MS-IHHO-LSTM: Carbon Price Prediction Model of Multi-Source Data Based on Improved Swarm Intelligence Algorithm and Deep Learning Method

Abstract

Accurate carbon price prediction can help save energy and reduce emissions worldwide. Thus, this paper proposes a model that combines swarm intelligence algorithms with deep learning to predict carbon prices. In this model, we collect news related to carbon trading, construct a dictionary of carbon financial sentiment, and determine the emotional value of the carbon news. Secondly, The Harris Hawks Optimization (HHO) algorithm is improved by updating the escape energy and introducing the inertia weight. Then, the LSTM is optimized using the improved Harris Hawks Optimization (IHHO) algorithm. Finally, technical and emotional data on carbon price as multiple source input values are integrated, and the MS-IHHO-LSTM prediction model is established. The results show that the MAPE of IHHO-LSTM is 1.89%, 30.48%, and 10.30% better than that of HHO-LSTM in Hubei, Shanghai, and Shenzhen Carbon Exchanges, respectively. Similarly, MS-IHHO-LSTM showed a lower MAPE than IHHO-LSTM by 27.79%, 29.82%, and 6.33% in the corresponding regions. The results of the experiment indicate that: 1) Using IHHO to optimize LSTM hyperparameters can avoid falling into local optimal and improve prediction accuracy; 2) Incorporating emotional values can further enhance the model’s performance. The MS-IHHO-LSTM prediction model facilitates low-carbon investment, technological innovation, and green production, enabling enterprises to support environmental sustainability.

Keywords

• Carbon price forecasting
• sentiment analysis
• deep learning
• multiple source data
• MS-IHHO-LSTM