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<p>Coal is a vital energy resource and chemical raw material. The price of coal is highly susceptible to fluctuations due to various complex and uncertain factors like supply-demand relationships and the macroeconomic environment. To accurately obtain coal price information, this paper proposes a novel multi-feature-aware gated residual deep network (MFA-GRDN) model to address the dynamic and multifaceted nature of coal price forecasting. The model integrates a gated residual network (GRN) for feature interaction modeling, a variable selection network (VSN) for adaptive factor weighting, and a gated recurrent neural network (GRNN) for temporal dependency learning, enabling dynamic factor importance assessment and enhanced long-term trend capture. Extensive experiments on real-world coal price datasets from Qinhuangdao Port demonstrate that the proposed MFA-GRDN model achieves the lowest root mean square error (RMSE) and highest goodness-of-fit (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(R^2\)</EquationSource> </InlineEquation>) among all benchmark models. Specifically, compared to the best-performing baseline, MFA-GRDN reduces RMSE to 36% and improves <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(R^2\)</EquationSource> </InlineEquation> consistently across both datasets, underscoring its superior accuracy and robustness in volatile coal markets.</p>

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Exploring the long-term period trend potential for enhancing coal price forecasting with adaptive influencing factor analysis

  • 期刊论文

  • 发表日期：

    2026年2月26日

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  • Jiang Luo,
  • Yalin Wang,
  • Chenliang Liu,
  • Yishun Liu,
  • Keke Huang,
  • Weihua Gui

摘要

Coal is a vital energy resource and chemical raw material. The price of coal is highly susceptible to fluctuations due to various complex and uncertain factors like supply-demand relationships and the macroeconomic environment. To accurately obtain coal price information, this paper proposes a novel multi-feature-aware gated residual deep network (MFA-GRDN) model to address the dynamic and multifaceted nature of coal price forecasting. The model integrates a gated residual network (GRN) for feature interaction modeling, a variable selection network (VSN) for adaptive factor weighting, and a gated recurrent neural network (GRNN) for temporal dependency learning, enabling dynamic factor importance assessment and enhanced long-term trend capture. Extensive experiments on real-world coal price datasets from Qinhuangdao Port demonstrate that the proposed MFA-GRDN model achieves the lowest root mean square error (RMSE) and highest goodness-of-fit ( \(R^2\) ) among all benchmark models. Specifically, compared to the best-performing baseline, MFA-GRDN reduces RMSE to 36% and improves \(R^2\) consistently across both datasets, underscoring its superior accuracy and robustness in volatile coal markets.