PUPAE：面向时间序列异常的直观且可执行的解释
PUPAE: Intuitive and Actionable Explanations for Time Series Anomalies
AI总结：
本文提出领域无关的反事实解释技术PUPAE，以“若非损坏B，异常本应像正常数据A”的形式生成客观正确、直观且可直接执行的时间序列异常解释。
摘要：
近年来，时间序列异常检测取得了显著进展。然而，在检测到一个（可能是初步的）异常之后，我们能否对其进行解释？此类解释将有助于对异常进行分诊。例如，在一家炼油厂中，面对一个异常，我们是应该派遣一名液压工程师，还是派一名实习生去更换传感器上的电池？已有一些并行的努力试图解释异常，但许多提出的技术产生的解释是间接的，而且往往看起来比它们试图解释的异常本身更加复杂。我们对各领域一线从业者使用的文献、检查清单和用户手册的回顾揭示了一个有趣的近乎普遍的共性。大多数从业者以如下格式讨论、解释和报告异常：该异常本应像正常数据A，若不是因为损坏B的话。读者会意识到这是一种反事实解释。在这项工作中，我们引入一种领域无关的反事实解释技术，用于为时间序列异常生成解释。正如我们将展示的，我们的方法能够生成视觉和基于文本的解释，这些解释在客观上是正确的、直观的，并且在许多情况下可以直接执行。
英文摘要：
In recent years there has been significant progress in time series anomaly detection. However, after detecting an (perhaps tentative) anomaly, can we explain it? Such explanations would be useful to triage anomalies. For example, in an oil refinery, should we respond to an anomaly by dispatching a hydraulic engineer, or an intern to replace the battery on a sensor? There have been some parallel efforts to explain anomalies, however many proposed techniques produce explanations that are indirect, and often seem more complex than the anomaly they seek to explain. Our review of the literature/checklists/user-manuals used by frontline practitioners in various domains reveals an interesting near-universal commonality. Most practitioners discuss, explain and report anomalies in the following format: The anomaly would be like normal data A, if not for the corruption B. The reader will appreciate that is a type of counterfactual explanation. In this work we introduce a domain agnostic counterfactual explanation technique to produce explanations for time series anomalies. As we will show, our method can produce both visual and text-based explanations that are objectively correct, intuitive and in many circumstances, directly actionable.
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。
