To Enhance the Prediction Accuracy of Carbon Trading Price: A Novel Model Combining Time–Frequency Decomposition and Hybrid Attention Mechanism.
Saved in:
| Title: | To Enhance the Prediction Accuracy of Carbon Trading Price: A Novel Model Combining Time–Frequency Decomposition and Hybrid Attention Mechanism. |
|---|---|
| Authors: | Cui, Haochu1 (AUTHOR), Sun, Yan1 (AUTHOR), Cheng, Peiyue1 (AUTHOR) chengpeiyue@sdufe.edu.cn |
| Source: | International Journal of Intelligent Systems. 9/7/2026, Vol. 2026, p1-21. 21p. |
| Subjects: | Carbon pricing, Time-frequency analysis, Forecasting, Moving average process, Fast Fourier transforms, Greenhouse gas mitigation |
| Geographic Terms: | Hubei Sheng (China) |
| Abstract: | Nowadays, carbon trading policy is regarded as an effective method to control carbon emissions. Accurate prediction of carbon trading price can benefit both governments and enterprises. To enhance the accuracy of prediction, this paper proposes a novel three‐stage carbon trading price prediction model combining time–frequency decomposition and hybrid attention mechanism. In the decomposition stage, the historical price is decomposed into trend and seasonal components using multiwindow moving average and into high‐ and low‐frequency components using fast Fourier transform. In the integration stage, a linear model predicts each component, and a hybrid attention mechanism is designed to calculate their dynamic weights to combine components to obtain distinct time‐domain and frequency‐domain prediction results. The fusion stage uses a linear network to integrate these intermediate results to accomplish the final carbon trading price prediction. Empirical data from Guangdong and Hubei are deployed to verify the proposed model. Comparative experiments demonstrate the proposed model reduces MAE by 1.1%–12.9% and MSE by 10.5%–13.1% for single‐step forecasting compared to the best‐performing baseline on each dataset, while maintaining competitive performance across most multistep predictions. Under accurate carbon trading price predictions, the governments can adjust quotas early to stabilize markets. Enterprises can also optimize allowance purchases or green investments based on price trends, enhancing emission reduction. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Intelligent Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| Abstract: | Nowadays, carbon trading policy is regarded as an effective method to control carbon emissions. Accurate prediction of carbon trading price can benefit both governments and enterprises. To enhance the accuracy of prediction, this paper proposes a novel three‐stage carbon trading price prediction model combining time–frequency decomposition and hybrid attention mechanism. In the decomposition stage, the historical price is decomposed into trend and seasonal components using multiwindow moving average and into high‐ and low‐frequency components using fast Fourier transform. In the integration stage, a linear model predicts each component, and a hybrid attention mechanism is designed to calculate their dynamic weights to combine components to obtain distinct time‐domain and frequency‐domain prediction results. The fusion stage uses a linear network to integrate these intermediate results to accomplish the final carbon trading price prediction. Empirical data from Guangdong and Hubei are deployed to verify the proposed model. Comparative experiments demonstrate the proposed model reduces MAE by 1.1%–12.9% and MSE by 10.5%–13.1% for single‐step forecasting compared to the best‐performing baseline on each dataset, while maintaining competitive performance across most multistep predictions. Under accurate carbon trading price predictions, the governments can adjust quotas early to stabilize markets. Enterprises can also optimize allowance purchases or green investments based on price trends, enhancing emission reduction. [ABSTRACT FROM AUTHOR] |
|---|---|
| ISSN: | 08848173 |
| DOI: | 10.1155/int/6531515 |
