A novel carbon price prediction model based on optimized least square support vector machine combining characteristic-scale decomposition and phase space reconstruction

Citations

Cited by:

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9. Jin, Huaiping & Zhang, Kehao & Fan, Shouyuan & Jin, Huaikang & Wang, Bin, 2024. "Wind power forecasting based on ensemble deep learning with surrogate-assisted evolutionary neural architecture search and many-objective federated learning," Energy, Elsevier, vol. 308(C).
10. Yongfa Chen & Yingjie Zhu & Jie Wang & Meng Li, 2025. "A Hybrid Model for Carbon Price Forecasting Based on Secondary Decomposition and Weight Optimization," Mathematics, MDPI, vol. 13(14), pages 1-24, July.
11. Xiande, Zhang & Chonghui, Fu & Pengcheng, Xie & Yajie, Bo & Feng, Pan & Wenjun, Wang, 2025. "Carbon price prediction model based on multi-agent and environment co-evolution," Energy, Elsevier, vol. 328(C).
12. Zhou, Yilin & Wang, Jianzhou & Wang, Kang & Gao, Jialu & Li, Hongmin & Lu, Haiyan, 2025. "An interpretable analytical framework for carbon price forecasting: Combining multi-source factors and price decomposition," Energy Economics, Elsevier, vol. 152(C).
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16. Xiaolu Wei & Hongbing Ouyang, 2023. "Forecasting Carbon Price Using Double Shrinkage Methods," IJERPH, MDPI, vol. 20(2), pages 1-20, January.
17. Meixia Wang, 2024. "Predicting China’s Energy Consumption and CO 2 Emissions by Employing a Novel Grey Model," Energies, MDPI, vol. 17(21), pages 1-25, October.
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19. Beibei Hu & Yunhe Cheng, 2023. "Predicting regional carbon price in China based on multi-factor HKELM by combining secondary decomposition and ensemble learning," PLOS ONE, Public Library of Science, vol. 18(12), pages 1-24, December.
20. Niu, Xiaoqin & Yüksel, Serhat & Dinçer, Hasan, 2023. "Emission strategy selection for the circular economy-based production investments with the enhanced decision support system," Energy, Elsevier, vol. 274(C).
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