TDTimexer：時間周波数強調と変数間依存モデリングを統合した炭素価格予測モデル
TDTimexer: A Carbon Price Forecasting Model Integrating Time–Frequency Enhancement and Cross-Variable Dependency Modeling (原題)
Jinyang Lv, Jing Li
🤖 gxceed AI 要約
日本語
炭素価格の非線形・多スケール変動を予測するため、MICとSHAPで入力変数を選別し、周波数応答を調整するTESAモジュールと変数間依存を強化する差分クロスアテンションをTimexerに統合したCPO-TDTimexerを提案。広東・湖北・上海の炭素市場で検証し、MAE・RMSE・MAPEをそれぞれ約24%改善、DM検定で有意性を確認した。
English
The authors propose CPO-TDTimexer, a carbon price forecasting framework combining MIC/SHAP variable selection, a spectral TESA module, differential cross-attention, and crested porcupine optimizer tuning. Tested on Guangdong, Hubei, and Shanghai carbon markets, it cuts MAE, RMSE, and MAPE by roughly 22–25% over the original Timexer, with Diebold–Mariano tests confirming significance.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
炭素価格はカーボンプライシング政策やICP・GX債の価格前提に直結する。日本の排出量取引制度拡充や炭素価格シグナルの分析に関心を持つ読者に、機械学習による価格予測の手法的示唆を提供する。
In the global GX context
Accurate carbon price forecasting underpins carbon market design, transition finance pricing, and corporate carbon cost assumptions under frameworks like ISSB and TCFD. This adds an ML methodology to the global carbon-pricing analytics toolkit, though evidence is China-specific.
👥 読者別の含意
🔬研究者:炭素価格予測における時系列・周波数・変数間依存の統合手法の有効性を示す実証例として参照できる。
🏢実務担当者:炭素価格のコスト前提や内部炭素価格設定のモデリングに応用可能な予測手法の示唆を得られる。
🏛政策担当者:炭素市場の価格シグナル予測精度向上が市場安定化や政策評価に資する点を参考にできる。
📄 Abstract（原文）
Carbon price series exhibit nonlinear fluctuations, multi-scale temporal patterns, and complex dependencies among influencing variables, making accurate forecasting challenging. To tackle these challenges, we introduce CPO-TDTimexer as a framework for forecasting carbon prices. First, the maximal information coefficient (MIC) and SHapley Additive exPlanations (SHAP) are combined to identify informative input variables. Next, a Temporal Enhancement and Spectral Adaptation (TESA) module is developed to adjust frequency responses through learnable spectral weights, extract trend and seasonal components, and preserve original sequence information through residual fusion. Differential cross-attention (DCA) is then introduced into Timexer to strengthen cross-variable dependency modeling and reduce the interference of redundant information. Finally, key model hyperparameters are tuned with the crested porcupine optimizer (CPO). The framework’s effectiveness is demonstrated through experiments in the carbon markets of Guangdong, Hubei, and Shanghai. Compared with the original Timexer, CPO-TDTimexer yields average reductions of 24.48% in MAE, 22.35% in RMSE, and 25.02% in MAPE. Ablation studies further confirm the complementary contributions of TESA, DCA, and CPO. Diebold–Mariano tests support the statistical significance of the forecasting improvements achieved by CPO-TDTimexer over the benchmark models.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/app162110772first seen 2026-11-02 04:56:44 · last seen 2026-11-06 05:03:45
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