelis compared to three benchmark algorithms: Random Forest, SVM Linear and RBF. We evaluatestatistical measures such as MSE (Mean Squared Error), MAPE (Mean Absolute Percentage Error),and accuracy to assess the algorithm robustness. Our findings suggest LSTM outperforms the othertechniques in regression, considering both MSE and MAPE but SVM Linear is better to identify pricetrends. Concerning predictions per se, all errors increase during the pandemic period, reinforcing thechallenge to identify patterns in crisis scenarios.