Multi-factor carbon emission rights trading price prediction based on Transformer-LSTM model
Carbon emission trading,as an important environmental policy tool,has been widely used in the world.How to use deep learning and other technologies to improve the predictive ability of carbon emission rights prices is an important issue.Based on this,this paper proposes a deep learning model for multi-factor carbon emission rights trading price prediction with Transformer-LSTM.Taking the carbon emission trading price in Hubei Province as an example,this paper aims to explore the use of deep learning methods to predict the changing trend of carbon emission trading price in Hubei Province.We input Transformer-LSTM model for prediction,and use support vector regression(SVR),multilayer perceptron(MLP),long short-term memory(LSTM)and Trans-former model for prediction and comparison.Through training on the historical data,the experimental results show that the predicted prices obtained by the Transformer-LSTM model are more consistent with the actual price of Hubei carbon emission allowance(HBEA),and perform better in terms of mean absolute error(MAE),mean square error(MSE),root mean square error(RMSE),and evaluation indicators.
carbon emission rights trading pricedeep learningTransformer-LSTMextreme gradient boostinglong short-term memory
万方数据
维普
