Analyzing Flight Delay Prediction Under Concept Drift

TL;DR

This paper examines how concept drift affects flight delay prediction accuracy and evaluates different drift handling strategies across various scales and models in aviation.

Contribution

It provides a comparative analysis of drift handling strategies and their effectiveness in flight delay prediction under different scales and models.

Findings

Drift handling strategies significantly influence prediction performance.

The impact of strategies varies with scale and model used.

Handling concept drift improves delay prediction accuracy.

Abstract

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.