Error in Forecasting_with_structural_timeseries (too many indices for array)

[bitsnaps]

Forecasting with Structural AR Timeseries

Issue description

Executing step by step each cell I got: too many indices for array at line:

IndexError                                Traceback (most recent call last)
<ipython-input-13-fc80453dcf5f> in <module>
     15         sigma=sigma,
     16         constant=True,
---> 17         dims="obs_id_fut_1",
     18     )
     19     yhat_fut = pm.Normal("yhat_fut", mu=ar1_fut[1:], sigma=sigma, dims="obs_id_fut")

Note: I'm not sure if the bug is related to pymc it self or the example, I can't find a way to verify that.

Expected output (according to the doc)

Sampling: [ar1_fut, likelihood, yhat_fut]

 100.00% [8000/8000 00:31<00:00]

Additional info:

Execution environment:

• python v3.7
• Miniconda + custom pip packages
• BeakerX (jupyter notebook).

Proposed solution

Probably reducing dimension.