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Multistep-ahead forecasting of coal prices using a hybrid deep learning model

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  1. Erdinc Akyildirim & Oguzhan Cepni & Shaen Corbet & Gazi Salah Uddin, 2023. "Forecasting mid-price movement of Bitcoin futures using machine learning," Annals of Operations Research, Springer, vol. 330(1), pages 553-584, November.
  2. Ferrari, Davide & Ravazzolo, Francesco & Vespignani, Joaquin, 2021. "Forecasting energy commodity prices: A large global dataset sparse approach," Energy Economics, Elsevier, vol. 98(C).
  3. Du, Pei & Guo, Ju’e & Sun, Shaolong & Wang, Shouyang & Wu, Jing, 2021. "Multi-step metal prices forecasting based on a data preprocessing method and an optimized extreme learning machine by marine predators algorithm," Resources Policy, Elsevier, vol. 74(C).
  4. Lin, Yu & Liao, Qidong & Lin, Zixiao & Tan, Bin & Yu, Yuanyuan, 2022. "A novel hybrid model integrating modified ensemble empirical mode decomposition and LSTM neural network for multi-step precious metal prices prediction," Resources Policy, Elsevier, vol. 78(C).
  5. Bangzhu Zhu & Chunzhuo Wan & Ping Wang & Julien Chevallier, 2025. "Interval Forecasting of Carbon Price With a Novel Hybrid Multiscale Decomposition and Bootstrap Approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(2), pages 376-390, March.
  6. Zhou, Na & Wu, Qiaosheng & Hu, Xiangping & Xu, Deyi & Wang, Xiaolin, 2020. "Evaluation of Chinese natural gas investment along the Belt and Road Initiative using super slacks-based measurement of efficiency method," Resources Policy, Elsevier, vol. 67(C).
  7. Bingzi Jin & Xiaojie Xu, 2025. "Forecasts of coking coal futures price indices through Gaussian process regressions," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 38(1), pages 203-217, March.
  8. Gustavo Carvalho Santos & Flavio Barboza & Antônio Cláudio Paschoarelli Veiga & Mateus Ferreira Silva, 2021. "Forecasting Brazilian Ethanol Spot Prices Using LSTM," Energies, MDPI, vol. 14(23), pages 1-15, November.
  9. Xiaojie Xu & Yun Zhang, 2023. "Steel price index forecasting through neural networks: the composite index, long products, flat products, and rolled products," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 36(4), pages 563-582, December.
  10. Liu, Qing & Liu, Min & Zhou, Hanlu & Yan, Feng, 2022. "A multi-model fusion based non-ferrous metal price forecasting," Resources Policy, Elsevier, vol. 77(C).
  11. Li, Ranran, 2023. "Forecasting energy spot prices: A multiscale clustering recognition approach," Resources Policy, Elsevier, vol. 81(C).
  12. Xu, Mengjie & Li, Xiang & Li, Qianwen & Sun, Chuanwang, 2024. "LNBi-GRU model for coal price prediction and pattern recognition analysis," Applied Energy, Elsevier, vol. 365(C).
  13. Wu, Siping & Liu, Junjie & Liu, Lang, 2024. "Interval price predictions for coal using a new multi-scale ensemble model," Energy, Elsevier, vol. 313(C).
  14. Houjian Li & Xinya Huang & Deheng Zhou & Andi Cao & Mengying Su & Yufeng Wang & Lili Guo, 2022. "Forecasting Carbon Price in China: A Multimodel Comparison," IJERPH, MDPI, vol. 19(10), pages 1-16, May.
  15. Bingzi Jin & Xiaojie Xu, 2025. "Machine learning price index forecasts of flat steel products," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 38(1), pages 97-117, March.
  16. Xiaojie Xu & Yun Zhang, 2023. "Coking coal futures price index forecasting with the neural network," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 36(2), pages 349-359, June.
  17. Jiang, Ping & Liu, Zhenkun & Wang, Jianzhou & Zhang, Lifang, 2021. "Decomposition-selection-ensemble forecasting system for energy futures price forecasting based on multi-objective version of chaos game optimization algorithm," Resources Policy, Elsevier, vol. 73(C).
  18. Lahmiri, Salim, 2024. "Fossil energy market price prediction by using machine learning with optimal hyper-parameters: A comparative study," Resources Policy, Elsevier, vol. 92(C).
  19. Wang, Xiaoyu & Wang, Jiaojiao & Wang, Wenhuan & Zhang, Shuquan, 2023. "International and Chinese energy markets: Dynamic spillover effects," Energy, Elsevier, vol. 282(C).
  20. Ding, Lili & Zhao, Zhongchao & Han, Meng, 2021. "Probability density forecasts for steam coal prices in China: The role of high-frequency factors," Energy, Elsevier, vol. 220(C).
