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

Tathiana M. Barchi, João Lucas Ferreira dos Santos, Priscilla Bassetto, Henrique Nazário Rocha, Sergio L. Stevan, Fernanda Cristina Correa, Yslene Rocha Kachba and Hugo Valadares Siqueira ()
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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
João Lucas Ferreira dos Santos: Graduate Program in Industrial Engineering (PPGEP), Federal University of Technology–Parana, Ponta Grossa 84017-220, Brazil
Priscilla Bassetto: Graduate Program in Industrial Engineering (PPGEP), Federal University of Technology–Parana, Ponta Grossa 84017-220, Brazil
Henrique Nazário Rocha: Department of Electrical Engineering, Federal University of Technology–Parana, Ponta Grossa 84017-220, Brazil
Sergio L. Stevan: Department of Electrical Engineering, Federal University of Technology–Parana, Ponta Grossa 84017-220, Brazil
Fernanda Cristina Correa: Department of Electrical Engineering, Federal University of Technology–Parana, Ponta Grossa 84017-220, Brazil
Yslene Rocha Kachba: Department of Industrial Engineering, Federal University of Technology–Parana, Ponta Grossa 84017-220, Brazil
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

FinTech, 2024, vol. 3, issue 1, 1-20

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.

Keywords: artificial neural networks; linear models; sugar price; forecasting (search for similar items in EconPapers)
JEL-codes: C6 F3 G O3 (search for similar items in EconPapers)
Date: 2024
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