Forecasting carbon price trends based on an interpretable light gradient boosting machine and Bayesian optimization

Abstract

The future carbon price is crucial to relevant companies, investors, and carbon policymakers, and the significance of carbon price prediction research is self-evident. However, existing study usually predicts actual carbon prices, rarely considering price trends and lacking reasonable interpretations for the prediction model. Thus, in this study, an interpretable machine learning model is proposed to predict carbon price trends. It integrates five methods, including the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), two-stage feature selection (TFS), light gradient boosting machine (LightGBM) optimized by Bayesian optimization algorithm (BOA), and SHapley Additive exPlanations (SHAP). The effectiveness of the proposed model is validated with the carbon prices of the Hubei carbon trading market, which has the largest volume among Chinese markets. The experimental results showed that the proposed model outperforms other benchmark models under five evaluation criteria, including AUC, Accuracy, Precision, Recall, and F1 score, on multiple-step predictions. For one-step-ahead prediction, the average hit ratio results are 0.8342, 77.32 %, 77.87 %, 76.83 %, and 76.88 % respectively; for five-step-ahead prediction, the average hit ratio results are 0.7641, 69.25 %, 71.17 %, 71.97 %, and 71.00 % respectively; and for ten-step-ahead prediction, the average hit ratio results are 0.7519, 69.11 %, 73.80 %, 69.61 %, and 71.16 % respectively. The SHAP model interpretation results indicated that the high-frequency intrinsic mode function (IMF) components of the historical carbon price are the most important features for predicting carbon price trends. This study contributes by forecasting both the upward and downward trends of carbon prices through multi-step-ahead forecasting with the LightGBM model and further interpreting the model’s predictions with the SHAP approach. Therefore, the proposed model has excellent forecasting performance with interpretability, which is an effective tool for forecasting carbon price trends.