A Multi-granularity Heterogeneous Ensemble Model for Point and Interval Forecasting of Carbon Prices-Reference-Cited by-同舟云学术

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A Multi-granularity Heterogeneous Ensemble Model for Point and Interval Forecasting of Carbon Prices

Published:2025-06-06 Issue:1 Volume:18 Page:
ISSN:1875-6883
Container-title:International Journal of Computational Intelligence Systems
language:en
Short-container-title:Int J Comput Intell Syst

Author:

Sha Di,Zeng Xianyi,Tran Kim-Phuc,Xia Lin,Wang Ruolin

Funder

Anhui Province young and middle-aged teachers training action project

Research Projects of Higher Education Institutions in Anhui Province

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s44196-025-00881-w.pdf

Reference70 articles.

1. Wang, J., Zhou, Y., Jiang, H.: A novel interval forecasting system based on multi-objective optimization and hybrid data reconstruct strategy. Expert Syst. Appl. (2023). https://doi.org/10.1016/j.eswa.2023.119539

2. Wang, W., Zhang, Y.J.: Does China’s carbon emissions trading scheme affect the market power of high-carbon enterprises? Energy Econ. (2022). https://doi.org/10.1016/j.eneco.2022.105906

3. Mao, S., Zeng, X.J.: SimVGNets: similarity-based visibility graph networks for carbon price forecasting. Expert Syst. Appl. (2023). https://doi.org/10.1016/j.eswa.2023.120647

4. Ding, L., Zhang, R., Zhao, X.: Forecasting carbon price in China unified carbon market using a novel hybrid method with three-stage algorithm and long short-term memory neural networks. Energy (2024). https://doi.org/10.1016/j.energy.2023.129761

5. Zhang, K., Yang, X., Wang, T., Thé, J., Tan, Z., Yu, H.: Multi-step carbon price forecasting using a hybrid model based on multivariate decomposition strategy and deep learning algorithms. J. Clean. Prod. (2023). https://doi.org/10.1016/j.jclepro.2023.136959

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