8. I'm not an expert at Python but I noticed that in some of the examples feature properties are explicitly called out, for example here (https://nixtla.github.io/mlforecast/forecast.html#dynamic-features) the first line of code is: series = generate_daily_series(100, equal_ends=True, n_static_features=2, static_as_categorical=False) I'm assuming this labels the features as not categorical; does this have any effect on forecasting (I have been labeling categorical features through LightGBM_Parameters, but previously in R the dataframe itself carried that information; not sure how Python works).

The static are declared as integers instead of categoricals there because xgboost can't handle them (or couldn't at the time). if you're using LightGBM you can pass them as categorical and they will be treated as such when training