Time Series cross-validation with exogenous covariates (TSLM) in fable package

[Nik333]

Hello:
Could you please help.
I am trying to do TS cross-validation using the template from Hyndman's course, 3rd edition, this section.

The pipeline is as follows:
Original tsibble > streched tsibble > model > forecast(h = 1) > accuracy()
The problem is when TSLM has exogenous covariates, then forecast() seems to need two arguments, h and "new data". Therefore, an error is generated.

I know it might be possible to get around it with tsCV() (used in Hyndman 2nd edition) but I was wondering if there is a more recent method that can take a stretched tsibble.

Regards,
Nik

@robjhyndman

---

^{Referred here by Forecasting: Principles and Practice, by Rob J Hyndman and George Athanasopoulos}

[robjhyndman]

There's an example here: Rob J Hyndman - Time series cross-validation using fable

If you provide new_data, you don't need to provide h.

[Nik333]

@robjhyndman

If you provide new_data, you don't need to provide h

Thanks a lot for replying.
I had a different question about your example, but I posted it on its page.