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Comparative Analysis of Linear Models and Artificial Neural Networks for Sugar Price Prediction

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  • Tathiana M. Barchi

    (Graduate Program Computer Sciences (PPGCC), Federal University of Technology–Paraná (UTFPR), Dr. Washington Subtil Chueire St., 330, Jardim Carvalho, Ponta Grossa 84017-220, Brazil
    Graduate Program in Industrial Engineering (PPGEP), Federal University of Technology–Parana, Ponta Grossa 84017-220, Brazil
    These authors contributed equally to this work.)

  • João Lucas Ferreira dos Santos

    (Graduate Program in Industrial Engineering (PPGEP), Federal University of Technology–Parana, Ponta Grossa 84017-220, Brazil
    These authors contributed equally to this work.)

  • Priscilla Bassetto

    (Graduate Program in Industrial Engineering (PPGEP), Federal University of Technology–Parana, Ponta Grossa 84017-220, Brazil
    These authors contributed equally to this work.)

  • Henrique Nazário Rocha

    (Department of Electrical Engineering, Federal University of Technology–Parana, Ponta Grossa 84017-220, Brazil
    These authors contributed equally to this work.)

  • Sergio L. Stevan

    (Department of Electrical Engineering, Federal University of Technology–Parana, Ponta Grossa 84017-220, Brazil
    Graduate Program in Electrical Engineering (PPGEE), Federal University of Technology–Parana, Ponta Grossa 84017-220, Brazil
    These authors contributed equally to this work.)

  • Fernanda Cristina Correa

    (Department of Electrical Engineering, Federal University of Technology–Parana, Ponta Grossa 84017-220, Brazil
    Graduate Program in Electrical Engineering (PPGEE), Federal University of Technology–Parana, Ponta Grossa 84017-220, Brazil
    These authors contributed equally to this work.)

  • Yslene Rocha Kachba

    (Department of Industrial Engineering, Federal University of Technology–Parana, Ponta Grossa 84017-220, Brazil
    These authors contributed equally to this work.)

  • Hugo Valadares Siqueira

    (Graduate Program Computer Sciences (PPGCC), Federal University of Technology–Paraná (UTFPR), Dr. Washington Subtil Chueire St., 330, Jardim Carvalho, Ponta Grossa 84017-220, Brazil
    Graduate Program in Industrial Engineering (PPGEP), Federal University of Technology–Parana, Ponta Grossa 84017-220, Brazil
    Department of Electrical Engineering, Federal University of Technology–Parana, Ponta Grossa 84017-220, Brazil
    Graduate Program in Electrical Engineering (PPGEE), Federal University of Technology–Parana, Ponta Grossa 84017-220, Brazil)

Abstract

Sugar is an important commodity that is used beyond the food industry. It can be produced from sugarcane and sugar beet, depending on the region. Prices worldwide differ due to high volatility, making it difficult to estimate their forecast. Thus, the present work aims to predict the prices of kilograms of sugar from four databases: the European Union, the United States, Brazil, and the world. To achieve this, linear methods from the Box and Jenkins family were employed, together with classic and new approaches of artificial neural networks: the feedforward Multilayer Perceptron and extreme learning machines, and the recurrent proposals Elman Network, Jordan Network, and Echo State Networks considering two reservoir designs. As performance metrics, the MAE and MSE were addressed. The results indicated that the neural models were more accurate than linear ones. In addition, the MLP and the Elman networks stood out as the winners.

Suggested Citation

  • Tathiana M. Barchi & João Lucas Ferreira dos Santos & Priscilla Bassetto & Henrique Nazário Rocha & Sergio L. Stevan & Fernanda Cristina Correa & Yslene Rocha Kachba & Hugo Valadares Siqueira, 2024. "Comparative Analysis of Linear Models and Artificial Neural Networks for Sugar Price Prediction," FinTech, MDPI, vol. 3(1), pages 1-20, March.
    • Handle: RePEc:gam:jfinte:v:3:y:2024:i:1:p:13-235:d:1355634
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    References listed on IDEAS

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    1. Pagani, Valentina & Stella, Tommaso & Guarneri, Tommaso & Finotto, Giacomo & van den Berg, Maurits & Marin, Fabio Ricardo & Acutis, Marco & Confalonieri, Roberto, 2017. "Forecasting sugarcane yields using agro-climatic indicators and Canegro model: A case study in the main production region in Brazil," Agricultural Systems, Elsevier, vol. 154(C), pages 45-52.
    2. Lin, Biing-Hwan & Variyam, Jayachandran N. & Allshouse, Jane E. & Cromartie, John, 2003. "Food And Agricultural Commodity Consumption In The United States: Looking Ahead To 2020," Agricultural Economic Reports 33959, United States Department of Agriculture, Economic Research Service.
    3. Ioannis Kosmas & Theofanis Papadopoulos & Georgia Dede & Christos Michalakelis, 2023. "The Use of Artificial Neural Networks in the Public Sector," FinTech, MDPI, vol. 2(1), pages 1-15, March.
    4. Samuel Asante Gyamerah & Janet Arthur & Saviour Worlanyo Akuamoah & Yethu Sithole, 2023. "Measurement and Impact of Longevity Risk in Portfolios of Pension Annuity: The Case in Sub Saharan Africa," FinTech, MDPI, vol. 2(1), pages 1-20, January.
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