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A hybrid model for multi-step coal price forecasting using decomposition technique and deep learning algorithms

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  1. Huang, Wenyang & Zhao, Jianyu & Wang, Xiaokang, 2024. "Model-driven multimodal LSTM-CNN for unbiased structural forecasting of European Union allowances open-high-low-close price," Energy Economics, Elsevier, vol. 132(C).
  2. Ehsani, Behdad & Pineau, Pierre-Olivier & Charlin, Laurent, 2024. "Price forecasting in the Ontario electricity market via TriConvGRU hybrid model: Univariate vs. multivariate frameworks," Applied Energy, Elsevier, vol. 359(C).
  3. Junhao Wu & Yuan Hu & Daqing Wu & Zhengyong Yang, 2022. "An Aquatic Product Price Forecast Model Using VMD-IBES-LSTM Hybrid Approach," Agriculture, MDPI, vol. 12(8), pages 1-26, August.
  4. He, Zhichao & Huang, Jianhua, 2023. "A novel non-ferrous metal price hybrid forecasting model based on data preprocessing and error correction," Resources Policy, Elsevier, vol. 86(PB).
  5. Wang, Kai & Gong, Haoran & Wang, Gongda & Yang, Xin & Xue, Haiteng & Du, Feng & Wang, Zhie, 2024. "N2 injection to enhance gas drainage in low-permeability coal seam: A field test and the application of deep learning algorithms," Energy, Elsevier, vol. 290(C).
  6. 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).
  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. Cai, Xiaotong & Yuan, Bo & Wu, Chao, 2025. "An integrated CEEMDAN and TCN-LSTM deep learning framework for forecasting," International Review of Financial Analysis, Elsevier, vol. 98(C).
  9. Junjie Liu & Lang Liu, 2024. "Point and Interval Forecasting of Coal Price Adopting a Novel Decomposition Integration Model," Energies, MDPI, vol. 17(16), pages 1-17, August.
  10. 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.
  11. 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).
  12. Wu, Siping & Liu, Junjie & Liu, Lang, 2024. "Interval price predictions for coal using a new multi-scale ensemble model," Energy, Elsevier, vol. 313(C).
  13. Wu, Siping & Xia, Guilin & Liu, Lang, 2023. "A novel decomposition integration model for power coal price forecasting," Resources Policy, Elsevier, vol. 80(C).
  14. Zhang, Kefei & Yang, Xiaolin & Xu, Liang & Thé, Jesse & Tan, Zhongchao & Yu, Hesheng, 2024. "Enhancing coal-gangue object detection using GAN-based data augmentation strategy with dual attention mechanism," Energy, Elsevier, vol. 287(C).
  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. Chao Zhang & Yihang Zhao & Huiru Zhao, 2022. "A Novel Hybrid Price Prediction Model for Multimodal Carbon Emission Trading Market Based on CEEMDAN Algorithm and Window-Based XGBoost Approach," Mathematics, MDPI, vol. 10(21), pages 1-16, November.
  17. 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).
  18. Shi, Tao & Li, Chongyang & Zhang, Wei & Zhang, Yi, 2023. "Forecasting on metal resource spot settlement price: New evidence from the machine learning model," Resources Policy, Elsevier, vol. 81(C).
  19. Zhang, Yan & Xu, Yushi & Zhu, Xintong & Huang, Jionghao, 2024. "Coal price shock propagation through sectoral financial interconnectedness in China's stock market: Quantile coherency network modelling and shock decomposition analysis," Journal of Commodity Markets, Elsevier, vol. 34(C).
  20. Yang, Xiaolin & Zhang, Kefei & Ni, Chao & Cao, Hua & Thé, Jesse & Xie, Guangyuan & Tan, Zhongchao & Yu, Hesheng, 2022. "Ash determination of coal flotation concentrate by analyzing froth image using a novel hybrid model based on deep learning algorithms and attention mechanism," Energy, Elsevier, vol. 260(C).
  21. 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.
  22. Zhou, Feite & Huang, Zhehao & Zhang, Changhong, 2022. "Carbon price forecasting based on CEEMDAN and LSTM," Applied Energy, Elsevier, vol. 311(C).
  23. Guan, Keqin & Gong, Xu, 2023. "A new hybrid deep learning model for monthly oil prices forecasting," Energy Economics, Elsevier, vol. 128(C).
  24. Wu, Junhao & Dong, Jinghan & Wang, Zhaocai & Hu, Yuan & Dou, Wanting, 2023. "A novel hybrid model based on deep learning and error correction for crude oil futures prices forecast," Resources Policy, Elsevier, vol. 83(C).
  25. Jesús Molina‐Muñoz & Andrés Mora‐Valencia & Javier Perote, 2024. "Predicting carbon and oil price returns using hybrid models based on machine and deep learning," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 31(2), June.
  26. 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).
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