pyrregular 0.1.10

Irregular time series made easy

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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)

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}, 
}