IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i22p15864-d1278494.html
   My bibliography  Save this article

Iron Ore Price Prediction Based on Multiple Linear Regression Model

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/22/15864/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/22/15864/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. An, Pengli & Xu, Qianqian, 2024. "Detecting the local characteristics from the iron ore import competition intensity among nations: A network-based resource allocation process method," Resources Policy, Elsevier, vol. 97(C).
    2. Zhi Lin Lee & Nur Haizum Abd Rahman & Jim Chong, 2025. "Predictive analysis of electric vehicle prices across various car brands in Germany," Quality & Quantity: International Journal of Methodology, Springer, vol. 59(2), pages 1081-1095, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhang, Meng & Guo, Huan & Sun, Ming & Liu, Sifeng & Forrest, Jeffrey, 2022. "A novel flexible grey multivariable model and its application in forecasting energy consumption in China," Energy, Elsevier, vol. 239(PE).
    2. Aboura Sofiane & Chevallier Julien & Jammazi Rania & Tiwari Aviral Kumar, 2016. "The place of gold in the cross-market dependencies," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(5), pages 567-586, December.
    3. Liu, Lijun & Qian, Jin & Hua, Li & Zhang, Bin, 2022. "System estimation of the SOFCs using fractional-order social network search algorithm," Energy, Elsevier, vol. 255(C).
    4. Cheng, Shen & Zhao, Gaiju & Gao, Ming & Shi, Yuetao & Huang, Mingming & Yousefi, Nasser, 2021. "Optimal hybrid energy system for locomotive utilizing improved Locust Swarm optimizer," Energy, Elsevier, vol. 218(C).
    5. Wu, Cong & Li, Jiaxuan & Liu, Wenjin & He, Yuzhe & Nourmohammadi, Samad, 2023. "Short-term electricity demand forecasting using a hybrid ANFIS–ELM network optimised by an improved parasitism–predation algorithm," Applied Energy, Elsevier, vol. 345(C).
    6. Adekoya, Oluwasegun B. & Oliyide, Johnson A., 2021. "How COVID-19 drives connectedness among commodity and financial markets: Evidence from TVP-VAR and causality-in-quantiles techniques," Resources Policy, Elsevier, vol. 70(C).
    7. Lu, Xiaohui & Li, Bing & Guo, Lin & Wang, Peifang & Yousefi, Nasser, 2021. "Exergy analysis of a polymer fuel cell and identification of its optimum operating conditions using improved Farmland Fertility Optimization," Energy, Elsevier, vol. 216(C).
    8. Brooks, Adria E. & Lesieutre, Bernard C., 2022. "A locational marginal price for frequency balancing operations in regulation markets," Applied Energy, Elsevier, vol. 308(C).
    9. Kucher, Oleg & McCoskey, Suzanne, 2017. "The long-run relationship between precious metal prices and the business cycle," The Quarterly Review of Economics and Finance, Elsevier, vol. 65(C), pages 263-275.
    10. Mishra, Bibhuti Ranjan & Pradhan, Ashis Kumar & Tiwari, Aviral Kumar & Shahbaz, Muhammad, 2019. "The dynamic causality between gold and silver prices in India: Evidence using time-varying and non-linear approaches," Resources Policy, Elsevier, vol. 62(C), pages 66-76.
    11. Mensi, Walid & Vo, Xuan Vinh & Kang, Sang Hoon, 2021. "Time and frequency connectedness and network across the precious metal and stock markets: Evidence from top precious metal importers and exporters," Resources Policy, Elsevier, vol. 72(C).
    12. O'Connor, Fergal A. & Lucey, Brian M. & Batten, Jonathan A. & Baur, Dirk G., 2015. "The financial economics of gold — A survey," International Review of Financial Analysis, Elsevier, vol. 41(C), pages 186-205.
    13. Husain, Shaiara & Tiwari, Aviral Kumar & Sohag, Kazi & Shahbaz, Muhammad, 2019. "Connectedness among crude oil prices, stock index and metal prices: An application of network approach in the USA," Resources Policy, Elsevier, vol. 62(C), pages 57-65.
    14. James Ming Chen & Mobeen Ur Rehman, 2021. "A Pattern New in Every Moment: The Temporal Clustering of Markets for Crude Oil, Refined Fuels, and Other Commodities," Energies, MDPI, vol. 14(19), pages 1-58, September.
    15. Liu, Xin & Li, Yang & Lin, Xueshan & Guo, Jiqun & Shi, Yunpeng & Shen, Yunwei, 2022. "Dynamic bidding strategy for a demand response aggregator in the frequency regulation market," Applied Energy, Elsevier, vol. 314(C).
    16. Yang, Zixuan & Liu, Qian & Zhang, Leiyu & Dai, Jialei & Razmjooy, Navid, 2020. "Model parameter estimation of the PEMFCs using improved Barnacles Mating Optimization algorithm," Energy, Elsevier, vol. 212(C).
    17. Hou, Rui & Li, Shanshan & Wu, Minrong & Ren, Guowen & Gao, Wei & Khayatnezhad, Majid & gholinia, Fatemeh, 2021. "Assessing of impact climate parameters on the gap between hydropower supply and electricity demand by RCPs scenarios and optimized ANN by the improved Pathfinder (IPF) algorithm," Energy, Elsevier, vol. 237(C).
    18. Yuan, Zhi & Wang, Weiqing & Wang, Haiyun & Mizzi, Scott, 2020. "Combination of cuckoo search and wavelet neural network for midterm building energy forecast," Energy, Elsevier, vol. 202(C).
    19. Desta Bati Nuno, 2022. "Analysis of the Causal Relationship between Precious Metal Prices and Inflation: Evidence from Ethiopia," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), vol. 0(1), pages 77-88.
    20. You, Wanhai & Chen, Jianyong & Xie, Haoqi & Ren, Yinghua, 2025. "Which uncertainty measure better predicts gold prices? New evidence from a CNN-LSTM approach," The North American Journal of Economics and Finance, Elsevier, vol. 76(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:22:p:15864-:d:1278494. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.