Open hokiegeek2 opened 5 years ago
Hey @hokiegeek2
In my case, when wanting to evaluate two levels of granularity, in fact we still only require a single analysis to be made. An example to be clearer:
Having a dataset of Dimension_1 | Dimension_2 | KPI X1 | Y1 |10 ...
What we want to understand is if on combination X1+Y1 there is an outlier. Therefore transforming the dataframe to only have: Dimensions | KPI X1,Y1 | 10
performs the exact same intent of the analysis, while passing only a single level of granularity.
Sorry for typos or formatting of the answer, hope it was helpful :)
Hi, was wondering if it is possible update this algorithm for seconds level of granularity?