Liquid Neural Network Models for Natural Gas Spot Price Time-Series Forecasting
For energy traders and power market analysts, this work offers a more adaptive forecasting method for nonstationary price behavior, though it is an incremental application of existing LNNs to a new domain.
This study applies Liquid Neural Networks (LNNs) to short-horizon forecasting of Henry Hub natural gas spot prices, demonstrating improved accuracy over traditional models in volatile market conditions.
Natural gas is undoubtedly an essential component of the global energy system. Accurate short-term forecasting of natural gas price is challenging due to pronounced volatility driven by seasonal demand patterns, geopolitical developments, and shifting macroeconomic conditions. The nonlinear dynamics and frequent regime changes can limit the effectiveness of traditional time-series models. In this study, we explore the use of Liquid Neural Networks (LNNs) for short-horizon forecasting of the Henry Hub spot price, a primary benchmark for pricing. LNNs are designed to adapt continuously to evolving temporal patterns through dynamic internal state updates, making them well suited for nonstationary price behavior. By improving forecast accuracy in volatile market conditions, this work aims to reduce uncertainty and enhance decision support across energy trading and power market applications.