Measurement of regularity of events I am dealing with events generated by different processes. My data includes time stamps when the events occur, so I am trying to differentiate the processes in regular or irregular categories or assign a numeric measure how "regular" a process is. Regular process would have very similar time deltas between each event.
I have looked into this paper which seemed to deal with very similar problem. Main idea would be to find an average time delta, count the occurrences of events per average time delta and perform goodness of fit tests on 3 distributions. What I don't like in particular are the definition of irregularity and bursting hypothesis. Also it doesn't look like an approach suitable for larger datasets (efficiency wise)
I have looked into approximate entropy, which seems to also have a "fast and easy" approach, but I am not sure whether I am missing something essential why it wouldn't work.
Also I've seen this answer, but I am a bit lost how exactly this shall be done. Would it be "bad fit equals irregular". Also how would you select the parameters (lambda) of Poisson distribution?

Capturing irregularity is straight forward: goodness of fit of the times between consecutive events with a poisson distribution.

Do you have (strong) opinions on one of the approaches and / or a suggestion how to tackle this problem?

Since you have the time stamps, you could calculate the inter-event time (waiting times between events). If your events arrives as a Poisson process, the inter-event times should have an exponential distribution, while if they arrive at constant times the distribution will be a constant. So you could start by characterizing that waiting time distribution. Maybe you could show us a plot?
But it is not clear for me how you want to use the information on irregularity, if you can tell us that maybe a better answer is possible.
