Liquid Neural Network Models for Natural Gas Spot Price Time-Series Forecasting
Liquid Neural Network Models for Natural Gas Spot Price Time-Series Forecasting
用于天然气现货价格时间序列预测的液态神经网络模型
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.
摘要： 天然气无疑是全球能源体系中不可或缺的组成部分。由于季节性需求模式、地缘政治发展以及宏观经济状况的变化，天然气价格表现出显著的波动性，这使得对其进行准确的短期预测极具挑战性。
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.
非线性动力学和频繁的机制转换可能会限制传统时间序列模型的有效性。在本研究中，我们探索了利用液态神经网络（LNNs）对亨利港（Henry Hub）现货价格进行短期预测的应用，该价格是天然气定价的主要基准。
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.
液态神经网络（LNNs）旨在通过动态内部状态更新，持续适应不断演变的时间模式，使其非常适合处理非平稳的价格行为。通过提高波动市场条件下的预测准确性，本研究旨在降低不确定性，并为能源交易和电力市场应用提供更强的决策支持。
Paper Details:
- Authors: Yiqian Liu, Jiayi Niu, Adam Kelleher, Subhabrata Das
- Submission Date: 24 Apr 2026
- Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
- DOI: 10.48550/arXiv.2604.24788
论文详情：
- 作者： Yiqian Liu, Jiayi Niu, Adam Kelleher, Subhabrata Das
- 提交日期： 2026年4月24日
- 学科分类： 机器学习 (cs.LG)；人工智能 (cs.AI)
- 数字对象标识符 (DOI)： 10.48550/arXiv.2604.24788