--- pretty_name: pyrregular viewer: false license: - cc-by-4.0 - cc-by-nc-sa-4.0 - cc0-1.0 - odbl - odc-by - other - unknown license_name: dataset-specific --- ![Pyrregular: irregular time series datasets and benchmarks](https://github.com/fspinna/pyrregular/blob/main/assets/images/logo_01.png?raw=true) # Pyrregular: Irregular Time Series Datasets and Benchmarks **Published at ICLR 2026** · 38 datasets, including the benchmark of 34 datasets and 12 classifiers · [GitHub](https://github.com/fspinna/pyrregular) · [Documentation](https://fspinna.github.io/pyrregular/) · [PyPI](https://pypi.org/project/pyrregular/) · [Paper](https://openreview.net/forum?id=qetBM8nLkf) Naturally irregular time series (uneven sampling, missing observations, signals recorded at different times, variable-length sequences) in one standardized format, for classification and regression. These are the datasets of the [pyrregular](https://github.com/fspinna/pyrregular) Python library, including the 34 of the ICLR 2026 benchmark. The files are HDF5, read by the library (the dataset viewer is off): ```bash pip install pyrregular ``` ```python from pyrregular import load_dataset df = load_dataset("Garment.h5") ``` The files are versioned with git tags (`data-v1`, ...); each pyrregular release downloads one fixed version. ## Licenses Each dataset keeps the license of its original source; we redistribute it in a converted format, with attribution. `unknown` means the source states no license (most of these come from the UEA & UCR time series archive). If you hold the rights to a dataset and want it removed or its attribution changed, please open an issue at https://github.com/fspinna/pyrregular/issues. | Dataset | License | Source | Citation | |---|---|---|---| | Abf | CC-BY-4.0 | source | [cite](https://arxiv.org/abs/2505.06047) | | Ais | [CC-BY-4.0](https://zenodo.org/records/8064564) | [source](https://zenodo.org/records/8064564) | [cite](https://doi.org/10.5281/zenodo.8064564) | | AllGestureWiimoteX | unknown | [source](https://timeseriesclassification.com/) | [cite](https://timeseriesclassification.com/description.php?Dataset=AllGestureWiimoteX) | | AllGestureWiimoteY | unknown | [source](https://timeseriesclassification.com/) | [cite](https://timeseriesclassification.com/description.php?Dataset=AllGestureWiimoteY) | | AllGestureWiimoteZ | unknown | [source](https://timeseriesclassification.com/) | [cite](https://timeseriesclassification.com/description.php?Dataset=AllGestureWiimoteZ) | | Animals | [other](https://research.fs.usda.gov/pnw/projects/starkeyproject) | [source](https://dl.acm.org/doi/pdf/10.1145/3167132.3167225) | [cite](https://doi.org/10.1145/3167132.3167225) | | AsphaltObstaclesCoordinates | unknown | [source](https://timeseriesclassification.com/) | [cite](https://timeseriesclassification.com/description.php?Dataset=AsphaltObstaclesCoordinates) | | AsphaltPavementTypeCoordinates | unknown | [source](https://timeseriesclassification.com/) | [cite](https://timeseriesclassification.com/description.php?Dataset=AsphaltPavementTypeCoordinates) | | AsphaltRegularityCoordinates | unknown | [source](https://timeseriesclassification.com/) | [cite](https://timeseriesclassification.com/description.php?Dataset=AsphaltRegularityCoordinates) | | CharacterTrajectories | [CC-BY-4.0](https://archive.ics.uci.edu/dataset/175/character+trajectories) | [source](https://timeseriesclassification.com/) | [cite](https://doi.org/10.24432/C58G7V) | | CombinedTrajectories | [CC-BY-NC-SA-4.0](https://www.kaggle.com/datasets/donotmess/combined-trajectories) | [source](https://www.kaggle.com/datasets/donotmess/combined-trajectories) | [cite](https://www.kaggle.com/datasets/donotmess/combined-trajectories) | | DodgerLoopDay | [CC-BY-4.0](https://archive.ics.uci.edu/dataset/157/dodgers+loop+sensor) | [source](https://timeseriesclassification.com/) | [cite](https://doi.org/10.24432/C51P50) | | DodgerLoopGame | [CC-BY-4.0](https://archive.ics.uci.edu/dataset/157/dodgers+loop+sensor) | [source](https://timeseriesclassification.com/) | [cite](https://doi.org/10.24432/C51P50) | | DodgerLoopWeekend | [CC-BY-4.0](https://archive.ics.uci.edu/dataset/157/dodgers+loop+sensor) | [source](https://timeseriesclassification.com/) | [cite](https://doi.org/10.24432/C51P50) | | Garment | [CC-BY-4.0](https://archive.ics.uci.edu/dataset/597/productivity+prediction+of+garment+employees) | [source](https://archive.ics.uci.edu/dataset/597/productivity+prediction+of+garment+employees) | [cite](https://doi.org/10.24432/C51S6D) | | Geolife | [unknown](https://www.microsoft.com/en-us/download/details.aspx?id=52367) | [source](https://www.microsoft.com/en-us/download/details.aspx?id=52367) | [cite](https://www.microsoft.com/en-us/research/publication/geolife-gps-trajectory-dataset-user-guide/) | | GeolifeSupervised | [unknown](https://www.microsoft.com/en-us/download/details.aspx?id=52367) | [source](https://www.microsoft.com/en-us/download/details.aspx?id=52367) | [cite](https://www.microsoft.com/en-us/research/publication/geolife-gps-trajectory-dataset-user-guide/) | | GestureMidAirD1 | unknown | [source](https://timeseriesclassification.com/) | [cite](https://timeseriesclassification.com/description.php?Dataset=GestureMidAirD1) | | GestureMidAirD2 | unknown | [source](https://timeseriesclassification.com/) | [cite](https://timeseriesclassification.com/description.php?Dataset=GestureMidAirD2) | | GestureMidAirD3 | unknown | [source](https://timeseriesclassification.com/) | [cite](https://timeseriesclassification.com/description.php?Dataset=GestureMidAirD3) | | GesturePebbleZ1 | unknown | [source](https://timeseriesclassification.com/) | [cite](https://timeseriesclassification.com/description.php?Dataset=GesturePebbleZ1) | | GesturePebbleZ2 | unknown | [source](https://timeseriesclassification.com/) | [cite](https://timeseriesclassification.com/description.php?Dataset=GesturePebbleZ2) | | InsectWingbeat | unknown | [source](https://timeseriesclassification.com/) | [cite](https://timeseriesclassification.com/description.php?Dataset=InsectWingbeat) | | JapaneseVowels | [CC-BY-4.0](https://archive.ics.uci.edu/dataset/128/japanese+vowels) | [source](https://timeseriesclassification.com/) | [cite](https://doi.org/10.24432/C5NS47) | | Ldfpa | [CC-BY-4.0](https://archive.ics.uci.edu/dataset/196/localization+data+for+person+activity) | [source](https://archive.ics.uci.edu/dataset/196/localization+data+for+person+activity) | [cite](https://doi.org/10.24432/C57G8X) | | MelbournePedestrian | [CC-BY-4.0](https://data.melbourne.vic.gov.au/explore/dataset/pedestrian-counting-system-monthly-counts-per-hour/) | [source](https://timeseriesclassification.com/) | [cite](https://data.melbourne.vic.gov.au/explore/dataset/pedestrian-counting-system-monthly-counts-per-hour/) | | Mimic3 | [ODbL-1.0](https://physionet.org/content/mimiciii-demo/1.4/) | [source](https://physionet.org/content/mimiciii-demo/1.4/) | [cite](https://doi.org/10.13026/C2HM2Q) | | PLAID | [CC-BY-4.0](https://figshare.com/articles/PLAID_2014/11605074) | [source](https://timeseriesclassification.com/) | [cite](https://doi.org/10.6084/m9.figshare.11605074) | | Pamap2 | [CC-BY-4.0](https://archive.ics.uci.edu/dataset/231/pamap2+physical+activity+monitoring) | [source](https://archive.ics.uci.edu/dataset/231/pamap2+physical+activity+monitoring) | [cite](https://doi.org/10.24432/C5NW2H) | | Physionet2012 | [ODC-By-1.0](https://physionet.org/content/challenge-2012/1.0.0/) | [source](https://physionet.org/content/challenge-2012/1.0.0/) | [cite](https://physionet.org/content/challenge-2012/1.0.0/) | | Physionet2019 | [CC-BY-4.0](https://physionet.org/content/challenge-2019/1.0.0/) | [source](https://physionet.org/content/challenge-2019/1.0.0/) | [cite](https://doi.org/10.13026/v64v-d857) | | PickupGestureWiimoteZ | unknown | [source](https://timeseriesclassification.com/) | [cite](https://timeseriesclassification.com/description.php?Dataset=PickupGestureWiimoteZ) | | Seabirds | [CC0-1.0](https://datadryad.org/dataset/doi:10.5061/dryad.t7ck5) | [source](https://doi.org/10.5061/dryad.t7ck5) | [cite](https://doi.org/10.5061/dryad.t7ck5) | | ShakeGestureWiimoteZ | unknown | [source](https://timeseriesclassification.com/) | [cite](https://timeseriesclassification.com/description.php?Dataset=ShakeGestureWiimoteZ) | | SpokenArabicDigits | [CC-BY-4.0](https://archive.ics.uci.edu/dataset/195/spoken+arabic+digit) | [source](https://timeseriesclassification.com/) | [cite](https://doi.org/10.24432/C52C9Q) | | TDrive | [unknown](https://www.microsoft.com/en-us/research/publication/t-drive-trajectory-data-sample/) | [source](https://www.microsoft.com/en-us/research/publication/t-drive-trajectory-data-sample/) | [cite](https://doi.org/10.1145/2020408.2020462) | | Taxi | [CC-BY-4.0](https://archive.ics.uci.edu/dataset/339/taxi+service+trajectory+prediction+challenge+ecml+pkdd+2015) | [source](https://archive.ics.uci.edu/dataset/339/taxi+service+trajectory+prediction+challenge+ecml+pkdd+2015) | [cite](https://doi.org/10.24432/C55W25) | | Vehicles | unknown | [source](https://dl.acm.org/doi/pdf/10.1145/3167132.3167225) | [cite](https://doi.org/10.1145/3167132.3167225) | ## Citation Please cite the original source of each dataset (Citation column) and pyrregular: ```bibtex @inproceedings{ spinnato2026pyrregular, title={{PYRREGULAR}: A Unified Framework for Irregular Time Series, with Classification Benchmarks}, author={Francesco Spinnato and Cristiano Landi}, booktitle={The Fourteenth International Conference on Learning Representations}, year={2026}, url={https://openreview.net/forum?id=qetBM8nLkf} } ```