The method is implemented as a Perfomalist API: https://www.trutechdev.com/2021/11/the-change-points-detection-perfomalapi.html.
Note there a tuning parameters that corresponds to once explained below:
How to find a change in the historical time-series data?
Long ago I have developed a method to do that which is based on EV data (Exception Value - a magnitude of anomalies collected historically).
Idea: any change that occurred first would appear as an anomaly and then become a normality (norm), so collecting and analyzing the severity of all anomalies opens the possibility to find phases in the history with different patterns. To detect that mathematically one just needs to find all roots of the following equation: EV(t)=0 , where t is time. But it is too simple as that might give you too many change points. To control the the sensitivity of detecting change points the method should have some sensitivity tuning parameters, such as following:
N - normality confidence band in percentiles = UCL-LCL (if it is 100%, that means all observations is normal, 0% means all observations abnormal)
Where UCL is upper control limit and LCL is lower control limit. Why "||" (absolute value)? To catch two types of changes: going up- and downwards.