BTW you should take time buckets that are relatively free of daily cycles like
3 day, week, or month buckets for “hot”. This is to remove cyclical affects
from the frequencies as much as possible since you need 3 buckets to see the
change in change, 2 for the change, and 1 for the event volume.
So your idea is to find anomalies in event frequencies to detect “hot” items?
Interesting, maybe Ted will chime in.
What I do is take the frequency, first, and second, derivatives as measures of
popularity, increasing popularity, and increasingly increasing popularity. Put
another way popular,
Hi "all",
I am wondering what would be the best way to incorporate event time
information into the calculation of the G-Test.
There is a claim here
https://de.slideshare.net/tdunning/finding-changes-in-real-data
saying "Time aware variant of G-Test is possible"
I remember i experimented with ex
