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Time Series Prediction with Artificial Neural Networks: An Analysis Using Brazilian Soybean Production

Author

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  • Emerson Rodolfo Abraham

    (Postgraduate Program in Production Engineering, Universidade Paulista-UNIP, Dr. Bacelar Street 1212, São Paulo 04026-002, Brazil)

  • João Gilberto Mendes dos Reis

    (Postgraduate Program in Production Engineering, Universidade Paulista-UNIP, Dr. Bacelar Street 1212, São Paulo 04026-002, Brazil
    Postgraduate Program in Business Administration, Universidade Paulista-UNIP, Dr. Bacelar Street 1212, São Paulo 04026-002, Brazil
    Postgraduate Program in Agribusiness, Universidade Federal da Grande Dourados, Dourados 79804-970, Brazil)

  • Oduvaldo Vendrametto

    (Postgraduate Program in Production Engineering, Universidade Paulista-UNIP, Dr. Bacelar Street 1212, São Paulo 04026-002, Brazil)

  • Pedro Luiz de Oliveira Costa Neto

    (Postgraduate Program in Production Engineering, Universidade Paulista-UNIP, Dr. Bacelar Street 1212, São Paulo 04026-002, Brazil)

  • Rodrigo Carlo Toloi

    (Postgraduate Program in Production Engineering, Universidade Paulista-UNIP, Dr. Bacelar Street 1212, São Paulo 04026-002, Brazil
    Instituto Federal de Mato Grosso, Campus Rondonópolis, Rondonópolis 78721-520, Brazil)

  • Aguinaldo Eduardo de Souza

    (Postgraduate Program in Production Engineering, Universidade Paulista-UNIP, Dr. Bacelar Street 1212, São Paulo 04026-002, Brazil
    Faculdade de Tecnologia de São Sebastião, Centro Paula Souza, São Sebastião 11600-970, Brazil
    Faculdade de São Vicente-UNIBR, São Vicente 11310-200, Brazil)

  • Marcos de Oliveira Morais

    (Postgraduate Program in Production Engineering, Universidade Paulista-UNIP, Dr. Bacelar Street 1212, São Paulo 04026-002, Brazil
    Universidade Santo Amaro-UNISA, Isabel Schmidt Street 349, São Paulo 04743-030, Brazil
    Centro Universitário Estácio São Paulo, Eng. Armando de Arruda Pereira Avenue, São Paulo 04309-010, Brazil)

Abstract

Food production to meet human demand has been a challenge to society. Nowadays, one of the main sources of feeding is soybean. Considering agriculture food crops, soybean is sixth by production volume and the fourth by both production area and economic value. The grain can be used directly to human consumption, but it is highly used as a source of protein for animal production that corresponds 75% of the total, or as oil and derived food products. Brazil and the US are the most important players responsible for more than 70% of world production. Therefore, a reliable forecasting is essential for decision-makers to plan adequate policies to this important commodity and to establish the necessary logistical resources. In this sense, this study aims to predict soybean harvest area, yield, and production using Artificial Neural Networks (ANN) and compare with classical methods of Time Series Analysis. To this end, we collected data from a time series (1961–2016) regarding soybean production in Brazil. The results reveal that ANN is the best approach to predict soybean harvest area and production while classical linear function remains more effective to predict soybean yield. Moreover, ANN presents as a reliable model to predict time series and can help the stakeholders to anticipate the world soybean offer.

Suggested Citation

  • Emerson Rodolfo Abraham & João Gilberto Mendes dos Reis & Oduvaldo Vendrametto & Pedro Luiz de Oliveira Costa Neto & Rodrigo Carlo Toloi & Aguinaldo Eduardo de Souza & Marcos de Oliveira Morais, 2020. "Time Series Prediction with Artificial Neural Networks: An Analysis Using Brazilian Soybean Production," Agriculture, MDPI, vol. 10(10), pages 1-18, October.
    • Handle: RePEc:gam:jagris:v:10:y:2020:i:10:p:475-:d:428232
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    References listed on IDEAS

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    3. Juan D. Borrero & Jesús Mariscal & Alfonso Vargas-Sánchez, 2022. "A New Predictive Algorithm for Time Series Forecasting Based on Machine Learning Techniques: Evidence for Decision Making in Agriculture and Tourism Sectors," Stats, MDPI, vol. 5(4), pages 1-14, November.
    4. Sebastian Kujawa & Gniewko Niedbała, 2021. "Artificial Neural Networks in Agriculture," Agriculture, MDPI, vol. 11(6), pages 1-6, May.
    5. Marley Nunes Vituri Toloi & Silvia Helena Bonilla & Rodrigo Carlo Toloi & Helton Raimundo Oliveira Silva & Irenilza de Alencar Nääs, 2021. "Development Indicators and Soybean Production in Brazil," Agriculture, MDPI, vol. 11(11), pages 1-15, November.
    6. Xue-Bo Jin & Wen-Tao Gong & Jian-Lei Kong & Yu-Ting Bai & Ting-Li Su, 2022. "PFVAE: A Planar Flow-Based Variational Auto-Encoder Prediction Model for Time Series Data," Mathematics, MDPI, vol. 10(4), pages 1-17, February.
    7. Lamichhane, Sabhyata & Mei, Bin & Siry, Jacek, 2023. "Forecasting pine sawtimber stumpage prices: A comparison between a time series hybrid model and an artificial neural network," Forest Policy and Economics, Elsevier, vol. 154(C).

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