Natural Gas Futures Price Prediction Based on Variational Mode Decomposition–Gated Recurrent Unit/Autoencoder/Multilayer Perceptron–Random Forest Hybrid Model

Forecasting natural gas futures prices can help to promote sustainable global energy development, as the efficient use of natural gas as a clean energy source has become key to the growing global demand for sustainable development. This study proposes a new hybrid model for the prediction of natural gas futures prices. Firstly, the original price series is decomposed, and the subsequences, along with influencing factors, are used as input variables. Secondly, the input variables are grouped based on their correlations with the output variable, and different models are employed to forecast each group. A gated recurrent unit (GRU) captures the long-term dependence, an autoencoder (AE) downscales and extracts the features, and a multilayer perceptron (MLP) maps the complex relationships. Subsequently, random forest (RF) integrates the results of the different models to obtain the final prediction. The experimental results show that the model has a mean absolute error (MAE) of 0.32427, a mean absolute percentage error (MAPE) of 10.17428%, a mean squared error (MSE) of 0.46626, a root mean squared error (RMSE) of 0.68283, an R-squared (R²) of 93.10734%, and an accuracy rate (AR) of 89.82572%. The results demonstrate that the proposed decomposition–selection–prediction–integration framework reduces prediction errors, enhances the stability through multiple experiments, improves the prediction efficiency and accuracy, and provides new insights for forecasting.