Prophet-LSTM combination model carbon trading price prediction research

Driven by the dual carbon goals, China's carbon trading plays an increasingly significant role in carbon emission reduction. The price of carbon trading is the core issue of carbon trading, and predicting changes in the price of carbon trading is of great significance to the long-term low carbon development of enterprises. Based on an in-depth analysis of the properties of Prophet model and Long Short-Term Memory (LSTM) neural network, a combined Prophet-LSTM model for short-term prediction of carbon trading price is proposed in this paper. Prophet and LSTM prediction models are built separately, then a combination model is built by optimizing the weight coefficients for carbon trading price prediction. The data of Hubei carbon trading market from 2019 to 2022 were used as an arithmetic example for validation. The experimental results show that the combined Prophet-LSTM forecasting model has stronger stability and higher accuracy than the standard Prophet model forecasting method, LSTM model forecasting method, ARIMA model forecasting method and SARIMAX model forecasting method in carbon trading price time series analysis. The combined forecasting method proposed in this paper can effectively improve the prediction accuracy of carbon trading price, thus helping enterprises to reasonably assess their carbon assets and control the cost of carbon in production to achieve low carbon and high quality development.