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Iron Ore Price Prediction Based on Multiple Linear Regression Model

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  • Yanyi Wang

    (School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
    Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, (Central South University), Ministry of Education, Changsha 410083, China
    Hunan Key Laboratory of Nonferrous Resources and Geological Hazard Exploration, Changsha 410083, China)

  • Zhenwei Guo

    (School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
    Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, (Central South University), Ministry of Education, Changsha 410083, China
    Hunan Key Laboratory of Nonferrous Resources and Geological Hazard Exploration, Changsha 410083, China)

  • Yunrui Zhang

    (ESSEC Business School, 95021 Paris, France)

  • Xiangping Hu

    (Industrial Ecology Programme and Department of Energy and Process Engineering, Norwegian University of Science and Technology (NTNU), 7034 Trondheim, Norway)

  • Jianping Xiao

    (School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
    Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, (Central South University), Ministry of Education, Changsha 410083, China
    Hunan Key Laboratory of Nonferrous Resources and Geological Hazard Exploration, Changsha 410083, China)

Abstract

The fluctuation of iron ore prices is one of the most important factors affecting policy. Therefore, the accurate prediction of iron ore prices has significant value in analysis and judgment regarding future changes in policies. In this study, we propose a correlation analysis to extract eight influencing factors of iron ore prices and introduce multiple linear regression analysis to the prediction. With historical data, we establish a model to forecast iron ore prices from 2020 to 2024. Taking prices in 2018 and 2019 as samples to test the applicability of the model, we obtain an acceptable level of error between the predicted iron ore prices and the actual prices. The prediction model based on multiple linear regression has high prediction accuracy. Iron ore prices will show a relatively stable upward trend over the next five years without the effects of COVID-19.

Suggested Citation

  • Yanyi Wang & Zhenwei Guo & Yunrui Zhang & Xiangping Hu & Jianping Xiao, 2023. "Iron Ore Price Prediction Based on Multiple Linear Regression Model," Sustainability, MDPI, vol. 15(22), pages 1-14, November.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:22:p:15864-:d:1278494
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    References listed on IDEAS

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    1. Paria Akbary & Mohammad Ghiasi & Mohammad Reza Rezaie Pourkheranjani & Hamidreza Alipour & Noradin Ghadimi, 2019. "Extracting Appropriate Nodal Marginal Prices for All Types of Committed Reserve," Computational Economics, Springer;Society for Computational Economics, vol. 53(1), pages 1-26, January.
    2. Kearney, Adrienne A. & Lombra, Raymond E., 2009. "Gold and platinum: Toward solving the price puzzle," The Quarterly Review of Economics and Finance, Elsevier, vol. 49(3), pages 884-892, August.
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