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Long-term forecast of energy commodities price using machine learning

Citations

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Cited by:

  1. Drachal, Krzysztof, 2021. "Forecasting selected energy commodities prices with Bayesian dynamic finite mixtures," Energy Economics, Elsevier, vol. 99(C).
  2. Kuntadi, Cris, 2022. "Effective energy commodity risk management on Indonesia," Resources Policy, Elsevier, vol. 78(C).
  3. Feng, Zongbao & Wu, Xianguo & Chen, Hongyu & Qin, Yawei & Zhang, Limao & Skibniewski, Miroslaw J., 2022. "An energy performance contracting parameter optimization method based on the response surface method: A case study of a metro in China," Energy, Elsevier, vol. 248(C).
  4. Rao, Amar & Sharma, Gagan Deep & Tiwari, Aviral Kumar & Hossain, Mohammad Razib & Dev, Dhairya, 2025. "Crude oil Price forecasting: Leveraging machine learning for global economic stability," Technological Forecasting and Social Change, Elsevier, vol. 216(C).
  5. 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.
  6. Haithem Awijen & Hachmi Ben Ameur & Zied Ftiti & Waël Louhichi, 2025. "Forecasting oil price in times of crisis: a new evidence from machine learning versus deep learning models," Annals of Operations Research, Springer, vol. 345(2), pages 979-1002, February.
  7. Saleh Abushamah, Hussein Abdulkareem & Skoda, Radek, 2022. "Nuclear energy for district cooling systems – Novel approach and its eco-environmental assessment method," Energy, Elsevier, vol. 250(C).
  8. Shu Tang & Dongphil Chun & Xuhui Liu, 2025. "An Applied Study on Predicting Natural Gas Prices Using Mixed Models," Energies, MDPI, vol. 18(19), pages 1-22, October.
  9. Liu, Yanchu & Zhou, Heyang & Yang, Haisheng, 2025. "Latent factor models for the Chinese commodity futures markets," Pacific-Basin Finance Journal, Elsevier, vol. 93(C).
  10. Wen, Danyan & He, Mengxi & Wang, Yudong & Zhang, Yaojie, 2025. "Forecasting gasoline prices using oil prices: New evidence based on the rocket and feather hypothesis," Energy, Elsevier, vol. 335(C).
  11. Liu, Jinpei & Qiu, Biyue & Du, Pengcheng & Zhao, Xiaoman & Zhu, Jiaming, 2025. "A novel probabilistic connectivity network link prediction model for natural gas price based on an improved K-shell algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 671(C).
  12. 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).
  13. Varshini, Anu & Kayal, Parthajit & Maiti, Moinak, 2024. "How good are different machine and deep learning models in forecasting the future price of metals? Full sample versus sub-sample," Resources Policy, Elsevier, vol. 92(C).
  14. Yue Ma & Ling Miao & Lianyong Feng & Ruirui Fang, 2026. "The ripple effects of international energy prices on domestic products in China under external shocks," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 28(3), pages 7129-7145, March.
  15. Fernandes, Leonardo H.S. & de Araujo, Fernando H.A. & Silva, José W.L. & Tabak, Benjamin Miranda, 2022. "Booms in commodities price: Assessing disorder and similarity over economic cycles," Resources Policy, Elsevier, vol. 79(C).
  16. Zhao, Shuchun & Guo, Junheng & Dang, Xiuhu & Ai, Bingyan & Zhang, Minqing & Li, Wei & Zhang, Jinli, 2022. "Energy consumption, flow characteristics and energy-efficient design of cup-shape blade stirred tank reactors: Computational fluid dynamics and artificial neural network investigation," Energy, Elsevier, vol. 240(C).
  17. Wu, Siping & Liu, Junjie & Liu, Lang, 2024. "Interval price predictions for coal using a new multi-scale ensemble model," Energy, Elsevier, vol. 313(C).
