JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2025, Vol. 60 ›› Issue (9): 121-132.doi: 10.6040/j.issn.1671-9352.0.2025.058

Previous Articles    

Multi-factor iron ore futures price prediction based on VMD-DBO-BiGRU

LIU Fuguo1,2, LIU Yuanmeng3, SHI Yufeng3,4*, TIAN Maozai1   

  1. 1. School of Statistics and Data Science, Xinjiang University of Finance and Economics, Urumqi 830012, Xinjiang, China;
    2. School of Mathematics and Data Science, Changji College, Changji 831100, Xinjiang, China;
    3. Institute for Financial Studies, Shandong University, Jinan 250100, Shandong, China;
    4. School of Mathematics, Shandong University, Jinan 250100, Shandong, China
  • Published:2025-09-10

Abstract: By integrating variational mode decomposition(VMD), the dung beetle optimization(DBO)and the bidirectional gated recurrent unit(BiGRU), a price prediction model(VMD-DBO-BiGRU)is proposed, aiming to enhance iron ore futures prediction accuracy. First, VMD is employed to decompose the iron ore futures price series into different modal components, extracting multi-scale price features while suppressing noise. Then, DBO is utilized to optimize the hyperparameters of the BiGRU model, mitigating the risk of local optimum associated with traditional optimization methods. Finally, the optimized BiGRU model is applied to predict each decomposed component, and the final futures price prediction is obtained by linear reconstruction of the predicted results. Experimental findings demonstrate that the proposed model significantly improves prediction accuracy in both single-step and multi-step forecasting, consistently outperforming benchmark models. Moreover, this model provides robust technical support for hedging and investment decision-making in enterprises such as a steel plant in Shandong, contributing to risk mitigation and improved decision efficiency.

Key words: iron ore futures price, variational mode decomposition, dung beetle optimization algorithm, bidirectional gated recurrent unit

CLC Number: 

