Abstract
Flight delays impose challenges that impact any flight transportation system-predicting when they will occur in a meaningful way to mitigate this issue. However, the distribution of the flight delay system variables changes over 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: (1) how do drift handling strategies influence the prediction performance of delays? (2) 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.
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