  18. Molina-Muñoz, Jesús & Mora-Valencia, Andrés & Perote, Javier, 2025. "Dynamic volatility spillovers among commodities, bitcoin, and emerging markets," Emerging Markets Review, Elsevier, vol. 69(C).
  19. Xu, Aiting & Chen, Jiapeng & Li, Jinchang & Chen, Zheyu & Xu, Shenyi & Nie, Ying, 2025. "Multivariate rolling decomposition hybrid learning paradigm for power load forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 212(C).
  20. Wu, Siping & Xia, Guilin & Liu, Lang, 2023. "A novel decomposition integration model for power coal price forecasting," Resources Policy, Elsevier, vol. 80(C).
  21. Tostes, Bernardo & Henriques, Sofia T. & Brockway, Paul E. & Heun, Matthew Kuperus & Domingos, Tiago & Sousa, Tânia, 2024. "On the right track? Energy use, carbon emissions, and intensities of world rail transportation, 1840–2020," Applied Energy, Elsevier, vol. 367(C).
  22. Hajirahimi, Zahra & Khashei, Mehdi & Etemadi, Sepideh, 2022. "A novel class of reliability-based parallel hybridization (RPH) models for time series forecasting," Chaos, Solitons & Fractals, Elsevier, vol. 156(C).
  23. Abdollahi, Hooman & Ebrahimi, Seyed Babak, 2020. "A new hybrid model for forecasting Brent crude oil price," Energy, Elsevier, vol. 200(C).
  24. Foued Hamouda & Nadia Arfaoui & Muhammad Abubakr Naeem, 2025. "Forecasting Energy Commodity Prices Amidst Worldwide Energy Transitions Using Artificial Intelligence Models," The Energy Journal, , vol. 46(5), pages 215-244, September.
  25. Bingzi Jin & Xiaojie Xu, 2026. "Machine learning wholesale white wheat price index forecasts," Quality & Quantity: International Journal of Methodology, Springer, vol. 60(1), pages 277-305, February.
  26. Dimitrios Mouchtaris & Emmanouil Sofianos & Periklis Gogas & Theophilos Papadimitriou, 2021. "Forecasting Natural Gas Spot Prices with Machine Learning," Energies, MDPI, vol. 14(18), pages 1-13, September.
  27. 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).
  28. Alameer, Zakaria & Fathalla, Ahmed & Li, Kenli & Ye, Haiwang & Jianhua, Zhang, 2020. "Multistep-ahead forecasting of coal prices using a hybrid deep learning model," Resources Policy, Elsevier, vol. 65(C).
  29. Manickavasagam, Jeevananthan & Visalakshmi, S. & Apergis, Nicholas, 2020. "A novel hybrid approach to forecast crude oil futures using intraday data," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
  30. Olubusoye, Olusanya E & Akintande, Olalekan J. & Yaya, OlaOluwa S. & Ogbonna, Ahamuefula & Adenikinju, Adeola F., 2021. "Energy Pricing during the COVID-19 Pandemic: Predictive Information-Based Uncertainty Indexes with Machine Learning Algorithm," MPRA Paper 109838, University Library of Munich, Germany.
  31. Wang, Jun & Cao, Junxing & Yuan, Shan & Cheng, Ming, 2021. "Short-term forecasting of natural gas prices by using a novel hybrid method based on a combination of the CEEMDAN-SE-and the PSO-ALS-optimized GRU network," Energy, Elsevier, vol. 233(C).
  32. 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.
  33. Ge, Lei & Huang, Qiwei & Zhu, Fengshuang & Chen, Shun, 2025. "Advanced time series forecasting for commodities: Insights from the FEDformer model," Energy Economics, Elsevier, vol. 147(C).
  34. Ahmad, Tanveer & Zhang, Dongdong & Huang, Chao, 2021. "Methodological framework for short-and medium-term energy, solar and wind power forecasting with stochastic-based machine learning approach to monetary and energy policy applications," Energy, Elsevier, vol. 231(C).
