Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
125 tokens/sec
GPT-4o
53 tokens/sec
Gemini 2.5 Pro Pro
42 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Analyzing Flight Delay Prediction Under Concept Drift (2104.01720v1)

Published 5 Apr 2021 in cs.LG

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.

User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (6)
  1. Lucas Giusti (2 papers)
  2. Leonardo Carvalho (2 papers)
  3. Antonio Tadeu Gomes (1 paper)
  4. Rafaelli Coutinho (3 papers)
  5. Jorge Soares (3 papers)
  6. Eduardo Ogasawara (10 papers)
Citations (7)

Summary

We haven't generated a summary for this paper yet.