通过优化协方差幂次，从部分观测数据中捕捉复杂系统潜在因果结构，实现早期事件预测。
The Powers of Precision: Structure-Informed Detection in Complex Systems -- From Customer Churn to Seizure Onset
- 基于协方差/精度矩阵的幂族特征表示，自适应学习系统隐含结构。
- 在癫痫发作与用户流失预测中达到竞争性准确率，验证方法有效性。
- 最优幂次具有可识别性与结构可解释性，兼顾预测与可解释性。
突发现象（如癫痫发作、用户突然流失或疫情暴发）常源于复杂系统的隐藏因果交互。本文提出一种机器学习方法，用于其早期检测，核心挑战在于：在数据生成过程未知且观测不全的情况下揭示并利用系统的潜在因果结构。该方法从一个单参数估计族——经验协方差或精度矩阵的幂次中学习最优特征表示，提供一种有原则的方式以调谐至驱动关键事件的底层结构。随后，监督学习模块对所学表示进行分类。我们证明了该族的结构一致性，并在癫痫发作检测和用户流失预测任务中展示了方法的实证有效性，结果具有竞争力。此外，为提升可解释性，我们发现最优协方差幂次表现出良好的可识别性并捕获结构特征，从而在预测性能与可解释统计结构之间取得平衡。
原文摘要 · Abstract (English)
Emergent phenomena -- onset of epileptic seizures, sudden customer churn, or pandemic outbreaks -- often arise from hidden causal interactions in complex systems. We propose a machine learning method for their early detection that addresses a core challenge: unveiling and harnessing a system's latent causal structure despite the data-generating process being unknown and partially observed. The method learns an optimal feature representation from a one-parameter family of estimators -- powers of the empirical covariance or precision matrix -- offering a principled way to tune in to the underlying structure driving the emergence of critical events. A supervised learning module then classifies the learned representation. We prove structural consistency of the family and demonstrate the empirical soundness of our approach on seizure detection and churn prediction, attaining competitive results in both. Beyond prediction, and toward explainability, we ascertain that the optimal covariance power exhibits evidence of good identifiability while capturing structural signatures, thus reconciling predictive performance with interpretable statistical structure.
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