Research on Carbon Asset Price Prediction Algorithm Based on Time Series Analysis

One of the most important market mechanisms for fostering sustainable development and lowering carbon emissions is carbon trading. A more efficient carbon market and more effective environmental policies depend on reliable carbon price forecasts. However, end consequences and the disorderly character of price for carbon sequences have prevented significant advances in the validity of these predictions. Using the EPO-CCPLSTM time series analysis technique, this study introduces a novel model for carbon price prediction. With EPO-CPPLSTM configured to predict carbon price series components, carbon prices are estimated by combining the outputs of the LSTM components. According to the findings of the empirical study, the suggested model achieves better prediction accuracy than the comparative models. The proposed model has provided an accuracy of 99.5% and 95.6% respectively. Carbon market trading operations will become more efficient with the model's implementation, which will also promote clean development across a number of industries.