Rolling Regression with Time Blocks

I’m looking at this example of rolling regression in the Pymc3 documentation here:, and I’m wondering if there’s an easy way to modify this such that the parameters which are undergoing the random walk (alpha & beta) can be made to repeat themselves a certain number of times before allowing to take a walk ‘step’.

For example, instead of each timestep having it’s own unique walk-step of alpha & beta (in this example, corresponding to a day), could it be modified so that the time steps are at a rougher granularity, perhaps if you expected the time-dependent relationship to change more slowly?

One idea is to model the alpha and beta as some gaussian process with a Periodic covariance matrix, or put some smoothing team on the random walk (which would be roughly equal to a gaussian process).