# Aeolus: A Multi-structural Flight Delay Dataset

arXiv: https://arxiv.org/abs/2510.26616 · HTML: https://mlsanity.com/paper/2510.26616/

## 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.

## Builds on (most cited first, 18 of 18)

- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Variational Graph Auto-Encoders](https://mlsanity.com/paper/1611.07308/) (2016)
- [Revisiting Deep Learning Models for Tabular Data](https://mlsanity.com/paper/2106.11959/) (2021)
- [Long-term Recurrent Convolutional Networks for Visual Recognition and Description](https://mlsanity.com/paper/1411.4389/) (2014)
- [AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks](https://mlsanity.com/paper/1810.11921/) (2018)
- [SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training](https://mlsanity.com/paper/2106.01342/) (2021)
- [When Do Neural Nets Outperform Boosted Trees on Tabular Data?](https://mlsanity.com/paper/2305.02997/) (2023)
- [Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks](https://mlsanity.com/paper/1708.04617/) (2017)
- [Benchmarking Distribution Shift in Tabular Data with TableShift](https://mlsanity.com/paper/2312.07577/) (2023)
- [TabReD: Analyzing Pitfalls and Filling the Gaps in Tabular Deep Learning Benchmarks](https://mlsanity.com/paper/2406.19380/) (2024)
- [TANGOS: Regularizing Tabular Neural Networks through Gradient Orthogonalization and Specialization](https://mlsanity.com/paper/2303.05506/) (2023)
- [On the Efficiency of NLP-Inspired Methods for Tabular Deep Learning](https://mlsanity.com/paper/2411.17207/) (2024)
- [Spatiotemporal Propagation Learning for Network-Wide Flight Delay Prediction](https://mlsanity.com/paper/2207.06959/) (2022)
- [Wild-Tab: A Benchmark For Out-Of-Distribution Generalization In Tabular Regression](https://mlsanity.com/paper/2312.01792/) (2023)
- [A Review on Flight Delay Prediction](https://mlsanity.com/paper/1703.06118/) (2017)
- [Analyzing Flight Delay Prediction Under Concept Drift](https://mlsanity.com/paper/2104.01720/) (2021)
- [Graph machine learning for flight delay prediction due to holding manouver](https://mlsanity.com/paper/2502.04233/) (2025)

## 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