Long story short, I'm trying to predict how likely it is for a content creator to release new content or when they are most likely to do so (and possibly how this changes over time). My problem is that I can't find a good form of analysis/model for this type of data.
The data: the dates that thousands of different chapters from manga series (basically Japanese comics) have been translated into English. Many of these series have relatively "regular" release schedules--every week, every month, etc. Many are more sporadic, go on hiatuses, get a bunch of chapters all at once, etc.
What I've thought about doing:
Poisson/negative binomial distributions: most recently, I thought I could model the times between each update with a poisson process or negative binomial distribution, but I fear that the data would be way too underdispersed for that. Also, the same-periodic nature of many of these manga series violate a lot of the underlying assumptions.
Time-series analysis: This seems relatively logical to me, but I can't find any analyses that cover data like mine. Each data point is just a date though, so the only possible values of a dependent variable with time as a predictor would be "there was an update" or "there wasn't an update," which seems like it wouldn't fit well. Maybe I could analyze some sort of moving frequency window?
Periodic analyses: I've Googled for hours, reading papers and tutorials about analyses of periodic data, but I haven't found anything that feels like it could capture this data well. Both for the same reason that time-series analyses seems odd, and because the data isn't always periodic, and I'd maybe want something that makes "vaguer" assumptions.
Can anyone point me to any good resources for this, or even just give me terms I could search for? Or just helpful suggestions?