Forecasting Carbon Price Trends With Image‐Based Convolutional Neural Networks
画像ベースの畳み込みニューラルネットワークによる炭素価格トレンド予測 (AI 翻訳)
Xiaohang Ren, Zihui Sima, Farhad Taghizadeh‐Hesary, Kun Duan
🤖 gxceed AI 要約
日本語
EUAチャート画像を用いたCNNモデルで炭素価格のトレンドを予測。次日の価格方向とn日累積トレンドを予測し、従来の時系列モデルより優れた性能を示した。中国炭素市場でも有効性を確認。
English
This paper proposes a CNN model using EUA chart images to predict carbon price trends. It converts price and volume data into pixel images for next-day and n-day cumulative predictions. The model outperforms traditional machine learning models and shows robustness in China's carbon market.
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
日本の研究者が参加し、中国市場での検証も行っているが、手法はEU ETSに基づく。日本市場への直接応用には追加検討が必要だが、炭素価格予測の新たな手法として参考になる。
In the global GX context
This study contributes to carbon price forecasting using deep learning on visual data, applicable to various carbon markets. It offers a novel tool for market participants and enhances understanding of price dynamics in emissions trading systems.
👥 読者別の含意
🔬研究者:GX researchers can explore the application of image-based deep learning in carbon pricing and extend the methodology to other markets.
🏢実務担当者:Carbon traders and market analysts can use this model for short-term price trend predictions to inform trading strategies.
🏛政策担当者:Regulators can leverage the model to monitor market efficiency and detect anomalies in carbon price movements.
📄 Abstract（原文）
ABSTRACT This paper proposes a convolutional neural network (CNN) model that utilizes EUA chart images to predict carbon price trends. An imaging approach is adopted to convert EUA price and trading volume data into pixel images across four different time horizons as model inputs, enabling predictions for both the next‐day price direction and the n ‐day cumulative trend. Results demonstrate that the image‐based CNN model achieves superior performance across various prediction metrics and time horizons, outperforming all traditional machine learning models reliant on time‐series data. Furthermore, our forecasting approach exhibits robustness within China's carbon market. This methodology provides carbon market participants with an effective predictive tool, contributing to the market's healthy operation.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1002/for.70175first seen 2026-05-21 04:54:47
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gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。