Timeseries artificial features

While working with a timeseries that has multiple dependant values for different variables, does it make sense to invest time in feature engineering artificial features related to overall state? Or am I just redundantly using the same information and should focus on a model capable of capturing the complexity?

This given we ignore trivial lag features and the dataset is small (100s of examples).


E.g. Say I have a dataset of students that compete against each other in debate class. …

artificial complexity datascience engineering feature feature engineering features focus information multiple sense state timeseries values variables