I would like to compute “rolling origin” multi-step forecasts on a test set, i.e. train models using data up to time T and then produce h-step forecasts with origin at T, T+1, T+2 and so on, for evaluation. This is similar to time series cross-validation as described here, except the models are re-estimated each time origin is moved. I presume it may take a long time sometimes, in which case I would probably prefer not to re-estimate. Is there an automated way in fable to that?

I would like to compute “rolling origin” multi-step forecasts on a test set, i.e. train models using data up to time T and then produce h-step forecasts with origin at T, T+1, T+2 and so on, for evaluation. This is similar to time series cross-validation as described here, except the models are re-estimated each time origin is moved. I presume it may take a long time sometimes, in which case I would probably prefer not to re-estimate. Is there an automated way in fable to that?

Q: Is there a good work-around broken user-defined transform handling in fable?

Q: Why do I get a different shape of output when producing bootstrap-simulated forecasts from bottom-up vs top-down hierarchical reconciliation procedures in the R tidyvets/fable framework?

Q: What exactly are bootstrap simulated forecast paths of hierarchically reconciled forecasts in R’s fable package? To my surprise, they’re not the result of bootstrap simulating baseline forecasts and reconciling each of those, as I show below.