Hi Team, I just came across your project - awesome! Would have wished to have seen it before having implemented most of it my self... Two quick questions: 1. I already have my multivariate time series dataset at hand with a variety of self-engineered date, lag and moving average features (including some domain specific impuation). Could I use your pipeline implementation with a LightGBM Regressor on top of my own pre-build extended dataframe? 2. Can I understand your multivarite implementation as a global method? Meaning that your under-hood implementation pools the data from all series (identified by a static time series id) and fits one LightGBM model to all series. It uses that model then to predict each series individually? Thanks for your help! 🙂

Hi, thanks for using the library. 1. Yes, if you don't define any transformations your dataframe should just go through the pipeline. 2. Yes, that's what happens. The series identifier is used to compute the features per serie, but in the end a single model is trained and used to predict all series.