A Novel Forecasting Framework for Carbon Emission Trading Price Based on Nonlinear Integration

The complex features of carbon price, such as volatility and nonlinearity, pose a serious challenge to accurately predict it. To this end, this paper proposes a novel forecasting framework for carbon emission trading price based on nonlinear integration, including feature selection, deep learning and model combination. Firstly, the historical carbon price series are collected and collated, and the factors affecting the carbon price are analyzed. Secondly, the data are downscaled and the input variables are screened using the max-relevance and min-redundancy. Then, the three integrated learning models are combined with the neural network model through nonlinear integration to construct a hybrid prediction model, and the best performing combined model is obtained. Finally, interval prediction is realized on the basis of point prediction. The experimental results show that the prediction model outperforms other comparative models in terms of prediction accuracy, stability and statistical hypothesis testing, and has good prediction performance. In summary, the hybrid prediction model proposed in this paper can not only provide high-precision carbon market price prediction for government and enterprise decision makers, but also help investors optimize their trading strategies and improve their returns.