物理引导的反事实解释用于大规模多变量时间序列：在可扩展和可解释的SEP事件预测中的应用
Physics-Guided Counterfactual Explanations for Large-Scale Multivariate Time Series: Application in Scalable and Interpretable SEP Event Prediction
AI总结：
本文提出了一种物理引导的反事实解释框架，用于大规模多变量时间序列中的SEP事件预测，实现了更高的预测精度和更高效的运行效率。
摘要：
准确预测太阳高能粒子事件对于保护卫星、宇航员和空间基础设施至关重要。现代空间天气监测从如地球静止操作环境卫星（GOES）等来源生成大量高频多变量时间序列（MVTS）数据。在这些数据上训练的机器学习（ML）模型显示出强大的预测能力，但大多数现有方法忽视了领域特定的可行性约束。反事实解释已成为提高模型可解释性的重要工具，但现有方法很少强制物理合理性。本文介绍了一种物理引导的反事实解释框架，这是一种新的方法，用于生成在时间序列分类任务中保持与底层物理原理一致的反事实解释。应用于太阳高能粒子（SEP）预测时，该框架实现了动态时间规整（DTW）距离减少超过80%，增加了接近性，生成了更稀疏的反事实解释，并将运行时间比最先进的基线方法如DiCE减少了近50%。除了数值改进外，该框架确保生成的反事实解释在物理上是合理的，并且在科学领域中是可行的。总之，该框架生成的反事实解释既有效又符合物理原理，同时为大数据环境中的可扩展反事实生成奠定了基础。
英文摘要：
Accurate prediction of solar energetic particle events is vital for safeguarding satellites, astronauts, and space-based infrastructure. Modern space weather monitoring generates massive volumes of high-frequency, multivariate time series (MVTS) data from sources such as the Geostationary perational Environmental Satellites (GOES). Machine learning (ML) models trained on this data show strong predictive power, but most existing methods overlook domain-specific feasibility constraints. Counterfactual explanations have emerged as a key tool for improving model interpretability, yet existing approaches rarely enforce physical plausibility. This work introduces a Physics-Guided Counterfactual Explanation framework, a novel method for generating counterfactual explanations in time series classification tasks that remain consistent with underlying physical principles. Applied to solar energetic particles (SEP) forecasting, this framework achieves over 80% reduction in Dynamic Time Warping (DTW) distance increasing the proximity, produces counterfactual explanations with higher sparsity, and reduces runtime by nearly 50% compared to state-of-the-art baselines such as DiCE. Beyond numerical improvements, this framework ensures that generated counterfactual explanations are physically plausible and actionable in scientific domains. In summary, the framework generates counterfactual explanations that are both valid and physically consistent, while laying the foundation for scalable counterfactual generation in big data environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
