I'm looking at attempting to capture the regularity of a time series of events, one measurement per day, with a year's worth of data, and there can be at most one event a day. Say for example the day you do laundry. What I want to capture is a measurement of regularity. Capturing irregularity is straight forward: goodness of fit of the times between consecutive events with a poisson distribution. But this doesn't distinguish between poor fitting series very well.
So I want to measure the other end. How good am I to sticking to a weekly or bi-weekly schedule. My instinct is I want to fit an autoregressive model and take the variance of epsilon. Does this sound right? And how do I normalize for frequency? That is I don't want the epsilon to predict whether I do laundry 50 times a year or 100 times. Just the regularity.
Or is there a conventional way of doing this that I'm overlooking? In this case I'm biased towards fast conventional techniques rather than creative techniques.