Predicting the changes in the WTI crude oil price dynamics using machine learning models
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DOI: 10.1016/j.resourpol.2022.102664
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- Xu, Kunliang & Wang, Weiqing, 2023. "Limited information limits accuracy: Whether ensemble empirical mode decomposition improves crude oil spot price prediction?," International Review of Financial Analysis, Elsevier, vol. 87(C).
- Zhang, Jiahao & Chen, Xiaodan & Wei, Yu & Bai, Lan, 2023. "Does the connectedness among fossil energy returns matter for renewable energy stock returns? Fresh insights from the Cross-Quantilogram analysis," International Review of Financial Analysis, Elsevier, vol. 88(C).
- Chen, Fu & Tiwari, Sunil & Mohammed, Kamel Si & Huo, Weidong & Jamróz, Paweł, 2023. "Minerals resource rent responses to economic performance, greener energy, and environmental policy in China: Combination of ML and ANN outputs," Resources Policy, Elsevier, vol. 81(C).
- Liang, Xuedong & Luo, Peng & Li, Xiaoyan & Wang, Xia & Shu, Lingli, 2023. "Crude oil price prediction using deep reinforcement learning," Resources Policy, Elsevier, vol. 81(C).
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- Chuandi Fang & Jinhua Cheng & Zhe You & Jiahao Chen & Jing Peng, 2023. "A Detailed Examination of China’s Clean Energy Mineral Consumption: Footprints, Trends, and Drivers," Sustainability, MDPI, vol. 15(23), pages 1-26, November.
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- Jiang, Lan & Jiang, Hua, 2023. "Analysis of predictions considering mineral prices, residential energy, and environmental risk: Evidence from the USA in COP 26 perspective," Resources Policy, Elsevier, vol. 82(C).
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Keywords
Energy economics; Machine learning models; Crude oil price;All these keywords.
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