Citation and Milestones¶
Citing PyPOTS¶
[Updates in Jun 2023] 🎉A short version of the PyPOTS paper is accepted by the 9th SIGKDD international workshop on Mining and Learning from Time Series (MiLeTS’23). Besides, PyPOTS has been included as a PyTorch Ecosystem project.
PyPOTS paper is available on arXiv at this URL., and we are pursuing to publish it in prestigious academic venues, e.g. JMLR (track for Machine Learning Open Source Software). If you use PyPOTS in your work, please cite it as below and 🌟star PyPOTS repository to make others notice this library. 🤗
1 @article{du2023pypots,
2 title = {{PyPOTS: A Python Toolkit for Data Mining on Partially-Observed Time Series}},
3 author = {Wenjie Du},
4 journal = {SIGKDD MiLeTS Workshop},
5 year = {2023},
6 }
7
8 @article{du2025pypots,
9 title = {{PyPOTS v1: A Python Toolkit for Machine Learning on Partially-Observed Time Series}},
10 author = {Wenjie Du, Yiyuan Yang, Linglong Qian, Jun Wang, and Qingsong Wen},
11 year = {2025},
12 }
Research Projects Using PyPOTS¶
There are scientific research projects using PyPOTS and referencing in their papers. Here is an incomplete list of them.
Project Milestones¶
2022-03: PyPOTS project is initiated;
2023-06: A short version of PyPOTS paper is accepted by KDD’23 MiLeTS workshop);
2023-07: PyPOTS has been selected as a PyTorch Ecosystem project;
2024-08: We present the keynote “Learning from Partially Observed Time Series: Towards Reality-Centric AI4TS” in IJCAI’24 AI4TS workshop;
2024-10: PyPOTS achieves its first 1K stars 🌟on GitHub;
2025-06: PyPOTS hits its first 1M downloads in total;