A novel deep learning carbon price short-term prediction model with dual-stage attention mechanism

•First utilization of a complete deep learning framework to make short-term predictions of carbon price time series data.•Replace the feature engineering work of traditional machine learning with dual-stage attention mechanisms.•Decompose the price series using CEEMDAN and reduce the dimensionality of the input series with PCA.•The influence of model hyperparameters on prediction results is discussed.