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?

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

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

that is really useful @Max (Nixtla), thank you for sharing

Thanks to you :)