Forecasting using distributions\timeseries in pymc 4.4.0

Questions
2

Do you want to forecast, conditioning on the last observed value or also redrawing the observed timepoints?

If it’s the first, the easiest is to add a Deterministic:

with pm.Model() as m:
  ...
  obs = pm.AR("obs", ..., observed=data)
  pred = pm.Deterministic(
    "preds", 
    pm.AR.dist(
      init_dist=pm.DiracDelta.dist(data[-1]), 
      ...,
      steps=100,
  ),
)

Passing the same parameters that you use for the observed timeseries, which I omitted with ...

If the second, just use the default sample_posterior_predictive. You can make the shape depend on a MutableData to increase the number of steps that are simulated.