If I'm using a tree method (e.g GBM) and I have a time series hourly data, and I predict my target variable $y$ for the next 48 hours, do my predictions were made all at once, or does the second day of prediction already use my first day of prediction as input?
For example, when I'm predicting $y$ at hour 2, does the model have in account the predicted value of $y$ at hour 1; and when I'm predicting $y$ at hour 3, does it have in account the value of $y$ at hour 2? and so on.. Or does the model predict all of the 48 values of $y$ without any connection between them (independently)?
I have all of the feature variables for the 48 hours, I only need to predict my target variable $y$. Basically, what I'm asking is if the method uses predictions to predict further ahead or does it predict all at once?
(The data does not have any lag features)