State Space: Exogenous Vars - Conflicting dimensions for time (bug?)

[rOybOiii]

Hey PyMC & @jessegrabowski

I’m trying to use state space’s forecast method with exogenous variables, but I keep getting this error:

ValueError: conflicting sizes for dimension ‘time’: length 1107 on the data but length 8 on coordinate ‘time’

My scenario: I’m passing exogenous indicator variables totaling 18 columns and 8 rows long, for an 8 day forecast. The period I’m forecasting is not at the -very- end of my time series. I’m testing my exogenous variable’s ability to reduce variance on events and holidays.

Time series info:

Length: 1107 data points
Start: 2022-01-19
End: 2025-01-29

Relevant code:

// Define start date and forecast period
start_date, n_periods = pd.to_datetime("2024-1-25"), 8

// Extract exogenous indicator variables for the forecast period
scenario = {'data_exog': pd.DataFrame(df_holidays.loc[start_date:].iloc[:n_periods].to_numpy(dtype=float), 
                                      columns=df_holidays.columns)}

// Generate the forecast
forecasts = ss_mod.forecast(trace, start=start_date, periods=n_periods, scenario=scenario)

My second line of code is extracting the relevant portion of my exogenous variables and assigning it to the scenario.

Am I getting this error because I’m not forecasting at the very end of my time series? (edit: dumb question, I’ll test this later.)

Conda Environment-
PyMC Version: 5.20.1
PyMC Extras Version: 0.2.3
Python Version: Python 3.11.11

Thanks,
Roy

[rOybOiii]

Can offer data and Jupyter file. Lemme know.

-Roy

[jessegrabowski]

Can you provide a very simple example that reproduces the error?

[rOybOiii]

Back at the other forum, I do have my model and some data available. I have not had a moment yet to pare it down to something a little simpler, but Jonathan was able to run it in 41 minutes, after putting in some column names into the data.

I'll try to provide something even simpler soon - but work calls me now.

-Roy

[rOybOiii]

Hey @jessegrabowski :

I modified my model so it's a little simpler, with only 2 exogenous variables, and I deleted about 25-30% of the data points. Total run time is about 26 minutes for me, but with your M4 processor, I imagine it'll be closer to 8 minutes. Do you need something even simpler?

I ran this model using a fresh environment with pymc-extras 0.2.3.

Here's the Jupyter file

Here's the data

-Mike