Carbon Price Forecasting Approach Based on Multi-Scale Decomposition and Transfer Learning

Abstract

Accurate carbon price forecasting is essential to provide the guidance for production and investment. Current research is mainly dependent on plenty of historical samples of carbon prices, which is impractical for the newly launched carbon market due to its short history. Based on the idea of transfer learning, this paper proposes a novel price forecasting model, which utilizes the correlation between the new and mature markets. The model is firstly pretrained on large data of mature market by gated recurrent unit algorithm, and then fine-tuned by the target market samples. An integral framework, including complexity decomposition method for data pre-processing, sample entropy for feature selection, and support vector regression for result post-processing, is provided. In the empirical analysis of new Chinese market, the root mean square error, mean absolute error, mean absolute percentage error, and determination coefficient of the model are 0.529, 0.476, 0.717% and 0.501 respectively, proving its validity.

Keywords

carbon emission trading / price forecasting / transfer learning / gated recurrent unit