Alexander März

Thanks for your suggestion. As of now, `model.py` among others also imports optuna and the plotting packages. So all the requirements in the `setup.py` are required. I go thorugh your...

@wangbingnan136 Many thanks for making the TCN model available. However, I am not sure which version of the following you are actually implementing: - [Probabilistic forecasting with temporal convolutional neural...

> Actually it is almost the same as https://github.com/philipperemy/keras-tcn. > I think the related paper is this one:https://arxiv.org/pdf/1803.01271.pdf 《An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling》...

> Actually, seeing this, we should probably switch to **Probabilistic forecasting with temporal convolutional neural network** as it has a proper decoder (The quantile loss is trivial and would be...

Hi @Fish-Soup, Thanks for the interest in the project. Can you please provide a miminal working example so that I can look into it? Thanks!

@hugolytics Thanks for opening the PR. The checks are not successful. Can you please look into it, thanks.

@hugolytics Is this PR still active? Otherwise I close it.

Closed with new PR

@fkiraly Thanks for suggesting the skpro integration. Integrating the LSS-models into both skpro and sktime would be a fantastic extension! For now I suggest we focus on the XGBoostLSS/LightGBMLSS integrations,...

I have created respective branches in the repos - XGBoostLSS: https://github.com/StatMixedML/XGBoostLSS/tree/skpro_integration - LightGBMLSS: https://github.com/StatMixedML/LightGBMLSS/tree/skpro_integration Please work towards them before we actually merge it to master.