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Installation

You can install via pip with:

pip install pyrregular

For the third-party models, install all of them:

pip install "pyrregular[models]"

or only the family you need: borf, sktime, tslearn, pypots, jax (e.g. pip install "pyrregular[sktime]"). The extra for each pipeline is listed in the table below.

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/

The datasets are versioned: each pyrregular release downloads a fixed version of the files, tagged on Hugging Face (currently data-v1). To use the data of an older release, install that release (e.g. pip install "pyrregular==0.3.1").

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:

Notes

• iborf is in pyrregular.models.borf (from pyrregular.models.borf import iborf_pipeline) and uses the timestamps: give it X, _ = df.irr.to_dense(concatenate_time=True, normalize_time=True).
• raindrop also needs torch-scatter, built for your torch/CUDA version (see the PyG install instructions).
• To reproduce the paper, list_paper_models() lists its 12 models and pyrregular.models.get_paper_model(name) returns a fresh copy of each pipeline. The paper's BORF is the aeon implementation (pip install "pyrregular[aeon]").
• These pipelines wrap fast-moving third-party libraries. They are tested weekly against the latest versions, but an upstream release can break one between pyrregular releases; to reproduce the paper results exactly, pin the library versions.

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

Citation

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

@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}
}