Forecasting Carbon Price Trends With Image-Based Convolutional Neural Networks

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.