is it possible that the target transform using globalsklearntransformer inside mlforecast is buggy? I realised when i use min-max scaler during training and make inference, the target is not re-scaled again. but i also dont find a way to access the fitted scaler for reverse trasformation:

sk_boxcox = PowerTransformer(method="box-cox", standardize=False)
boxcox_global = GlobalSklearnTransformer(sk_boxcox)

scaler = MinMaxScaler(feature_range=(0, 1))
minmax_global = GlobalSklearnTransformer(scaler)

target_transforms = [minmax_global]

target_transforms=target_transforms

Can you provide an example? I don't understand if you're using both boxcox and min max and what do you mean by "the target is not re-scaled again". Using a global transformer computes the statistics across all series and uses the same scale for all of them, so maybe that scaling isn't good for the model and it just predicts zero or similar

no, this was just same examples. during my tuning i test both, but i dont use the local components from target transform but use the sklearn based solution. but i just saw there is LocalBoxCox in traget tragsform. it should transform the target back during prediction, right?

you can run a quick check by loading your serialized model and checking if the target transforms are there, e.g.

mlf = your_load_fn(path)
assert mlf.ts.target_transforms is not None

if that assert fails then the target transformations weren't saved and thus the results will be the raw predictions from the model

ah, perfect! amazin

The inverse transform should be applied to the predictions then, you can check its stats with

fitted_tfm = loaded_model.ts.target_transforms[0].transformer_
fitted_tfm.data_min_, fitted_tfm.data_max_

so using your local boxcox solution works well. but the min max global sklearn based scaler showed predictions being much lower than what was seen during training. not sure what happened, but i had some issues with scaling before but never had the time to investigate further. i will use the local solutions now and unfortunately cant investigate further what happens when using the global solution. it might very well be something on my side, although i applied it as is shown in documentation, but who knows. but thanks so much again jose for your help. you really saved my night again with your tips 🙂 thanks so much