  • O211
[1] JIA Lijun, XU Ruoyu, WU Jian, et al. Impacts of geopolitical risk and economic policy uncertainty on metal futures price volatility: evidence from China[J]. Resources Policy, 2023, 87:104328.
[2] PUSTOV A, MALANICHEV A, KHOBOTOLOV I. Long-term iron ore price modeling: marginal costs vs. incentive price[J]. Resources Policy, 2013, 38(4):558-567.
[3] PAN Weixu, LIU Shiqian, KUMRAL M, et al. Iron ore price forecast based on a multi-echelontandem learning model[J]. Natural Resources Research, 2024, 33(5):1969-1992.
[4] 李林泰,崔巍. 铁矿石价格波动的因果推断: 影响因素与效应分析[J]. 技术经济,2024,43(8):36-45. LI Lintai, CUI Wei. Causal inference of iron ore price fluctuations: analysis of influencing factors and effects[J]. Technological Economics, 2024, 43(8):36-45.
[5] 廖正卿. 铁矿石价格影响因素及价格预测[J]. 环渤海经济瞭望,2020,34(4):51-52. LIAO Zhengqing. Influencing factors and price forecasting of iron ore[J]. Economic Outlook of the Bohai Economic Rim, 2020, 34(4):51-52.
[6] 周慧勤,陈婷,何建军. 铁矿石价格影响因素及价格预测研究[J]. 中国物价,2018,6:55-57. ZHOU Huiqin, CHEN Ting, HE Jianjun. Research on influencing factors and price forecasting of iron ore[J]. China Price, 2018, 6:55-57.
[7] 王维. 我国进口铁矿石价格影响因素分析[J]. 中国物价,2017,2:58-61. WANG Wei. Analysis of influencing factors of imported iron ore prices in China[J]. China Price, 2017, 2:58-61.
[8] 邵留国,许自花,张仕璟. 新市场格局下铁矿石价格影响因素研究[J]. 管理评论,2018,30(2):13-24,41. SHAO Liuguo, XU Zihua, ZHANG Shijing. Research on influencing factors of iron ore prices under the new market pattern[J]. Management Review, 2018, 30(2):13-24,41.
[9] 王忠康,顾晓薇,胥孝川,等. 基于加权移动平均法的铁矿石价格预测[J]. 中国矿业,2017,26(7):31-34. WANG Zhongkang, GU Xiaowei, XU Xiaochuan, et al. Iron ore price forecasting based on weighted moving average method[J]. China Mining, 2017, 26(7):31-34.
[10] 许可,刘静怡. 基于VAR-TARCH模型的铁矿石期货价格发现功能实证研究[J]. 中国证券期货,2020,3:21-31. XU Ke, LIU Jingyi. Empiricalstudy on the price discovery function of iron ore futures prices based on VAR-TARCH model[J]. China Securities and Futures, 2020, 3:21-31.
[11] 斯燕,陈艺. 基于LSTM神经网络模型的铁矿石期货市场实证研究[J]. 中国集体经济,2024(2):100-103. SI Yan, CHEN Yi. Empirical study on iron ore futures market based on LSTM neural network model[J]. China Collective Economy, 2024(2):100-103.
[12] 施焕伟. 铁矿石期货价格预测及其套期保值策略研究[D]. 北京:中国石油大学(北京),2021. SHI Huanwei. Research on iron ore futures price forecasting and hedging strategy[D]. Beijing: China University of Petroleum(Beijing), 2021.
[13] 沈欣宜,李旭,沈虹. 基于机器学习的铜期货价格预测分析[J]. 扬州大学学报(自然科学版),2021,24(5):1-7. SHEN Xinyi, LI Xu, SHEN Hong. Forecasting analysis of copper futures prices based on machine learning[J]. Journal of Yangzhou University(Natural Science Edition), 2021, 24(5):1-7.
[14] 邓龙鑫. 基于混沌时间序列和机器学习的铁矿石价格预测模型研究[D]. 长沙:中南大学,2022. DENG Longxin. Research on iron ore price forecasting model based on chaos time series and machine learning[D]. Changsha: Central South University, 2022.
[15] 廖婧文. 基于VMD-LSTM-ELMAN模型的国际原油价格人工智能预测研究[J]. 成都理工大学学报(自然科学版),2024,51(1):164-180. LIAO Jingwen. Research on artificial intelligence forecasting of international crude oil prices based on VMD-LSTM-ELMAN model[J]. Journal of Chengdu University of Technology(Natural Science Edition), 2024, 51(1):164-180.
[16] 王瑞,王雨宁,逯静. 基于双分解和深度学习的短期风电功率预测[J/OL]. 武汉大学学报(工学版).(2024-10-08)[2025-04-11]. htttp://kns.cnki.net/kcms/detail/42.1675.T.20241008.1139.004.html. WANG Rui, WANG Yuning, LU Jing. Short-term wind power prediction based on double decomposition and deep learning[J/OL]. Journal of Wuhan University(Engineering Edition).(2024-10-08)[2025-04-11]. http://kns.cnki.net/kcms/detail/42.1675.T.20241008.1139.004.html.
[17] 邹婕,李路. 基于随机森林的SA-BiGRU模型的股票价格预测研究[J]. 中国物价,2023,11:52-56. ZOU Jie, LI Lu. Research on stock price forecasting based on random forest and SA-BiGRU model[J]. China Price, 2023, 11:52-56.
[18] DRAGOMIRETSKIY K, ZOSSO D. Variational mode decomposition[J]. IEEE Transactions on Signalprocessing, 2013, 62(3):531-544.
[19] XUE Jiankai, SHEN Bo. Dung beetle optimizer: a new meta-heuristic algorithm for global optimization[J]. The Journal of Supercomputing, 2023, 79(7):7305-7336.
[20] SHEPARD D. A two-dimensional interpolation function for irregularly-spaced data[C] //Proceedings of the 1968 23rd ACM National Conference. New York: ACM, 1968:517-524.
[1] Pingtao LYU,Caishi WANG,Jijun ZHAO. A class of two-state quantum walk based on Hadamard walk with the same eigenvalues and continuous spectrum [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2024, 59(8): 84-93.
[2] Yufeng GAO,Decheng FENG. Upcrossing and downcrossing inequalities for conditional demimartingales and conditional N-demimartingales [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2024, 59(6): 122-126.
[3] Yaru ZHANG,Li XIA,Dianqiu ZHANG. Perpetual American lookback option pricing under mixed bi-fractional Brownian motion [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2024, 59(4): 98-107.
[4] Xiaodong YAN. Strategic limit theory and strategic statistical learning [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2024, 59(1): 1-10, 45.
[5] Chenghao XU,Kaiyong WANG. The ruin probability of a two-dimensional discrete-time risk model [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2023, 58(11): 27-34, 52.
[6] Peng YANG,Xiaoyan ZHANG. Optimal reinsurance contract design based on Stackelberg game [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2023, 58(11): 1-14,26.
[7] Shimiao ZHANG,Yan LYU. Parameter estimation for competitive Lotka-Volterra model with Lévy noise [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2023, 58(10): 24-31.
[8] Jiaxin DING,Yongfeng GUO,Lina MI. Transition behavior of underdamped periodic potential system driven by Gaussian noise and Lévy noise [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2023, 58(8): 111-117.
[9] Qi GAO,Hongshuai DAI,Yanhua WU. MPEWMA chart for detecting tandem queuing network [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2023, 58(8): 104-110, 117.
[10] Yejun CHEN,Huisheng DING. θ-almost periodic solutions for stochastic differential equations in infinite dimensions with Stepanov almost periodic coefficients [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2023, 58(6): 113-126.
[11] ZHOU Yu-lan, LI Zhuan, LI Xiao-hui. Properties of modified stochastic gradient operators in continuous-time Guichardet-Fock space [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2018, 53(12): 62-68.
[12] LI Yong-ming, NIE Cai-ling, LIU Chao, GUO Jian-hua. Consistency of estimator of nonparametric regression function for arrays of rowwise NSD [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2018, 53(12): 69-74.
[13] CHEN Hao-jun, ZHENG Ying, MA Ming, BIAN Li-na, LIU Hua. Covariance of self-exciting filtered Poisson process [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2018, 53(12): 75-79.
[14] LI Xiao-juan, GAO Qiang. Regularity for product space under sublinear expectation framework [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2018, 53(4): 66-75.
[15] MA Ming, BIAN Li-na, LIU Hua. Low order moments of self-excited filtered Poisson processes based on joint distribution of event points [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2018, 53(4): 55-58.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!