Alexander März
Alexander März
@fkiraly Thanks for raising this important issue! I am open to grant you access as a developer, but want to clarify that I don't see a need to move the...
Thanks for opening the PR and for your interest in the proect, very much appreciated! I`d need some time, though, to look into it in detail. May I ask you...
Thanks for your changses. I am currently occupied with the Hyper-Tree paper, so please do expect some delay in my review.
@gmgeorg Thanks for creating the PR. I assume you further refine the notebooks and the .py files? Since, e.g., the Gaussian example still has some explanations for modelling the gaussian...
@claude Can you fix this for me?
@claude Fix this for me
Hi @nucflash Thanks a lot for taking this up. I am greatful for support. Most of the work concerning relaxing dependencies and their updates has been done by @fkiraly for...
@GGA-PERSO, @Thomas2419 maybe you also want to look at this https://github.com/amazon-science/chronos-forecasting/blob/e48f48071f999825b4ed114230229dd771fac129/src/chronos/chronos2/pipeline.py#L452 https://github.com/amazon-science/chronos-forecasting/blob/e48f48071f999825b4ed114230229dd771fac129/src/chronos/chronos2/pipeline.py#L456 > Note: categorical covariates are only supported as numpy arrays as torch does not support str dtype
The original LightGBMLSS is now aligned with @fkiraly's implementation for XGBoostLSS. just wanted to inform you.