Iron Ore Price Prediction Based on Multiple Linear Regression Model

Yanyi Wang, Zhenwei Guo (), Yunrui Zhang, Xiangping Hu and Jianping Xiao ()
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Yanyi Wang: School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
Zhenwei Guo: School of Geosciences and Info-Physics, Central South University, 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

Sustainability, 2023, vol. 15, issue 22, 1-14

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.

Keywords: iron ore price; multiple linear regression; prediction; geophysics prospecting (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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