A novel interval-valued carbon price analysis and forecasting system based on multi-objective ensemble strategy for carbon trading market

Abstract

Accurate forecasting of carbon price is crucial for the efficient management and stable operation of carbon markets. Earlier studies are limited to point and interval forecasts based on single-valued carbon price and lack analysis and forecasting based on interval-valued carbon price. Therefore, this study proposes a novel analysis and forecasting system from a new perspective of interval-valued carbon price. Specifically, a carbon price analysis sub-system is developed to investigate the directional causal relationship between the upper and lower bounds of the interval-valued carbon price series. The carbon price forecasting sub-system is developed by designing a data preprocessing module, sub-model forecasting module, and multi-objective ensemble module. The data preprocessing module adopts the decomposition algorithm to preprocess the interval-valued carbon price. Then the sub-model forecasting module utilizes multiple neural network models to predict the highest and lowest prices. Finally, the multi-objective ensemble module adopts a non-linear and multi-objective ensemble strategy to ensemble the forecasting results of the sub-models. It can be found that the consideration of both upper and lower bounds of interval-valued carbon price within the range leads to higher prediction accuracy for the highest or lowest price predictions. Additionally, the ensemble model can effectively leverage the strengths of individual sub-models, resulting in more precise and stable predictions. The average absolute percentage errors for the highest and lowest price predictions in the Hubei and Guangzhou carbon trading markets are 0.8574%, 1.2738%, 0.9774%, and 1.8217% respectively, vividly demonstrating the effectiveness of the proposed system in carbon price prediction.