I believe I have a basic working version of this. Assumes your trace has a `rho`

and `scale`

. This also allows for multiple observations for the same date, which is often a case I’m working with. But if you want to just have one observation per date you could just remove the `date_idx`

parts.

```
def predict_outofsample(trace, date_idx):
"""
trace: a pymc3 MultiTrace object
date_idx: np.ndarray with shape (N_obs), indicating for each observation what date it corresponds to
(so you can have multiple observations on the same day that will have the same prediction)
"""
samples = []
horizon = np.max(date_idx)
for point in enumerate(trace.points()):
rho, scale = point['rho'], point['scale']
thetas = [np.random.normal(loc=0, scale=scale)]
for i in range(horizon):
thetas.append(rho*thetas[-1] + np.random.normal(loc=0, scale=scale))
samples.append(thetas)
return np.array(samples)[:, date_idx]
```