# Analyzing Flight Delay Prediction Under Concept Drift

arXiv: https://arxiv.org/abs/2104.01720 · HTML: https://mlsanity.com/paper/2104.01720/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 44 references that are in the map.
- First posted on arXiv 5 Apr 2021.

## Builds on (most cited first, 2 of 2)

- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [Characterizing Concept Drift](https://mlsanity.com/paper/1511.03816/) (2015)

## Built on by (most cited first, 1 of 1)

- [Aeolus: A Multi-structural Flight Delay Dataset](https://mlsanity.com/paper/2510.26616/) (2025)

## Abstract (arXiv)

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.