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Installation

You can install via pip with:

pip install pyrregular

For third party models use:

pip install pyrregular[models]

Quick Guide

List datasets

If you want to see all the datasets available, you can use the list_datasets function:

from pyrregular import list_datasets

df = list_datasets()

Load a dataset

To load a dataset, you can use the load_dataset function. For example, to load the "Garment" dataset, you can do:

from pyrregular import load_dataset

df = load_dataset("Garment.h5")

The dataset is saved in the default os cache directory, which can be found with:

import pooch

print(pooch.os_cache("pyrregular"))

The repository is hosted at: https://huggingface.co/datasets/splandi/pyrregular/

Downstream tasks

Classification

To use the dataset for classification, you can just "densify" it:

from pyrregular import load_dataset

df = load_dataset("Garment.h5")
X, _ = df.irr.to_dense()
y, split = df.irr.get_task_target_and_split()

X_train, X_test = X[split != "test"], X[split == "test"]
y_train, y_test = y[split != "test"], y[split == "test"]

# We have ready-to-go models from various libraries:
from pyrregular.models.rocket import rocket_pipeline

model = rocket_pipeline
model.fit(X_train, y_train)
model.score(X_test, y_test)

There are several pipelines available in pyrregular.models:

💾 Library	📖 Source	🔗 Pipeline	ℹ️ Type
aeon	Spinnato et al. (2024)	borf	dictionary-based transform + lgbm classifier
aeon		rifc	interval-based transform + lgbm classifier
diffrax	Kidger et al. (2020)	ncde	neural controlled differential equations
pypots	Cao et al. (2018)	brits	bidirectional recurrent imputation network
pypots	Che et al. (2018)	grud	gated recurrent unit with decay
pypots	Zhang et al. (2021)	raindrop	graph neural network
pypots	Du et al. (2023)	saits	self-attention-based imputation transformer
pypots	Wu et al. (2022)	timesnet	temporal 2d-variation transformer
sktime	Ke et al. (2017)	lgbm	gradient boosted tree
sktime	Dempster et al. (2021)	rocket	kernel-based transform + lgbm classifier
sktime	Bagheri et al. (2016)	svm	support vector machine with distance kernel
tslearn	Sakoe & Chiba (1978)	knn	distance-based with dynamic time warping

Regression

Regression is still work in progress, but is available for some datasets:

from pyrregular import load_dataset
from sklearn.pipeline import make_pipeline
from aeon.transformations.collection.dictionary_based import BORF
from sklearn.linear_model import LassoCV

df = load_dataset("Garment.h5")
X, _ = df.irr.to_dense()
y, split = df.irr.get_task_target_and_split("regression")

X_train, X_test = X[split != "test"], X[split == "test"]
y_train, y_test = y[split != "test"], y[split == "test"]

borf_pipeline = make_pipeline(
    BORF(),
    LassoCV(),
)
model = borf_pipeline
model.fit(X_train, y_train)
model.score(X_test, y_test)

Available Datasets

📈 Dataset	📖 Source
Alembics Bowls Flasks	Spinnato & Landi, 2025
AllGestureWiimoteX	Guna et al., 2014
AllGestureWiimoteY	Guna et al., 2014
AllGestureWiimoteZ	Guna et al., 2014
Animals	Ferrero et al., 2018
AsphaltObstaclesCoordinates	Souza, 2018
AsphaltPavementTypeCoordinates	Souza, 2018
AsphaltRegularityCoordinates	Souza, 2018
CharacterTrajectories	Williams et al., 2006
DodgerLoopDay	Ihler et al., 2006
DodgerLoopGame	Ihler et al., 2006
DodgerLoopWeekend	Ihler et al., 2006
Geolife	Zheng et al., 2009; Zheng et al., 2008; Zheng et al., 2010
GestureMidAirD1	Caputo et al., 2018
GestureMidAirD2	Caputo et al., 2018
GestureMidAirD3	Caputo et al., 2018
GesturePebbleZ1	Mezari & Maglogiannis, 2018
GesturePebbleZ2	Mezari & Maglogiannis, 2018
GPS Data of Seabirds	Browning et al., 2018
InsectWingbeat	Chen et al., 2014
JapaneseVowels	Kudo et al., 1999
Localization Data for Person Activity	Vidulin et al., 2010
MelbournePedestrian	City of Melbourne, 2019
MIMIC-III Clinical Database (Demo)	Johnson et al., 2016; Johnson et al., 2019; Goldberger et al., 2000
PAMAP2 Physical Activity Monitoring	Reiss & Stricker, 2012
PhysioNet 2012	Silva et al., 2012
PhysioNet 2019	Reyna et al., 2020
PickupGestureWiimoteZ	Guna et al., 2014
PLAID	Gao et al., 2014
Productivity Prediction of Garment Employees	Imran et al., 2021
ShakeGestureWiimoteZ	Guna et al., 2014
SpokenArabicDigits	Hammami & Bedda, 2010
Taxi	Moreira-Matias et al., 2013
Vehicles	Chorochronos Archive, 2019

Citation

If you use this package in your research, please cite the following paper:

@misc{spinnato2025pyrregular,
      title={PYRREGULAR: A Unified Framework for Irregular Time Series, with Classification Benchmarks}, 
      author={Francesco Spinnato and Cristiano Landi},
      year={2025},
      eprint={2505.06047},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2505.06047}, 
}