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Forecasting Brazilian Ethanol Spot Prices Using LSTM

Author

Listed:
  • Gustavo Carvalho Santos

    (Electrical Engineering School, Federal University of Uberlândia, Uberlândia 38408-100, Brazil
    These authors contributed equally to this work.)

  • Flavio Barboza

    (School of Business and Management, Federal University of Uberlândia, Uberlândia 38408-100, Brazil
    These authors contributed equally to this work.)

  • Antônio Cláudio Paschoarelli Veiga

    (Electrical Engineering School, Federal University of Uberlândia, Uberlândia 38408-100, Brazil
    These authors contributed equally to this work.)

  • Mateus Ferreira Silva

    (School of Accounting, Federal University of Uberlândia, Uberlândia 38408-100, Brazil
    These authors contributed equally to this work.)

Abstract

Ethanol is one of the most used fuels in Brazil, which is the second-largest producer of this biofuel in the world. The uncertainty of price direction in the future increases the risk for agents operating in this market and can affect a dependent price chain, such as food and gasoline. This paper uses the architecture of recurrent neural networks—Long short-term memory (LSTM)—to predict Brazilian ethanol spot prices for three horizon-times (12, 6 and 3 months ahead). The proposed model is compared to three benchmark algorithms: Random Forest, SVM Linear and RBF. We evaluate statistical 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 other techniques in regression, considering both MSE and MAPE but SVM Linear is better to identify price trends. Concerning predictions per se, all errors increase during the pandemic period, reinforcing the challenge to identify patterns in crisis scenarios.

Suggested Citation

  • Gustavo Carvalho Santos & Flavio Barboza & Antônio Cláudio Paschoarelli Veiga & Mateus Ferreira Silva, 2021. "Forecasting Brazilian Ethanol Spot Prices Using LSTM," Energies, MDPI, vol. 14(23), pages 1-15, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:23:p:7987-:d:691284
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    References listed on IDEAS

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