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

Autor: 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
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: FinTech, Vol 3, Iss 1, Pp 216-235 (2024)
Druh dokumentu: article
ISSN: 2674-1032
DOI: 10.3390/fintech3010013
Popis: 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.
Databáze: Directory of Open Access Journals
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