A novel crude oil price trend prediction method: Machine learning classification algorithm based on multi-modal data features
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DOI: 10.1016/j.energy.2021.122706
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- Wang, Xiufeng & Jiang, Yiyun & Gu, Manyi, 2024. "Exploring the interplay: Crude oil futures, economic shocks, and China's resources," Resources Policy, Elsevier, vol. 91(C).
- Amar Rao & Marco Tedeschi & Kamel Si Mohammed & Umer Shahzad, 2024. "Role of Economic Policy Uncertainty in Energy Commodities Prices Forecasting: Evidence from a Hybrid Deep Learning Approach," Computational Economics, Springer;Society for Computational Economics, vol. 64(6), pages 3295-3315, December.
- Jia, Miaoyin & Lu, Gan & Yan, Youliang & Nazir, Sidra, 2024. "Resilience through mineral resource development, oil, and natural resource efficiency: Strengthening economies," Resources Policy, Elsevier, vol. 91(C).
- Mohsin, Muhammad & Jamaani, Fouad, 2023. "Green finance and the socio-politico-economic factors’ impact on the future oil prices: Evidence from machine learning," Resources Policy, Elsevier, vol. 85(PA).
- Jisung Jo & Umji Kim & Eonkyung Lee & Juhyang Lee & Sewon Kim, 2023. "A Supply Chain-Oriented Model to Predict Crude Oil Import Prices in South Korea Based on the Hybrid Approach," Sustainability, MDPI, vol. 15(24), pages 1-18, December.
- Zhang, Wen & Wu, Zhibin & Zeng, Xiaojun & Zhu, Changhui, 2023. "An ensemble dynamic self-learning model for multiscale carbon price forecasting," Energy, Elsevier, vol. 263(PC).
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Keywords
Crude oil price prediction; Multi-modal data features; Machine learning; Classification algorithm; Variational mode decomposition;All these keywords.
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