Analyzing Flight Delay Prediction Under Concept Drift
This addresses flight delay prediction for aviation systems, but it is incremental as it focuses on comparing existing strategies under different conditions.
The paper tackled flight delay prediction under concept drift by evaluating how drift handling strategies affect performance across different scales, finding that their impacts vary with scale and models.
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.