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#mlforecast
Title
# mlforecast
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Matías Benedetto

07/10/2023, 9:24 AM
Hi everyone, first of all thanks for creating such a cool project. I have been doing forecast with ML by hand until I came across this repo. However, I also notice the StatsForecast project which seems to also cover many of my use cases. Any suggestions as to why choose to do my project with one or the other?
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José Morales

07/11/2023, 1:48 AM
Hi. Each package has its own scope, the main differences are: • statsforecast: ◦ mainly statistical models ◦ one model per serie • mlforecast ◦ ML (sklearn-compatible) models ◦ one model for all series
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Max (Nixtla)

07/16/2023, 5:56 PM
Hi @Matías Benedetto, you don't have to choose wither or. You can leverage both. Here is a tutorial on how to combina them: https://nixtla.github.io/statsforecast/docs/tutorials/statisticalneuralmethods.html
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Matías Benedetto

07/21/2023, 11:57 AM
that is really useful @Max (Nixtla), thank you for sharing
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Max (Nixtla)

07/21/2023, 1:39 PM
Thanks to you :)