A Decision-Support Approach to International Natural Gas Price Forecasting Using Machine Learning Models - Peifeng Wu; Yaqiang Chen (Decision Making: Applications in Management and Engineering, 2025)
Accurate forecasting of international natural gas prices is essential for effective decision-making within the context of a volatile energy market, as unpredictable responses to price fluctuations often arise from non-stationary behaviour. Traditional econometric approaches frequently encounter limitations in capturing market volatility, nonlinear dynamics, and structural breaks, which diminishes their practical utility for strategic planning and operational decisions. This study seeks to conduct a comparative analysis of multiple machine learning (ML) techniques, including linear regression, support vector machines, decision trees, random forests, neural networks, and ensemble methods, in forecasting international natural gas prices using datasets obtained from the International Energy Agency (IEA), Global Data, and the Bloomberg terminal. The findings indicate that more sophisticated ML models, particularly ensemble methods and neural networks, outperform conventional forecasting approaches in terms of accuracy and reliability. Furthermore, the study highlights that forecasts generated through ML can significantly enhance decision-making processes for key stakeholders in the natural gas sector, including government policymakers, investors, and oil and gas producers, by informing structured risk management, optimising resource allocation, and supporting long-term strategic planning