To Enhance the Prediction Accuracy of Carbon Trading Price: A Novel Model Combining Time–Frequency Decomposition and Hybrid Attention Mechanism

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.