  Flight delays impose challenges that impact any flight transportation system.
Predicting when they are going to occur is an important way to mitigate this
issue. However, the behavior of the flight delay system varies through time.
This phenomenon is known in predictive analytics as concept drift. This paper
investigates the prediction performance of different drift handling strategies
in aviation under different scales (models trained from flights related to a
single airport or the entire flight system). Specifically, two research
questions were proposed and answered: (i) How do drift handling strategies
influence the prediction performance of delays? (ii) Do different scales change
the results of drift handling strategies? In our analysis, drift handling
strategies are relevant, and their impacts vary according to scale and machine
learning models used.
