Forecast Natural Gas Price by an Extreme Learning Machine Framework Based on Multi-Strategy Grey Wolf Optimizer and Signal Decomposition

Zhuolin Wu, Jiaqi Zhou and Xiaobing Yu ()
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Zhuolin Wu: School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
Jiaqi Zhou: School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
Xiaobing Yu: School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China

Sustainability, 2025, vol. 17, issue 12, 1-37

Abstract: Natural gas is one of the most important sources of energy in modern society. However, its strong volatility highlights the importance of accurately forecasting natural gas price trends and movements. The nonlinear nature of the natural gas price series makes it difficult to capture. Therefore, we propose a forecasting framework based on signal decomposition and intelligent optimization algorithms to predict natural gas prices. In this forecasting framework, we implement point, probability interval, and quantile interval forecasting. First, the natural gas price sequence is decomposed into multiple Intrinsic Mode Functions (IMFs) using the Ensemble Empirical Mode Decomposition (EEMD) technique. Each decomposed sequence is then predicted using an optimized Extreme Learning Machine (ELM), and the individual results are aggregated as the final result. To improve the efficiency of the intelligent algorithm, a Multi-Strategy Grey Wolf Optimizer (MSGWO) is developed to optimize the hidden layer matrices of the ELM. The experimental results prove that the proposed framework not only provides more reliable point forecasts with good nonlinear adaptability but also describes the uncertainty of natural gas price series more accurately and completely.

Keywords: natural gas price; extreme learning machine; time series forecasting; signal decomposition; intelligent optimization algorithm (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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