Enhanced Carbon Price Forecasting Using Extended Sliding Window Decomposition with LSTM and SVR

Accurately forecasting carbon prices plays a vital role in shaping environmental policies, guiding investment strategies, and accelerating the development of low-carbon technologies. However, traditional forecasting models often face challenges related to information leakage and boundary effects. This study proposes a novel extended sliding window decomposition (ESWD) mechanism to prevent information leakage and mitigate boundary effects, thereby enhancing decomposition quality. Additionally, a fully data-driven multivariate empirical mode decomposition (MEMD) technique is incorporated to further improve the model’s capabilities. Partial decomposition operations, combined with high-resolution and full-utilization strategies, ensure mode consistency. An empirical analysis of China’s largest carbon market, using eight key indicators from energy, macroeconomics, international markets, and climate fields, validates the proposed model’s effectiveness. Compared to traditional LSTM and SVR models, the hybrid model achieves performance improvements of 66.6% and 23.5% in RMSE for closing price prediction, and 73.8% and 10.8% for opening price prediction, respectively. Further integration of LSTM and SVR strategies enhances RMSE performance by an additional 82.7% and 8.3% for closing prices, and 30.4% and 4.5% for opening prices. The extended window setup (EW10) yields further gains, improving RMSE, MSE, and MAE by 11.5%, 35.4%, and 23.7% for closing prices, and 4.5%, 8.4%, and 4.2% for opening prices. These results underscore the significant advantages of the proposed model in enhancing carbon price prediction accuracy and trend prediction capabilities.