Carbon Market Price Forecasting in China Using Probability Density Recurrent Networks

With the nationwide proliferation of carbon markets, the scarcity of carbon emissions allowances has become comparable to other commodities in traditional markets, endowing them with unique market value. Carbon prices play a central role in carbon market mechanisms, reflecting the outcomes of market competition and shaping government policies related to carbon emissions allocation. Consequently, the accurate prediction of carbon prices is essential for businesses to comprehend fluctuations in carbon prices, efficiently manage carbon emissions, and provide a sound foundation for trading decisions. In this study, we address data uncertainty by developing a probability density recurrent network. We investigate the intricate interrelationships and evolving patterns among data points using probability density distribution functions in time series data, enabling the creation of a prediction model to anticipate future data points. Multiple evaluation criteria corroborate the precision and effectiveness of the methodology employed in this research.