TFR: A Temporal Feature-Refined Multi-Stage Carbon Price Forecasting

Abstract

Accurate carbon price forecasting is crucial for effective carbon market analysis and decision-making. We propose a novel Temporal Feature-Refined (TFR) model to address the challenges of complex dependencies and high noise levels in carbon price time series data. The TFR model integrates Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) for signal decomposition, an Autoencoder for feature optimization, and a Temporal Convolutional Network (TCN) for capturing long-range temporal dependencies. It incorporates both traditional economic factors and unconventional determinants such as air quality, policy uncertainty, and public sentiment. Experiments on the Shanghai carbon trading market demonstrate that the TFR model significantly outperforms existing methods, achieving an 83.96% improvement in MAE over Support Vector Regression (SVR) and up to a 65.56% improvement over Long Short-Term Memory (LSTM) networks. Further analyses, including comparisons with different decomposition models and ablation studies, confirm the effectiveness of each component and the overall model.