  35. Kais Tissaoui & Taha Zaghdoudi & Abdelaziz Hakimi & Mariem Nsaibi, 2023. "Do Gas Price and Uncertainty Indices Forecast Crude Oil Prices? Fresh Evidence Through XGBoost Modeling," Computational Economics, Springer;Society for Computational Economics, vol. 62(2), pages 663-687, August.
  36. Wang, Hanjie & Maruejols, Lucie & Yu, Xiaohua, 2021. "Predicting energy poverty with combinations of remote-sensing and socioeconomic survey data in India: Evidence from machine learning," Energy Economics, Elsevier, vol. 102(C).
  37. Farooq Ahmad & Livio Finos & Mariangela Guidolin, 2024. "Forecasting Hydropower with Innovation Diffusion Models: A Cross-Country Analysis," Forecasting, MDPI, vol. 6(4), pages 1-20, November.
  38. Zhang, Xiaokong & Chai, Jian & Tian, Lingyue & Yang, Ying & Zhang, Zhe George & Pan, Yue, 2023. "Forecast and structural characteristics of China's oil product consumption embedded in bottom-line thinking," Energy, Elsevier, vol. 278(PA).
  39. Zhang, Tingting & Tang, Zhenpeng & Wu, Junchuan & Du, Xiaoxu & Chen, Kaijie, 2021. "Multi-step-ahead crude oil price forecasting based on two-layer decomposition technique and extreme learning machine optimized by the particle swarm optimization algorithm," Energy, Elsevier, vol. 229(C).
  40. Yang, Zhaiting & Liu, Huiqin & Jiang, Youwei & Zhang, Zhuyun, 2023. "Innovative strategies for green economic recovery: Enhancing efficiency in resource markets," Resources Policy, Elsevier, vol. 86(PB).
  41. Jonathan Berrisch & Florian Ziel, 2020. "Distributional Modeling and Forecasting of Natural Gas Prices," Papers 2010.06227, arXiv.org, revised Aug 2021.
  42. Abdollahi, Hooman, 2020. "A novel hybrid model for forecasting crude oil price based on time series decomposition," Applied Energy, Elsevier, vol. 267(C).
  43. Zadeh, Omid Razavi & Romagnoli, Silvia, 2024. "Financing sustainable energy transition with algorithmic energy tokens," Energy Economics, Elsevier, vol. 132(C).
  44. Hamida, Amal Ben & de Peretti, Christian & Belkacem, Lotfi, 2026. "Benford’s law and intraday microstructure anomalies: Forecasting market movements with high-frequency data," Research in International Business and Finance, Elsevier, vol. 84(C).
  45. Ansari Saleh Ahmar & Zulkifli Rais & Faika Tunnas, 2025. "Comparative analysis of support vector regression (SVR) and SutteARIMA in predicting coal prices in Indonesia," Quality & Quantity: International Journal of Methodology, Springer, vol. 59(5), pages 4185-4200, October.
  46. Qin Lu & Jingwen Liao & Kechi Chen & Yanhui Liang & Yu Lin, 2024. "Predicting Natural Gas Prices Based on a Novel Hybrid Model with Variational Mode Decomposition," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 639-678, February.
  47. Xie, Gang & Jiang, Fuxin & Zhang, Chengyuan, 2023. "A secondary decomposition-ensemble methodology for forecasting natural gas prices using multisource data," Resources Policy, Elsevier, vol. 85(PA).
  48. Lin, Yu & Dai, Dongsheng & Yu, Yuanyuan & Li, Zhaofeng & Huang, Wenhui & Zhao, Liangkai & Xing, Haiyang, 2025. "Forecasting natural gas prices using a novel hybrid model: Comparative study of different sliding windows," Energy, Elsevier, vol. 329(C).
  49. Tiwari, Aviral Kumar & Sharma, Gagan Deep & Rao, Amar & Hossain, Mohammad Razib & Dev, Dhairya, 2024. "Unraveling the crystal ball: Machine learning models for crude oil and natural gas volatility forecasting," Energy Economics, Elsevier, vol. 134(C).
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