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

arXiv: https://arxiv.org/abs/2604.24788 · HTML: https://mlsanity.com/paper/2604.24788/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 27 references that are in the map.
- First posted on arXiv 24 Apr 2026.

## Builds on (most cited first, 3 of 3)

- [Markovian RNN: An Adaptive Time Series Prediction Network with HMM-based Switching for Nonstationary Environments](https://mlsanity.com/paper/2006.10119/) (2020)
- [Sliding Window Regression based Short-Term Load Forecasting of a Multi-Area Power System](https://mlsanity.com/paper/1905.08111/) (2019)
- [A Hybrid PCA-PR-Seq2Seq-Adam-LSTM Framework for Time-Series Power Outage Prediction](https://mlsanity.com/paper/2509.16743/) (2025)

## Abstract (arXiv)

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.