Liquid Neural Network Models for Natural Gas Spot Price Time-Series Forecasting

TL;DR

This paper investigates the use of Liquid Neural Networks for short-term natural gas price forecasting, aiming to improve accuracy in volatile, nonstationary market conditions.

Contribution

It introduces LNNs as adaptive models capable of handling the dynamic and regime-changing nature of natural gas prices.

Findings

LNNs outperform traditional models in volatile market scenarios.

LNNs adapt effectively to regime changes in price dynamics.

Forecast accuracy is improved with LNNs in nonstationary conditions.

Abstract

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.