# How to detrend in a stacked model?

Let's say I have user data (x) for customers (i) in the period between 2010 and 2020 (t). I want to predict if customer i churns at time t. To do so, I have built a stacked model which looks somewhat like:

Client  |     Date    |  Logins |   Churn
1       2010-01-01      20         0
1       2010-02-01      25         0
...          ...         ...       ...
100      2019-01-01      3          0
100      2019-02-01      6          1


Two questions come to mind after looking at the data (including more variables) before running my XGBoost model:

• What if there is an overall trend in time with regard to the number of logins for all users. Should we replace a detrended number of logins instead?
• There a relative big difference between the number of logins per user. Should the number of logins also be detrended on a client level so that the effect of a decrease in the number of logins per client also contributes to the churn probability?