I am trying to use
xgboost for performing some regression and the features I have are rather simple and limited. I have the time stamp associated with some measurements. The measurements are customer counts and the dependent variable is predicting the average customer wait time. There is dependence on time of the day and weekends as well. For example, I notice that wait times are longer in thee afternoon than in the morning and evenings. There is also a dependency on weekday, holiday or weekends. So I also added a boolean variable to indicate whether the given day is a weekend or holiday or not.
The time data I have is in 10 minutes interval and is a regular python time stamp with the date, hours, minutes and second granularity. How should such time features be included with xgboost or random forests or indeed any such modelling paradigms?