Hi, how do I reuse the model for new data? So literally storing the fitted and optimised model and using predict for some new observations (I see the new_data argument in .predict but I need a fitted model first).

hey @Piotr Pomorski! Thanks for asking. What library are you referring to? :)

ah right, mlforecast

hey @Piotr Pomorski! Currently, mlforecast doesn’t have a custom save and load behavior. Maybe you could try storing the fitted mlforecast class using pickle and after that call

predict

with

new_data

.

@Piotr Pomorski, if you need that, we are happy to work on that. Would you mind opening a request on our Github?

I actually just reused the optimised parameters and tested the performance by refitting the model with the same (optimised) parameters. However, I stumbled upon one problem with this. The PredictionIntervals seem to be recalibrating as well which I don't want to do on the test set. Is there a way to move them from training to test somehow? Or abandoning them completely and just using levels in predict as another hyperparameter would do?