  Time series anomaly detection has been a perennially important topic in data
science, with papers dating back to the 1950s. However, in recent years there
has been an explosion of interest in this topic, much of it driven by the
success of deep learning in other domains and for other time series tasks. Most
of these papers test on one or more of a handful of popular benchmark datasets,
created by Yahoo, Numenta, NASA, etc. In this work we make a surprising claim.
The majority of the individual exemplars in these datasets suffer from one or
more of four flaws. Because of these four flaws, we believe that many published
comparisons of anomaly detection algorithms may be unreliable, and more
importantly, much of the apparent progress in recent years may be illusionary.
In addition to demonstrating these claims, with this paper we introduce the UCR
Time Series Anomaly Archive. We believe that this resource will perform a
similar role as the UCR Time Series Classification Archive, by providing the
community with a benchmark that allows meaningful comparisons between
approaches and a meaningful gauge of overall progress.
