Carbon price prediction based on multi-factor MEEMD-LSTM model

Abstract

China’s national carbon market has already become the largest carbon market in the world. The prediction of carbon price is extremely important for policymakers and market participants. Therefore, the prediction of carbon price in China is of great significance. To achieve a better prediction effect, a multi-factor hybrid model combined with modified ensemble empirical mode decomposition (MEEMD) and long short-term memory (LSTM) neural network optimized by machine reasoning system on the basis of production rules is proposed in this paper. In addition to historical carbon price, other factors, such as energy, macroeconomy, environmental condition, temperature, exchange rate which affect carbon price fluctuation, are formed as multi-factor. The change characteristics of carbon price time series data and other associated factors are extracted in the carbon price prediction. The MEEMD is used to decompose data which is taken as potential input variables into LSTM neural network for prediction and the machine reasoning system based on production rules can automatically search and optimize the parameters of LSTM to further improve the prediction results. The experimental results demonstrate that the proposed method has better prediction effect, robustness and adaptability than the LSTM model without MEEMD decomposition, the single factor MEEMD-LSTM method and other benchmark models. Overall it seems that the proposed method is an advanced approach for predicting the non-stationary and non-linear carbon price time series.