<!channel>: Dear community, show some love for MLF...
# mlforecastm
Max (Nixtla)
07/20/2023, 8:18 PM<!channel>: Dear community, show some love for MLForecast
https://www.linkedin.com/feed/update/urn:li:activity:7087015150888620033?commentUrn=u[…]0560564264960%2Curn%3Ali%3Aactivity%3A7087015150888620033%29
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Andrew Doherty
07/26/2023, 10:45 AMHi @Max (Nixtla), in your response you mention local scalers for MLForecast but I can't find this in the documentation/code. I must be missing it. Could you share where this is? Thanks.
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Max (Nixtla)
07/26/2023, 2:07 PMHi Andrew! In mlforecast you have to specifically tell the software which scaler to use. You can do so through this argument:
target_transforms=[LocalStandardScaler()]
See line 14 on this notebook: https://www.kaggle.com/code/fedegr/mlforecast-functime/notebook
Max (Nixtla)
07/26/2023, 2:09 PM@José Morales: maybe we should write a little guide or something.
Max (Nixtla)
07/26/2023, 2:11 PMMeanwhile here is another example: https://nixtla.github.io/mlforecast/docs/end_to_end_walkthrough.html#adding-features
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José Morales
07/26/2023, 5:43 PMYeah I have a guide for target transforms almost done, will add it soon
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Andrew Doherty
07/27/2023, 8:23 AMExcellent, thanks a lot @Max (Nixtla) and @José Morales. Do you plan on implementing for exogenous features? To enable sliding/expanding window standard scaling for example.
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José Morales
07/27/2023, 9:04 PMThis is already possible by doing something like here https://github.com/Nixtla/mlforecast/issues/124#issue-1683960264
But we will add a built in way to do it soon
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Andrew Doherty
07/31/2023, 9:41 AMOK nice one. Thanks again.