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1. Forecasting crude oil prices: A Gated Recurrent Unit-based nonlinear Granger Causality model. (2025). Zhang, Dayong ; Lu, Quanying ; Guo, Mengzhuo ; Lin, Qingyuan ; Liang, Qian.
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2. A hybrid model based on iTransformer for risk warning of crude oil price fluctuations. (2025). Guo, Yuwei ; Li, Jinchao.
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3. Predicting carbon and oil price returns using hybrid models based on machine and deep learning. (2024). Perote, Javier ; Molinamuoz, Jesus ; Moravalencia, Andres.
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4. Effective Crude Oil Prediction Using CHS-EMD Decomposition and PS-RNN Model. (2024). Patil, Renuka ; Swasthika, T J ; Chaithanya, B N ; Chellin, George J ; Ruby, Usha A.
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5. Oil market regulatory: An ensembled model for prediction. (2024). Liu, Yancheng ; Gu, Xiang ; Fan, Kun ; Chen, Haixin.
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6. A robust time-varying weight combined model for crude oil price forecasting. (2024). Xu, Yan ; Zhou, Suyu ; Jie, Qian ; Du, Pei ; Liu, Longlong ; Wang, Jianzhou.
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7. A novel secondary decomposition method for forecasting crude oil price with twitter sentiment. (2024). Guo, Yuanxuan ; Qian, Shuangyue ; Tang, Ling ; Li, Ling ; Wu, Jun.
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8. A novel hybrid model with two-layer multivariate decomposition for crude oil price forecasting. (2024). Zhao, Zhengling ; Wang, Shouyang ; Sun, Shaolong.
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9. Hybrid framework combining grey system model with Gaussian process and STL for CO2 emissions forecasting in developed countries. (2024). Ma, Xin ; An, Hongyu.
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10. Fortify the investment performance of crude oil market by integrating sentiment analysis and an interval-based trading strategy. (2024). Cheng, Zishu ; Wei, Yunjie ; Wang, Shouyang ; Li, Mingchen ; Yang, Kun.
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12. An analysis of crude oil prices in the last decade (2011-2020): With deep learning approach. (2023). Choudhury, Karabi Dutta ; Datta, Tapan Kumar ; Sen, Abhibasu.
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13. Do Gas Price and Uncertainty Indices Forecast Crude Oil Prices? Fresh Evidence Through XGBoost Modeling. (2023). Zaghdoudi, Taha ; Nsaibi, Mariem ; Tissaoui, Kais ; Hakimi, Abdelaziz.
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14. A Supply Chain-Oriented Model to Predict Crude Oil Import Prices in South Korea Based on the Hybrid Approach. (2023). Kim, Umji ; Lee, Juhyang ; Jo, Jisung.
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15. A new hybrid model for multi-step WTI futures price forecasting based on self-attention mechanism and spatial–temporal graph neural network. (2023). Xue, Minggao ; Cheng, LI ; Zhao, Geya.
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16. Limited information limits accuracy: Whether ensemble empirical mode decomposition improves crude oil spot price prediction?. (2023). Wang, Weiqing ; Xu, Kunliang.
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18. Prophesying the Short-Term Dynamics of the Crude Oil Future Price by Adopting the Survival of the Fittest Principle of Improved Grey Optimization and Extreme Learning Machine. (2022). Kumar, Sachin ; Mallick, Pradeep Kumar ; Zymbler, Mikhail ; Das, Kaberi ; El-Sayed, Hesham ; Mishra, Debahuti.
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19. Do EEMD based decomposition-ensemble models indeed improve prediction for crude oil futures prices?. (2022). Xu, Kunliang ; Niu, Hongli.
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22. A new secondary decomposition-reconstruction-ensemble approach for crude oil price forecasting. (2022). Sun, Shaolong ; Zhao, Panpan.
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23. Forecasting crude oil market returns: Enhanced moving average technical indicators. (2022). Zhang, Yaojie ; Wang, Yudong ; Liu, LI ; Wen, Danyan.
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25. A novel crude oil price trend prediction method: Machine learning classification algorithm based on multi-modal data features. (2022). Li, Xiuming ; Sun, Mei ; Mensah, Isaac Adjei ; He, Huizi.
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26. Oil price, US stock market and the US business conditions in the era of COVID-19 pandemic outbreak. (2022). Managi, Shunsuke ; ben Zaied, Younes ; ben Mabrouk, Nejah ; Yousfi, Mohamed ; ben Lahouel, Bechir.
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27. Robust Multi-Step Predictor for Electricity Markets with Real-Time Pricing. (2021). Vyatkin, Valeriy ; Sierla, Seppo ; Kahawala, Sachin ; Nawaratne, Rashmika ; Osipov, Evgeny ; de Silva, Daswin ; Alahakoon, Damminda ; Jennings, Andrew.
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28. Formulating the Concept of an Investment Strategy Adaptable to Changes in the Market Situation. (2021). Ivanyuk, Vera.
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29. Decomposition-selection-ensemble forecasting system for energy futures price forecasting based on multi-objective version of chaos game optimization algorithm. (2021). Liu, Zhenkun ; Jiang, Ping ; Zhang, Lifang ; Wang, Jianzhou.
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30. Data-driven based machine learning models for predicting the deliverability of underground natural gas storage in salt caverns. (2021). Ali, Aliyuda.
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31. Neural network prediction of crude oil futures using B-splines. (2021). Shang, Han Lin ; Butler, Sunil ; Kokoszka, Piotr ; Miao, Hong.
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32. A novel method for online real-time forecasting of crude oil price. (2021). Zhao, Yuan ; Gong, Xue ; Zhang, Weiguo ; Wang, Chao.
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33. Predicting risk in energy markets: Low-frequency data still matter. (2021). Výrost, Tomáš ; Lyócsa, Štefan ; Vrost, Toma ; Lyocsa, Tefan ; Todorova, Neda.
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34. Forecasting Crude Oil Price Using Event Extraction. (2021). Huang, Xiaohong ; Liu, Jiangwei.
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