<a href="https://github.com/WenjieDu/BenchPOTS">
<img src="https://pypots.com/figs/pypots_logos/BenchPOTS/logo_FFBG.svg" width="200" align="right">
</a>
<h3 align="center">Welcome to BenchPOTS</h3>
<p align="center"><i>a Python toolbox for benchmarking ML on POTS (Partially-Observed Time Series)</i></p>
<p align="center">
<a href="https://docs.pypots.com/en/latest/install.html#reasons-of-version-limitations-on-dependencies">
<img alt="Python version" src="https://img.shields.io/badge/Python-v3.7+-E97040?logo=python&logoColor=white">
</a>
<a href="https://github.com/WenjieDu/BenchPOTS/releases">
<img alt="the latest release version" src="https://img.shields.io/github/v/release/wenjiedu/benchpots?color=EE781F&include_prereleases&label=Release&logo=github&logoColor=white">
</a>
<a href="https://github.com/WenjieDu/BenchPOTS/blob/main/LICENSE">
<img alt="BSD-3 license" src="https://img.shields.io/badge/License-BSD--3-E9BB41?logo=opensourceinitiative&logoColor=white">
</a>
<a href="https://github.com/WenjieDu/PyPOTS#-community">
<img alt="Community" src="https://img.shields.io/badge/join_us-community!-C8A062">
</a>
<a href="https://github.com/WenjieDu/BenchPOTS/graphs/contributors">
<img alt="GitHub contributors" src="https://img.shields.io/github/contributors/wenjiedu/benchpots?color=D8E699&label=Contributors&logo=GitHub">
</a>
<a href="https://star-history.com/#wenjiedu/benchpots">
<img alt="GitHub Repo stars" src="https://img.shields.io/github/stars/wenjiedu/benchpots?logo=None&color=6BB392&label=%E2%98%85%20Stars">
</a>
<a href="https://github.com/WenjieDu/BenchPOTS/network/members">
<img alt="GitHub Repo forks" src="https://img.shields.io/github/forks/wenjiedu/benchpots?logo=forgejo&logoColor=black&label=Forks">
</a>
</p>
To evaluate the performance of algorithms on POTS datasets, a benchmarking toolkit is necessary, hence the ecosystem library BenchPOTS is developed.
BenchPOTS provides the standard and unified preprocessing pipelines of a variety of POTS datasets.
It supports a variety of evaluation tasks to help users understand the performance of different algorithms.
## ❖ Citing BenchPOTS/PyPOTS
The paper introducing PyPOTS project is available on arXiv at [this URL](https://arxiv.org/abs/2305.18811),
and we are pursuing to publish it in prestigious academic venues, e.g. JMLR (track for
[Machine Learning Open Source Software](https://www.jmlr.org/mloss/)). If you use BenchPOTS in your work,
please cite PyPOTS project as below and 🌟star this repository to make others notice this library. 🤗 Thank you!
<p align="center">
<a href="https://github.com/WenjieDu/PyPOTS">
<img src="https://pypots.com/figs/pypots_logos/Ecosystem/PyPOTS_Ecosystem_Pipeline.png" width="95%"/>
</a>
</p>
``` bibtex
@article{du2023pypots,
title={{PyPOTS: a Python toolbox for data mining on Partially-Observed Time Series}},
author={Wenjie Du},
year={2023},
eprint={2305.18811},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2305.18811},
doi={10.48550/arXiv.2305.18811},
}
```
or
> Wenjie Du. (2023).
> PyPOTS: a Python toolbox for data mining on Partially-Observed Time Series.
> arXiv, abs/2305.18811.https://arxiv.org/abs/2305.18811
<details>
<summary>🏠 Visits</summary>
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<br>
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