# PYRREGULAR: A Unified Framework for Irregular Time Series, with Classification Benchmarks

arXiv: https://arxiv.org/abs/2505.06047 · HTML: https://mlsanity.com/paper/2505.06047/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 May 2025.

## Built on by (most cited first, 7 of 7)

- [Domain-Informed Multi-View Self-Distillation for Astronomical Light-Curve Representation Learning with JEPA](https://mlsanity.com/paper/2606.28446/) (2026)
- [GARLIC: Graph Attention-based Relational Learning of Multivariate Time Series in Intensive Care](https://mlsanity.com/paper/2608.10969/) (2026)
- [Beyond MSE: Rethinking the Evaluation Metric and Benchmarking for Irregular Time Series Forecasting](https://mlsanity.com/paper/2608.17293/) (2026)
- [A Synthetic Ground-Truth Framework for the Evaluation of Explainable AI Methods](https://mlsanity.com/paper/2609.30397/) (2026)
- [MASCIT: A Mask-Aware State Space Classifier for Naturally Irregular Time Series](https://mlsanity.com/paper/2609.34409/) (2026)
- [A Time-Aware Bag-of-Receptive-Fields for Interpretable Irregular Time Series Classification](https://mlsanity.com/paper/2609.39268/) (2026)
- [QiYao-I: A Manifold Based Foundation Model for Irregular Multivariate Time Series Forecasting](https://mlsanity.com/paper/2610.06936/) (2026)

## Abstract (arXiv)

Irregular temporal data, characterized by varying recording frequencies, differing observation durations, and missing values, presents significant challenges across fields like mobility, healthcare, and environmental science. Existing research communities often overlook or address these challenges in isolation, leading to fragmented tools and methods. To bridge this gap, we introduce a unified framework, and the first standardized dataset repository for irregular time series classification, built on a common array format to enhance interoperability. This repository comprises 34 datasets on which we benchmark 12 classifier models from diverse domains and communities. This work aims to centralize research efforts and enable a more robust evaluation of irregular temporal data analysis methods.