Recent hurricane events have caused unprecedented amounts of damage on critical infrastructure systems and have severely threatened our public safety and economic health. The most observable (and severe) impact of these hurricanes is the loss of electric power in many regions, which causes breakdowns in essential public services. Understanding power outages and how they evolve during a hurricane provides insights on how to reduce outages in the future, and how to improve the robustness of the underlying critical infrastructure systems. In this article, we propose a novel scalable segmentation with explanations framework to help experts understand such datasets. Our method, CnR (Cut-n-Reveal), first finds a segmentation of the outage sequences based on the temporal variations of the power outage failure process so as to capture major pattern changes. This temporal segmentation procedure is capable of accounting for both the spatial and temporal correlations of the underlying power outage process. We then propose a novel explanation optimization formulation to find an intuitive explanation of the segmentation such that the explanation highlights the culprit time series of the change in each segment. Through extensive experiments, we show that our method consistently outperforms competitors in multiple real datasets with ground truth. We further study real county-level power outage data from several recent hurricanes (Matthew, Harvey, Irma) and show that CnR recovers important, non-trivial, and actionable patterns for domain experts, whereas baselines typically do not give meaningful results.

中文翻译：

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Cut-n-Reveal

最近的飓风事件对关键基础设施系统造成了前所未有的破坏，严重威胁了我们的公共安全和经济健康。这些飓风最明显（也是最严重）的影响是许多地区的电力中断，导致基本公共服务中断。了解停电及其在飓风期间的演变方式，可以帮助我们了解如何减少未来的停电，以及如何提高底层关键基础设施系统的稳健性。在本文中，我们提出了一种具有解释框架的新型可扩展分割，以帮助专家理解此类数据集。我们的方法，CnR（Cut-n-Reveal），首先根据停电故障过程的时间变化找到停电序列的分段，以捕捉主要的模式变化。这种时间分割过程能够考虑潜在断电过程的空间和时间相关性。然后，我们提出了一种新颖的解释优化公式，以找到对分割的直观解释，从而使解释突出显示罪魁祸首每个段的变化的时间序列。通过广泛的实验，我们表明我们的方法在具有基本事实的多个真实数据集中始终优于竞争对手。我们进一步研究了来自最近几次飓风（Matthew、Harvey、Irma）的实际县级停电数据，并表明 CnR 为领域专家恢复了重要、重要且可操作的模式，而基线通常不会给出有意义的结果。