> classic random forest / decision trees have trouble with time series predictions This is not true. The trick is you have to convert your longitudinal data to

> classic random forest / decision trees have trouble with time series predictions This is not true. The trick is you have to convert your longitudinal data to be cross-sectional via feature engineering of lagged features. There are also related tricks like expanding datetimes into features like day of week, day of month, etc. This can be a lot of work, though there are software tools which help do this. Some general time-oriented feature engineering plus a vanilla random forest is a great second baseline (after LOCF), and then if needed you can spend 10x the time tuning a GBM to beat that. If you have a GPU, checkout fastai's tabular learning stuff; easy feature eng and neural nets with embeddings can do a lot with low effort.