Carbon Price Prediction With Public Social Media Big Data and an Interpretable Multi‐Objective Intelligent Feature Optimization Strategy

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:This paper provides a novel feature optimization method with interpretability for carbon price forecasting, which can be extended to other environmental markets.

🏢実務担当者:Carbon trading teams can use social media sentiment as a leading indicator for price movements to optimize trading strategies.

🏛政策担当者:Regulators overseeing carbon markets may consider incorporating alternative data sources to monitor market dynamics and prevent manipulation.

炭素価格の正確な予測は、炭素市場における資源配分の最適化と企業の排出削減意思決定の導出に不可欠である。しかし、炭素価格は多様な要因に影響され、従来の予測手法ではこれらの要因間の複雑な相互関係を考慮できないことが多く、効果的な特徴量の抽出が困難となり、予測精度が低下する。これらの課題に対処するため、本研究は改良型多目的セクレタリーバード最適化アルゴリズムに基づく解釈可能なインテリジェント特徴最適化戦略を革新的に開発する。この戦略は、離散最適化と双方向伝播技術を導入することで特徴マスキングの問題に効果的に対処し、有用な特徴量の精密な識別と定量化を可能にする。埋め込み型の解釈可能メカニズムの革新的な開発は、特徴最適化プロセスにおいて炭素価格予測のための透明かつ解釈可能な基盤を提供できる。さらに、本研究は投資家のセンチメントと関心に応答する公開ソーシャルメディアビッグデータを炭素価格予測に組み込む初期の試みであり、その独自のリアルタイム性と双方向性は社会の動態を鋭敏に捉え、炭素価格予測の即時性をさらに最適化できる。実証研究により、提案された特徴最適化戦略とマルチモーダル公開ソーシャルメディアビッグデータの導入が、予測の精度と頑健性を有意に向上させることが示されている。本研究は炭素価格予測の方法論的革新を提供し、投資家が取引意思決定を最適化するための貴重な参考となる。

AI 翻訳（deepseek-v4-flash）。 正確を期す場合は下の原文を参照してください。

Accurate forecasts of carbon prices are essential for optimizing resource allocation in the carbon market and guiding corporate emissions reduction decisions. However, carbon prices are influenced by a variety of factors, and traditional forecasting methods often fail to account for the complex interrelationships among these factors, making it difficult to extract effective features and reduce the forecasting accuracy. To address these challenges, this study innovatively develops an interpretable intelligent feature optimization strategy based on an improved multi‐objective secretary bird optimization algorithm. This strategy effectively addresses the issue of feature masking by introducing the discrete optimization and bidirectional propagation technique, thereby enabling the precise identification and quantification of useful features. The innovative development of embedded interpretable mechanisms can provide a transparent and interpretable basis for carbon price forecasting in the feature optimization process. Furthermore, this study is the early attempt to incorporate public social media big data, which responds to investor sentiment and attention, into carbon price forecasts, whose unique real‐time and interactive nature can keenly capture social dynamics, further optimizing the timeliness of carbon price prediction. Empirical studies show the proposed feature optimization strategy and the introduction of multimodal public social media big data can significantly improve the precision and robustness of the prediction. The study offers methodological innovations for carbon price forecasting and serves as a valuable reference for investors to optimize their trading decisions.

• semanticscholar https://doi.org/10.1002/for.70108first seen 2026-05-15 17:20:32 · last seen 2026-08-12 05:16:14