Open AccessA Hybrid PLO-Transformer-CEEMDAN Model for Natural Gas Price Forecasting
Author(s) -
Xiang Wang,
Hao Wang,
Zirong Wang,
Di Wu,
Yuan Xue,
Lingxiao Ye,
Yucong Zhang
Publication year - 2025
Publication title -
ieee access
Language(s) - English
Resource type - Magazines
SCImago Journal Rank - 0.587
H-Index - 127
eISSN - 2169-3536
DOI - 10.1109/access.2025.3597657
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
To address the nonlinear and non-stationary nature of natural gas prices, this study proposes a hybrid PLO-Transformer-CEEMDAN forecasting framework. The model integrates weight optimization, trend extraction, and residual correction, leveraging 27 heterogeneous features—including Google Trends—to enhance predictive performance. Empirical tests on 2004–2024 Henry Hub monthly data demonstrate that the model achieves an RMSE of 0.0257 and an R ² of 0.9998, reducing RMSE by 92.87% compared to the baseline Transformer. Notably, incorporating Google Trends alone improved accuracy by 30.91%, highlighting its significant value in capturing market sentiment ahead of price movements. The proposed method significantly outperforms conventional benchmarks in both accuracy and generalization, providing a robust tool for energy price forecasting and market risk management.
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