Even the original twitter blog seems to have gone out of its way to make this seem more complex then it is...
Decomposition of time series is done with STL (stl function in stats package) and this is the first part of what they call "Seasonal Hybrid ESD (S-H-ESD)" (sounds impressive right?) which then apparently just involves taking the max absolute difference from the detrended sample mean in terms of standard deviations, remove it and repeat until you have your collection of x outliers. If they wanted to this could be explained in a few sentences, and the underlying code is really simple [0], but for whatever reason it's been written up as advanced analytics — as if decomposing a time series is a major challenge.
While the computation might be relatively simple, its still necessary to be aware of literature and use the proper academic description for the methods.
Decomposition of time series is done with STL (stl function in stats package) and this is the first part of what they call "Seasonal Hybrid ESD (S-H-ESD)" (sounds impressive right?) which then apparently just involves taking the max absolute difference from the detrended sample mean in terms of standard deviations, remove it and repeat until you have your collection of x outliers. If they wanted to this could be explained in a few sentences, and the underlying code is really simple [0], but for whatever reason it's been written up as advanced analytics — as if decomposing a time series is a major challenge.
[0] https://github.com/twitter/AnomalyDetection/blob/master/R/de...