A hybrid carbon price prediction model based-combinational estimation strategies of quantile regression and long short-term memory

Abstract

In recent years, global warming mitigation has received increasing attention. Reasonable carbon prices in stable carbon markets can reduce greenhouse gas emissions. Therefore, to accurately predict carbon prices and describe their fluctuations, a hybrid model was developed based on Complete Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), SampEn (SE), Long Short-Term Memory (LSTM), Quantile regression (QR), and Kernel density estimation (KDE). First, CEEMDAN was used to decompose the original carbon price series into several Intrinsic Mode Functions (IMFs), and the SE of each decomposed series was calculated to reconstruct a new series. The actual influencing factors determine the number of new series. The prediction model, named QRLSTM, combines QR and LSTM to achieve point and interval predictions. Finally, KDE was used to obtain a probabilistic prediction of the daily carbon price. Compared with other methods, the experimental results in Hubei, China, showed that the proposed CEEMDAN-SE-QRLSTM model had the best performance. For point prediction, the MSE, MAE, RMSE, MAPE, and R2 were 0.19, 0.33, 0.43, 0.01, and 91.7%, respectively. In interval prediction, the coverage width criterion (CWC) in the 95%, 90%, and 80% confidence intervals was small, with values of 0.37, 0.32, and 0.27, respectively. In probabilistic prediction, the continuous ranked probability score (CRPS) with 95% confidence was small, with a value of 0.25. In addition, the improvement in point prediction and stability of the hybrid model were also proved. The proposed model can provide more accurate results and valuable information for decision-makers.