Alexander März

@CDonnerer seems like there is quite some overlap with XGBoostLSS, an approach I have developed in 2019 https://github.com/StatMixedML/XGBoostLSS

@esmit61 You might want to use XGBoostLSS, that supports Negative Binomial Distribution Type I https://github.com/StatMixedML/XGBoostLSS

@Fish-Soup You might want to use XGBoostLSS that supports estimating the Beta distribution https://github.com/StatMixedML/XGBoostLSS

Let me know if I can contribute, very happy to.

@kashif Not sure if you had the chance already to go through the material. Very happy to support. Shall we have the discussion on how to proceed offline?

@kashif Based on our discussion about forecast reconciliation, this [Paper](https://arxiv.org/abs/1906.10586) seems to be good starting point.

Adding the [hierarchy](https://mofc.unic.ac.cy/m5-guidelines/#thedataset) of the M5 dataset 

That might be of interest also https://forecasters.org/blog/2020/10/25/call-for-papers-international-journal-of-forecasting-innovations-in-hierarchical-forecasting/

@Cattes Thanks for the effort! Very much appreciate it. Despite your great effort, I want to mention a couple of things: - I agree that making the package available on...

> I have tested the automatic deployment to the test PyPI again, and its now working Ok nice, thanks. > I think having a minimal example in the readme is...