  21. Guangyong Zhang & Lixin Tian & Min Fu & Bingyue Wan & Wenbin Zhang, 2020. "Research on the Transmission Ability of China’s Thermal Coal Price Information Based on Directed Limited Penetrable Interdependent Network," Sustainability, MDPI, vol. 12(18), pages 1-23, September.
  22. Sabarathinam Srinivasan & Suresh Kumarasamy & Zacharias E. Andreadakis & Pedro G. Lind, 2023. "Artificial Intelligence and Mathematical Models of Power Grids Driven by Renewable Energy Sources: A Survey," Energies, MDPI, vol. 16(14), pages 1-56, July.
  23. Zhang, Kefei & Cao, Hua & Thé, Jesse & Yu, Hesheng, 2022. "A hybrid model for multi-step coal price forecasting using decomposition technique and deep learning algorithms," Applied Energy, Elsevier, vol. 306(PA).
  24. Wang, Di & Zhang, Zhiyuan & Yang, Xiaodi & Zhang, Yanfang & Li, Yuman & Zhao, Yueying, 2021. "Multi-scenario simulation on the impact of China's electricity bidding policy based on complex networks model," Energy Policy, Elsevier, vol. 158(C).
  25. Yujing Liu & Ruoyun Du & Dongxiao Niu, 2022. "Forecast of Coal Demand in Shanxi Province Based on GA—LSSVM under Multiple Scenarios," Energies, MDPI, vol. 15(17), pages 1-16, September.
  26. Yang, Kailing & Zhang, Xi & Luo, Haojia & Hou, Xianping & Lin, Yu & Wu, Jingyu & Yu, Liang, 2024. "Predicting energy prices based on a novel hybrid machine learning: Comprehensive study of multi-step price forecasting," Energy, Elsevier, vol. 298(C).
  27. Zhao, Jue & Hosseini, Shahab & Chen, Qinyang & Jahed Armaghani, Danial, 2023. "Super learner ensemble model: A novel approach for predicting monthly copper price in future," Resources Policy, Elsevier, vol. 85(PB).
  28. Ghallabi, Fahmi & Souissi, Bilel & Du, Anna Min & Ali, Shoaib, 2025. "ESG stock markets and clean energy prices prediction: Insights from advanced machine learning," International Review of Financial Analysis, Elsevier, vol. 97(C).
  29. Wang, Yalin & Xie, Wufei & Liu, Chenliang & Luo, Jiang & Qiu, Zhifeng & Deconinck, Geert, 2024. "Forecast of coal consumption in salt lake enterprises based on temporal gated recurrent unit network with squeeze-and-excitation attention," Energy, Elsevier, vol. 299(C).
  30. Wu, Siping & Xia, Guilin & Liu, Lang, 2023. "A novel decomposition integration model for power coal price forecasting," Resources Policy, Elsevier, vol. 80(C).
  31. Taicir Mezghani & Mouna Boujelbène Abbes, 2023. "Forecast the Role of GCC Financial Stress on Oil Market and GCC Financial Markets Using Convolutional Neural Networks," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 30(3), pages 505-530, September.
  32. Shao, Qihui & Du, Yongqiang & Xue, Wenxuan & Yang, Zhiyuan & Jia, Zhenxin & Shao, Xianzhu & Xu, Xue & Duan, Hongbo & Zhu, Zhipeng, 2024. "Predicting China's thermal coal price: Does multivariate decomposition-integrated forecasting model with window rolling work?," Resources Policy, Elsevier, vol. 99(C).
  33. Hachmi Ben Ameur & Sahbi Boubaker & Zied Ftiti & Wael Louhichi & Kais Tissaoui, 2024. "Forecasting commodity prices: empirical evidence using deep learning tools," Annals of Operations Research, Springer, vol. 339(1), pages 349-367, August.
  34. Khoshalan, Hasel Amini & Shakeri, Jamshid & Najmoddini, Iraj & Asadizadeh, Mostafa, 2021. "Forecasting copper price by application of robust artificial intelligence techniques," Resources Policy, Elsevier, vol. 73(C).
  35. Nabavi, Zohre & Mirzehi, Mohammad & Dehghani, Hesam, 2024. "Reliable novel hybrid extreme gradient boosting for forecasting copper prices using meta-heuristic algorithms: A thirty-year analysis," Resources Policy, Elsevier, vol. 90(C).
  36. Shucheng Lin & Yue Wang & Haocheng Wei & Xiaoyi Wang & Zhong Wang, 2025. "Hybrid Method for Oil Price Prediction Based on Feature Selection and XGBOOST-LSTM," Energies, MDPI, vol. 18(9), pages 1-27, April.
  37. Hao, Jun & Feng, Qianqian & Yuan, Jiaxin & Sun, Xiaolei & Li, Jianping, 2022. "A dynamic ensemble learning with multi-objective optimization for oil prices prediction," Resources Policy, Elsevier, vol. 79(C).
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