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In recent years there has been significant progress in time series anomalydetection. However, after detecting an (perhaps tentative) anomaly, can weexplain it? Such explanations would be useful to triage anomalies. For example,in an oil refinery, should we respond to an anomaly by dispatching a hydraulicengineer, or an intern to replace the battery on a sensor? There have been someparallel efforts to explain anomalies, however many proposed techniques produceexplanations that are indirect, and often seem more complex than the anomalythey seek to explain. Our review of the literature/checklists/user-manuals usedby frontline practitioners in various domains reveals an interestingnear-universal commonality. Most practitioners discuss, explain and reportanomalies in the following format: The anomaly would be like normal data A, ifnot for the corruption B. The reader will appreciate that is a type ofcounterfactual explanation. In this work we introduce a domain agnosticcounterfactual explanation technique to produce explanations for time seriesanomalies. As we will show, our method can produce both visual and text-basedexplanations that are objectively correct, intuitive and in many circumstances,directly actionable.

PUPAE: Intuitive and Actionable Explanations for Time Series Anomalies