Questions About Microsift Time Series Algorithm

Hello,
I was working with Microsoft Time Series (MTS) algorithm and simulated data in order to evaluate/know it a little more.
I simulated 24 points of the model y[t] = 5.74-0.1486 y[t-1] + e[t] and 19 points of the model y[t] = 10.48-0.0486 y[t-1] + e[t] (a change of level), where e ~ N(0,0.01).
The MTS output is: if time>=23.5 then AR(3) else AR(1): y[t] = 6.23-0.2536 y[t-1].
So, I am wondering:
how the algorithm works whit the time variable as a split variable? Like the other variables? Only considering 4 time points?
Why the MTS algorithm produces AR(p) models where p is a little large (like the example: I simulated an AR(1) model and the output is an AR(3) model), what about parsimony models?
A AR model is a stationary model, so what happen if some data have trend? We need eliminate the trend before the MTS algorithm can be used?Thanks for your time