> Firstly, because no one could tell us how it really worked, how does it know what we consider an anomaly? Some old-fashioned techniques (eg. just triggering

> Firstly, because no one could tell us how it really worked, how does it know what we consider an anomaly? Some old-fashioned techniques (eg. just triggering on high deviations with adjustment for seasonality) are at least explicable, given a little time. But they don't get much buzz. My own view is that the untreated problem for most attempts to apply time series analysis / anomaly detection / concept drift / process control is that the time series observed are highly variable to begin with. A highly variable series means (1) you need more data to build the predictions, (2) those predictions are less certain and (3) the false positives will be higher. Put another way: the need for such tools is a symptom, and a treatment, but not a cure. Making software more predictable is more value-adding than building ever more elaborate predictors.