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Forecasting iron ore import and consumption of China using grey model optimized by particle swarm optimization algorithm

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  • Ma, Weimin
  • Zhu, Xiaoxi
  • Wang, Miaomiao

Abstract

The iron and steel industry plays a fundamental role in a country's national economy, especially in developing countries. China is the largest iron ore consumption market in the world. However, because of limited domestic iron ore resources, a large proportion of iron ore is imported from other countries. Faced with the conflict between the iron ore supply shortage and the growing demand, it is necessary for the government to predict imports and total consumption. This paper develops a high-precision hybrid model based on grey prediction and rolling mechanism optimized by particle swarm optimization algorithm. We use the China Statistical Yearbook (1996–2011) as our database to test the efficiency and accuracy of the proposed method. According to the experimental results, the proposed new method clearly can improve the prediction accuracy of the original grey model. Future projections have also been done for iron ore imports and total consumption in China in the next five years.

Suggested Citation

  • Ma, Weimin & Zhu, Xiaoxi & Wang, Miaomiao, 2013. "Forecasting iron ore import and consumption of China using grey model optimized by particle swarm optimization algorithm," Resources Policy, Elsevier, vol. 38(4), pages 613-620.
    • Handle: RePEc:eee:jrpoli:v:38:y:2013:i:4:p:613-620
    DOI: 10.1016/j.resourpol.2013.09.007
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    9. Peng, Cheng & Chen, Heng & Lin, Chaoran & Guo, Shuang & Yang, Zhi & Chen, Ke, 2021. "A framework for evaluating energy security in China: Empirical analysis of forecasting and assessment based on energy consumption," Energy, Elsevier, vol. 234(C).
    10. Chen, Wenhui & Lei, Yalin & Jiang, Yong, 2016. "Influencing factors analysis of China’s iron import price: Based on quantile regression model," Resources Policy, Elsevier, vol. 48(C), pages 68-76.
    11. Torbat, Sheida & Khashei, Mehdi & Bijari, Mehdi, 2018. "A hybrid probabilistic fuzzy ARIMA model for consumption forecasting in commodity markets," Economic Analysis and Policy, Elsevier, vol. 58(C), pages 22-31.
    12. Sun, Sizhong & Anwar, Sajid, 2019. "R&D activities and FDI in China’s iron ore mining industry," Economic Analysis and Policy, Elsevier, vol. 62(C), pages 47-56.
    13. Geng Xu & Fei Li & Peipei Jiang & Shiqiu Zhang, 2023. "Preparation of Red Iron by Magnetization Roasting-Hydrothermal Method Using Ultra-Low-Grade Limonite," Sustainability, MDPI, vol. 15(6), pages 1-13, March.
    14. Xuan Yanni & Yue Qiang, 2016. "Retrospective and Prospective Analysis on the Trends of China’s Steel Production," Journal of Systems Science and Information, De Gruyter, vol. 4(4), pages 291-306, August.
    15. Wu, Jinxi & Yang, Jie & Ma, Linwei & Li, Zheng & Shen, Xuesi, 2016. "A system analysis of the development strategy of iron ore in China," Resources Policy, Elsevier, vol. 48(C), pages 32-40.
    16. Xuan, Yanni & Yue, Qiang, 2016. "Forecast of steel demand and the availability of depreciated steel scrap in China," Resources, Conservation & Recycling, Elsevier, vol. 109(C), pages 1-12.
    17. Hossein Kamalzadeh & Saeid Nassim Sobhan & Azam Boskabadi & Mohsen Hatami & Amin Gharehyakheh, 2019. "Modeling and Prediction of Iran's Steel Consumption Based on Economic Activity Using Support Vector Machines," Papers 1912.02373, arXiv.org.
    18. Zhang, Kai & Yin, Kedong & Yang, Wendong, 2022. "Predicting bioenergy power generation structure using a newly developed grey compositional data model: A case study in China," Renewable Energy, Elsevier, vol. 198(C), pages 695-711.
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    More about this item

    Keywords

    Iron ore import and consumption; Grey prediction; Particle swarm optimization; Rolling mechanism; China;
    All these keywords.

    JEL classification:

    • F17 - International Economics - - Trade - - - Trade Forecasting and Simulation

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