An adaptive multi-factor integrated forecasting model based on periodic reconstruction and random forest for carbon price

Abstract

Accurate and appropriate carbon price prediction can provide a quantitative benchmark for the decision-making of government and investors, promoting the rapid development of carbon market. However, the inherently complexity of carbon price affected by multiple external factors poses a challenge for accurate forecasting. Thus, an adaptive multi-factor integrated hybrid model based on periodic reconstruction and random forest is developed for carbon price prediction. In the model, the improved decomposition method and periodic reconstruction are introduced to fully extract and integrate the hidden laws, which realizes the efficient and accurate prediction under multiple time scales. Considering the disparities of carbon markets, a three-stage influencing factors screening framework is proposed based on random forest, achieving the adaptive prediction by using the selected external factors to modify the forecasting of carbon prices. Four representative carbon markets in China (i.e., Shanghai, Guangdong, Shenzhen, and Hubei) are employed for empirical analysis. The results reveal that carbon price can be affected by energy and financial markets in short-term fluctuations, while its long-term trends are mainly influenced by climate and policy effects. Compared with other benchmark models, the proposed adaptive model considering multiple factors is reasonable and effective to predict carbon price with different characteristics that the average MAPE and RMSE are 0.3977 and 0.5036, respectively. Therefore, the proposed model not only provides a reliable tool for carbon price prediction, but also provides a unique perspective for governments and investors to explore the multi-time scale influencing factors of carbon price variations, which helps stakeholders understand the market rules and make appropriate decisions.