Carbon Price Prediction of LSTM Method Based on Attention Mechanism

With the proposal of double carbon target, carbon market has gradually become the focus of people’s attention, and accurate prediction of carbon price can help people better understand the dynamics of carbon market and allocate carbon emission quota reasonably. This paper carries out relevant research based on attention mechanism and long short-term memory (LSTM) network, and proposes a carbon price prediction model based on attention mechanism to improve LSTM method. This model can distinguish the importance of information, and in this way, important features in time series data can be obtained for prediction to improve the accuracy. Then, this paper takes the carbon price data of European Energy Exchange as an example to verify the performance of the model, and compares prediction results of the proposed model with those of other common forecasting models. The results show that the carbon price prediction model proposed in this paper has better fitting effect.