System Engineering and Productivity

System Engineering and Productivity

Forecasting Global Iron Ore Prices Using Neural Networks

Document Type : Research Paper

Authors
Pourya Farajian 1
Nima Farajian 2
1 Corresponding author: Lecturer, Department of Architectural and Urban Engineering, Faculty of Civil Engineering and Architecture, University of Eyvanekey, Eyvanekey, Iran
2 Lecturer, Department of Computer Engineering, Faculty of Electrical, Computer and Mechanical Engineering, University of Eyvanekey, Eyvanekey, Iran
10.22034/sep.2022.243419
Abstract
Today's world's dependence on technology increases human need for products produced from iron ore, and predictions indicate that steel demand will increase by 60 percent by 2035 (Mohammadi, Soltani Mohammadi, and Bakhshandeh Omnieh, 2013). For this reason, forecasting the price of metals, including iron ore, using quantitative and qualitative methods such as technical and economic market studies has not been very consistent with reality. One of the common methods of examining prices is the time series method. In this study, iron ore prices were predicted by modeling and using time series analysis with the help of a dynamic neural network. Next, the price of iron ore was estimated by using an artificial neural network method and considering the monthly price of iron ore and the factors affecting its fluctuations, and then the results obtained were evaluated in terms of predictability. The optimal neural network model with 3 layers and 10 neurons has estimated the price of iron ore with very good accuracy. In this model, the training error is about 1.7%, and for validation it is 2.3%, and the test error is 1.5%. Also, the regression and correlation values ​​of the data at a confidence level of 95% and a high correlation value of R2=0.98 indicate a good model with good accuracy.
Keywords
Iron ore price
Forecasting
Neural network

Copyright ©, Pourya Farajian, Nima Farajian

 

License

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System Engineering and Productivity
Volume 2, Issue 3 - Serial Number 4
Serial No. 4, Autumn Quarterly
Autumn 2022
Pages 113-126

  • Receive Date 03 November 2022
  • Revise Date 22 November 2022
  • Accept Date 04 December 2022
  • First Publish Date 04 December 2022
  • Publish Date 22 November 2022