Carbon Trading Price Prediction Research Based on HPSO-LSTM
Donglin Chen, Shi Chen · Applied and Computational Engineering · 2025
Accurate carbon price forecasting plays a key role in promoting emission reductions and advancing the low-carbon economy. Given the strong nonlinear nature of carbon prices and the subjective challenge in tuning hyperparameters for traditional LSTM networks, this study introduces a prediction framework combining a Hybrid Particle Swarm Optimization (HPSO) algorithm with an LSTM neural network. Using China’s national carbon market data, both univariate and multivariate time series predictions are conducted. Results demonstrate that the HPSO algorithm efficiently tunes LSTM hyperparameters, enhancing performance compared to multilayer perceptron (MLP) models. Moreover, incorporating multiple variables yields superior predictive outcomes over using historical prices alone.