Researchers theorize that they might be imitating snakes. Breakthroughs, discoveries, and DIY tips sent every weekday. A caterpillar-looking bug hangs out on a stem, minding its own business. Suddenly, forceps emerge, moving towards the creature. As soon as they touch the chunky insect, it hisses and whips its body side-to-side.

New research shows ill pupae emit a chemical signal before ever leaving their cocoons. Breakthroughs, discoveries, and DIY tips sent every weekday. Ants are some of nature's most selfless animals. They practice social distancing when ill, consistently act for the good of the colony, and will die to protect their queen from outsiders. This evolutionary drive is so strong that at least one ant species will even willingly sacrifice before they leave their cocoons.

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.