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maybe the

cross_validation

method from the core neuralforecast can help? it basically trains your model up to a point then forecast for a given number of horizons in the future, which could be week 1, 2, .., n

That's certainly a possible workaround, thanks! For it to work properly I assume I'd have to append the last n observations from y_train to y_test and run cross_validation.

That's a fair workaround. I tend to leave out some of the dataset/y_test for out-of-sample testing (especially the most recent past), just to check for overfitting

How would this work for SimpleExponentialSmoothingOptimized though? This has no

forward

method. I'd expect to be able to find the optimized alpha value on the training set and then call forward on the test set. Being able to use y_true in a rolling way is a big requirement for me

I guess it does just that. I wasn't familiar with the model, but was able to whip up some quick example to show it in action: github gist. from what I get: • it cuts off the training data at 1958-11-30, fixing alpha • then uses the following y_hat as the rolling weights

Thank you so much for helping! The issue is that in our use case we might have access to y_true so we want to split experiments in cases where we use y_hat in a rolling way (as in your gist) and cases where we replace the oldest y_hat with y_true. I think forward does this (although it isn't really documented what it does...) but this model does not have it 😕

I guess you're right. Maybe a loop over fit/predict would work, where you choose whether to paste y_hat or y_true?

Yeah, I can make it work with a few ugly workarounds or looping. That's totally ok. Since the package already does so much I was implicitly expecting it to do eveyrhting.

yeah it'll get ugly fast 😅

I can open an issue on Git to support this because I think it's a common enough use case

perhaps there's a place for a

rolling

flag in

cross_validation

to do just that