mlsanityData to 14 Oct 2026

Aeolus: A Multi-structural Flight Delay Dataset

arXiv 2510.26616 ↗First posted 30 Oct 2025cs.LG cs.AIcited by 0

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Abstract (arXiv)

We introduce Aeolus, a large-scale Multi-modal Flight Delay Dataset designed to advance research on flight delay prediction and support the development of foundation models for tabular data. Existing datasets in this domain are typically limited to flat tabular structures and fail to capture the spatiotemporal dynamics inherent in delay propagation. Aeolus addresses this limitation by providing three aligned modalities: (i) a tabular dataset with rich operational, meteorological, and airportlevel features for over 50 million flights; (ii) a flight chain module that models delay propagation along sequential flight legs, capturing upstream and downstream dependencies; and (iii) a flight network graph that encodes shared aircraft, crew, and airport resource connections, enabling cross-flight relational reasoning. The dataset is carefully constructed with temporal splits, comprehensive features, and strict leakage prevention to support realistic and reproducible machine learning evaluation. Aeolus supports a broad range of tasks, including regression, classification, temporal structure modeling, and graph learning, serving as a unified benchmark across tabular, sequential, and graph modalities. We release baseline experiments and preprocessing tools to facilitate adoption. Aeolus fills a key gap for both domain-specific modeling and general-purpose structured data research.Our source code and data can be accessed at https://github.com/Flnny/Delay-data

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 42 references that are in the map. First posted on arXiv 30 Oct 2025.

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18 of 42 references.

References · 18

  1. 2014Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modelingcited by 2,367
  2. 2014DeepWalk: Online Learning of Social Representationscited by 1,547
  3. 2016Variational Graph Auto-Encoderscited by 1,382
  4. 2021Revisiting Deep Learning Models for Tabular Datacited by 740
  5. 2014Long-term Recurrent Convolutional Networks for Visual Recognition and Descriptioncited by 484
  6. 2018AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networkscited by 320
  7. 2021SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Trainingcited by 319
  8. 2023When Do Neural Nets Outperform Boosted Trees on Tabular Data?cited by 223
  9. 2017Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networkscited by 126
  10. 2023Benchmarking Distribution Shift in Tabular Data with TableShiftcited by 66
  11. 2024TabReD: Analyzing Pitfalls and Filling the Gaps in Tabular Deep Learning Benchmarkscited by 33
  12. 2023TANGOS: Regularizing Tabular Neural Networks through Gradient Orthogonalization and Specializationcited by 32
6 more
  1. 2024On the Efficiency of NLP-Inspired Methods for Tabular Deep Learningcited by 7
  2. 2022Spatiotemporal Propagation Learning for Network-Wide Flight Delay Predictioncited by 6
  3. 2023Wild-Tab: A Benchmark For Out-Of-Distribution Generalization In Tabular Regressioncited by 6
  4. 2017A Review on Flight Delay Predictioncited by 4
  5. 2021Analyzing Flight Delay Prediction Under Concept Driftcited by 1
  6. 2025Graph machine learning for flight delay prediction due to holding manouvercited by 1

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