Hello,

Given a list of items L collected in the time intervals t = 1,2,...,10, I want to test the hypothesis that the sublist of items in the time interval t = 1,2,...,5 does not come from the same distribution as the sublist of items in the time intervals t = 6,7,...,10.

Is it any standard way to test this hypothesis? I think that we can assume that the list of items comes from a Poisson distributions, then test the null H_0: lambda1 = lambda2 against H_a: lambda1 ~= lambda2 using standard tests for Poisson means.

Has any of you which is more familiar with sequential pattern literature have seen this test before?

Thanks!

Hello,

I am not sure exactly for this case. But you could have a look at the topic of "change detection / concept drift" in data streams. In data stream mining, there are various algorithms in pattern mining that attempts to detect whether there is some significant change between two time windows. Maybe that you could find some ideas by looking at this topic.

Best regards,

Philippe

Thanks for the suggestion Philippe! I'll take a look.