This message was deleted.
# mlforecast
s
This message was deleted.
j
Hey. Can you provide the code that you're running? There was an issue with the built in lag transformations, are you using those?
j
Hi @José Morales May you share the issue of lag transformation? Perhaps I missed it but did not find discussions in the issue of repository
j
The transformations that kept state like ExpandingMean weren't being backed up correctly during predict, so the second model would get the feature updated with the first model's prediction and so on
👌 1
j
the first thing i realized, which might be a problem on my side. i have features in my training_df (densities around events like christmas), but when i run predict, i dont give that information (i guess i need to specify my exogenous features). it is a bit weird that predict still runs. otherwise this is my full for training:
ok, i fllowed up on your hint and expandingmean and expotentiallyweightedmean creates the bug. roollingmean creates a stable result: _lag_transforms_={ 1: [ExpandingMean()], # creates a bug 7: [ExponentiallyWeightedMean(alpha=0.5)], # creates a bug 24: [RollingMean(_window_size_=30)], # works fine },
but this means the first forecast is the correct one and how it should be and then any further predictions get biased? or is there a general issues with predict atm? also, do you need help with bug fixing? if you point me to the problem, i could have a look and see if i can help.
but then when i run some tuning (especially ts-cv tuning), and each forecast is impacted by the previous one, then i am fucked basically...?
or is it not a problem, because the model is instantiated new every time a new set of params is tested? that could be fine then, right?
j
The issue is fixed in the latest version (0.12.0) so you should be fine by just upgrading
j
ah, ok. thanks 🙂