Year: 2025
Type: article
Abstract: 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.
Authors: Donglin Chen, Chen Shi
Institution: Wuhan University of Technology
Language: English
Field: Decision Sciences
Domain: Social Sciences
SDG: Climate action
Open Access status: hybrid
OpenAlex ID: W4410907870