Forecasting Copper Prices Using Deep Learning: Implications for Energy Sector Economies

Reza Derakhshani (), Amin GhasemiNejad, Naeeme Amani Zarin, Mohammad Mahdi Amani Zarin and Mahdis sadat Jalaee
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Reza Derakhshani: Department of Earth Sciences, Utrecht University, 3584CB Utrecht, The Netherlands
Amin GhasemiNejad: Department of Economics, Faculty of Management and Economics, Shahid Bahonar University of Kerman, Kerman 76169-13439, Iran
Naeeme Amani Zarin: Department of Economics, Faculty of Management and Economics, Shahid Bahonar University of Kerman, Kerman 76169-13439, Iran
Mohammad Mahdi Amani Zarin: Department of Computer Sciences, Shahid Bahonar University of Kerman, Kerman 76169-13439, Iran
Mahdis sadat Jalaee: Department of Economics, Faculty of Management and Economics, Shahid Bahonar University of Kerman, Kerman 76169-13439, Iran

Mathematics, 2024, vol. 12, issue 15, 1-10

Abstract: Energy is a foundational element of the modern industrial economy. Prices of metals play a crucial role in energy sectors’ revenue evaluations, making them the cornerstone of effective payment management employed by resource policymakers. Copper is one of the most important industrial metals, and plays a vital role in various aspects of today’s economies. Copper is strongly associated with many industries, such as electrical wiring, construction, and equipment manufacturing; therefore, the price of copper has become a significant impact factor on the performance of related energy companies and economies. The accurate prediction of copper prices holds particular significance for market participants and policymakers. This study carried out research to address the gap in copper price forecasting using a one-dimensional convolutional neural network (1D-CNN). The proposed method was implemented and tested using extensive data spanning from November 1991 to May 2023. To assess the performance of the CNN model, standard evaluation metrics, such as the R-value, mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE), were employed. For the prediction of global copper prices, the proposed artificial intelligence algorithm demonstrated high accuracy. Lastly, future global copper prices were predicted up to 2027 by the CNN and compared with forecasts published by the International Monetary Fund and the International Society of Automation. The results show the exceptional performance of the CNN, establishing it as a reliable tool for monitoring copper prices and predicting global copper price volatilities near reality, and as carrying significant implications for policymakers and governments in shaping energy policies and ensuring equitable implementation of energy strategies.

Keywords: time series analysis; artificial intelligence; convolutional neural network; non-linear forecasting models; numerical methods in economics (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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