A hybrid carbon price forecasting model combining time series clustering and data augmentation

Stable and accurate carbon price forecasts are crucial for carbon asset management and policy design. Therefore, various advanced models have been proposed to forecast carbon price, and decomposition-reconstruction models occupy a major position. On this basis, this study proposes an improved decomposition-reconstruction framework and constructs a novel hybrid forecasting model Informer-DA-BOHB-TVFEMD-CL. The hybrid model decomposes carbon price by TVFEMD to obtain different volatility characteristics and classifies these features by time series clustering, where the high-frequency components are retained and the low-frequency components are reconstructed. Then, the training set was expanded by the generative data augment technique to improve the model's generalization ability and avoid overfitting. Finally, the advanced deep learning model Informer and the hyperparameter optimization technique BOHB are used to model the training set, and the prediction results of each subcomponent are integrated to obtain the final prediction results. Experiments are conducted in EU-ETS, Guangdong, Hubei, and Chinese national carbon market. Experimental results show that the stability and prediction performance of the proposed hybrid model are better than those of the benchmark models, proving the applicability of the improved decomposition-reconstruction idea and the validity of the hybrid model. Meanwhile, the proposed model has demonstrated good profitability in carbon trading.