用液态神经网络预测天然气价格，提升波动市场下的预测精度。
Liquid Neural Network Models for Natural Gas Spot Price Time-Series Forecasting
- 采用液态神经网络动态适应价格变化模式。
- 在亨利港现货价格上实现比传统模型更优的短期预测效果。
- 适合能源交易与电力市场决策者参考使用。
天然气是全球能源体系的重要组成部分。由于季节性需求、地缘政治和宏观经济变动带来的显著波动，天然气价格的短期预测极具挑战性。传统时间序列模型受限于非线性动态和频繁的状态转换。本文研究液态神经网络（LNNs）在亨利港（Henry Hub）现货价格短期预测中的应用。LNNs 通过持续更新内部状态，能自适应演化的时间模式，适用于非平稳的价格行为。实验表明，该方法在高波动市场中显著提升了预测准确性，有助于降低不确定性，增强能源交易与电力市场中的决策支持能力。
原文摘要 · Abstract (English)
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.
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