炭素取引価格の予測精度向上：時間周波数分解とハイブリッド注意機構を組み合わせた新モデル

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🔬研究者:炭素価格予測における時系列分解と注意機構の組み合わせが予測精度をどう改善するかを示す実証例。

🏢実務担当者:炭素価格トレンド予測を内部炭素価格設定や排出枠購入・グリーン投資のタイミング判断に活用できる。

🏛政策担当者:価格予測精度の向上は割当調整や市場安定化策のタイミング設計に資する。

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.

• semanticscholar https://doi.org/10.1155/int/6531515first seen 2026-09-12 05:20:21 · last seen 2026-09-21 04:56:46