Welcome to PyPOTS docs!#

A Python Toolbox for Machine Learning on Partially-Observed Time Series

⦿ Motivation: Due to all kinds of reasons like failure of collection sensors, communication error, and unexpected malfunction, missing values are common to see in time series from the real-world environment. This makes partially-observed time series (POTS) a pervasive problem in open-world modeling and prevents advanced data analysis. Although this problem is important, the area of data mining on POTS still lacks a dedicated toolkit. PyPOTS is created to fill in this blank.

⦿ Mission: PyPOTS is born to become a handy toolbox that is going to make data mining on POTS easy rather than tedious, to help engineers and researchers focus more on the core problems in their hands rather than on how to deal with the missing parts in their data. PyPOTS will keep integrating classical and the latest state-of-the-art data mining algorithms for partially-observed multivariate time series. For sure, besides various algorithms, PyPOTS is going to have unified APIs together with detailed documentation and interactive examples across algorithms as tutorials.

🤗 Please star this repo to help others notice PyPOTS if you think it is a useful toolkit. Please properly cite PyPOTS in your publications if it helps with your research. This really means a lot to our open-source research. Thank you!

The rest of this readme file is organized as follows: ❖ Available Algorithms, ❖ PyPOTS Ecosystem, ❖ Installation, ❖ Usage, ❖ Citing PyPOTS, ❖ Contribution, ❖ Community.

❖ Available Algorithms#

PyPOTS supports imputation, classification, clustering, forecasting, and anomaly detection tasks on multivariate partially-observed time series with missing values. The table below shows the availability of each algorithm in PyPOTS for different tasks. The symbol ✅ indicates the algorithm is available for the corresponding task (note that models will be continuously updated in the future to handle tasks that are not currently supported. Stay tuned❗️). The task types are abbreviated as follows: IMPU: Imputation; FORE: Forecasting; CLAS: Classification; CLUS: Clustering; ANOD: Anomaly Detection. The paper references are all listed at the bottom of this readme file.

🌟 Since v0.2, all neural-network models in PyPOTS has got hyperparameter-optimization support. This functionality is implemented with the Microsoft NNI framework. You may want to refer to our time-series imputation survey repo Awesome_Imputation to see how to config and tune the hyperparameters.

🔥 Note that Transformer, Crossformer, PatchTST, DLinear, ETSformer, FEDformer, Informer, Autoformer are not proposed as imputation methods in their original papers, and they cannot accept POTS as input. To make them applicable on POTS data, we apply the embedding strategy and training approach (ORT+MIT) the same as we did in SAITS paper.

Type	Algorithm	IMPU	FORE	CLAS	CLUS	Year
Neural Net	SAITS [1]	✅				2023
Neural Net	Crossformer [2]	✅				2023
Neural Net	TimesNet [3]	✅				2023
Neural Net	PatchTST [2]	✅				2023
Neural Net	DLinear [4]	✅				2023
Neural Net	ETSformer [5]	✅				2023
Neural Net	FEDformer [6]	✅				2022
Neural Net	Raindrop [7]			✅		2022
Neural Net	Informer [8]	✅				2021
Neural Net	Autoformer [9]	✅				2021
Neural Net	CSDI [10]	✅	✅			2021
Neural Net	US-GAN [11]	✅				2021
Neural Net	CRLI [12]				✅	2021
Probabilistic	BTTF [13]		✅			2021
Neural Net	GP-VAE [14]	✅				2020
Neural Net	VaDER [15]				✅	2019
Neural Net	M-RNN [16]	✅				2019
Neural Net	BRITS [17]	✅		✅		2018
Neural Net	GRU-D [18]			✅		2018
Neural Net	Transformer [19]	✅				2017
Naive	LOCF/NOCB	✅				/
Naive	Median	✅				/
Naive	Mean	✅				/

❖ PyPOTS Ecosystem#

At PyPOTS, things are related to coffee, which we’re familiar with. Yes, this is a coffee universe! As you can see, there is a coffee pot in the PyPOTS logo. And what else? Please read on ;-)

👈 Time series datasets are taken as coffee beans at PyPOTS, and POTS datasets are incomplete coffee beans with missing parts that have their own meanings. To make various public time-series datasets readily available to users, Time Series Data Beans (TSDB) is created to make loading time-series datasets super easy! Visit TSDB right now to know more about this handy tool 🛠, and it now supports a total of 168 open-source datasets!

👉 To simulate the real-world data beans with missingness, the ecosystem library PyGrinder, a toolkit helping grind your coffee beans into incomplete ones, is created. Missing patterns fall into three categories according to Robin’s theory [20]: MCAR (missing completely at random), MAR (missing at random), and MNAR (missing not at random). PyGrinder supports all of them and additional functionalities related to missingness. With PyGrinder, you can introduce synthetic missing values into your datasets with a single line of code.

👈 Now we have the beans, the grinder, and the pot, how to brew us a cup of coffee? Tutorials are necessary! Considering the future workload, PyPOTS tutorials is released in a single repo, and you can find them in BrewPOTS. Take a look at it now, and learn how to brew your POTS datasets!

☕️ Welcome to the universe of PyPOTS. Enjoy it and have fun!

❖ Installation#

PyPOTS is available on both PyPI and Anaconda.

Refer to the page Installation to see different ways of installing PyPOTS.

❖ Usage#

Besides BrewPOTS, you can also find a simple and quick-start tutorial notebook on Google Colab with this link. You can also raise an issue or ask in our community.

Additionally, we present you a usage example of imputing missing values in time series with PyPOTS in Section Quick-start Examples, you can click it to view.

❖ 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.

The paper introducing PyPOTS 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. 🤗

@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},
}

Wenjie Du. (2023). PyPOTS: a Python toolbox for data mining on Partially-Observed Time Series. arXiv, abs/2305.18811. https://doi.org/10.48550/arXiv.2305.18811

❖ Contribution#

You’re very welcome to contribute to this exciting project!

By committing your code, you’ll

make your well-established model out-of-the-box for PyPOTS users to run, and help your work obtain more exposure and impact. Take a look at our inclusion criteria. You can utilize the template folder in each task package (e.g. pypots/imputation/template) to quickly start;
become one of PyPOTS contributors and be listed as a volunteer developer on the PyPOTS website;
get mentioned in our release notes;

You can also contribute to PyPOTS by simply staring🌟 this repo to help more people notice it. Your star is your recognition to PyPOTS, and it matters!

The lists of PyPOTS stargazers and forkers are shown below, and we’re so proud to have more and more awesome users, as well as more bright ✨stars:

👀 Check out a full list of our users’ affiliations on PyPOTS website here !

❖ Community#

We care about the feedback from our users, so we’re building PyPOTS community on

Slack. General discussion, Q&A, and our development team are here;
LinkedIn. Official announcements and news are here;
WeChat (微信公众号). We also run a group chat on WeChat, and you can get the QR code from the official account after following it;

If you have any suggestions or want to contribute ideas or share time-series related papers, join us and tell. PyPOTS community is open, transparent, and surely friendly. Let’s work together to build and improve PyPOTS!