Carbon price forecasting: a novel deep learning approach.

Abstract

Carbon emission trading market promotes carbon emission reduction effectively. Accurate carbon price forecasting is crucial for relevant policy makers and investors. However, due to the non-linearity, uncertainty, and complexity of carbon prices, the current predication models fail to predict carbon prices accurately. In this paper, an advanced deep neural network model named TCN-Seq2Seq is proposed to forecast carbon prices. The novelty of the proposed model focuses on the "sequence to sequence" layout to learn temporal data dependencies using only fully convolutional layers. Being provided with parallel training for fewer parameters, TCN-Seq2Seq forecasting model is more suitable for small carbon price dataset in few-shot learning way. Qualitatively and quantitatively, we find that the proposed framework consistently and significantly outperforms traditional statistical forecasting models and state-of-the-art deep learning prediction model with respect to predictive ability and robustness. Particularly, our proposed model achieves forecasting accuracy with the highest DA value (0.9697), the lowest MAPE value (0.0027), and the lowest RMSE value (0.0149), showing superior prediction performance compared with the traditional statistical forecasting models. The accuracy of carbon price forecasting gives insight to policy makers and carbon market investors.