PyPOTS Ecosystem Pipeline
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 ;-)

TSDB (Time Series Data Beans)

The library helps load public time-series datasets.

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 open-source time-series datasets readily available to our users, PyPOTS gets supported by its ecosystem library Time Series Data Beans (TSDB), a toolbox that makes loading time-series datasets super easy! Visit TSDB right now to learn more about this handy tool 🛠, and it now supports a total of 168 open-source datasets!


The toolbox for grinding time-series data beans to produce missingness.

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: 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 missing values. With PyGrinder, you can introduce synthetic missing values into your datasets with a single line of code.


The kit for benchmarking machine learning on Partially-Observed Time Series

To evaluate the performance of algorithms on POTS datasets, a benchmarking suite 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.


The toolkit for machine learning on partially-observed time series.

With ground data beans (i.e. processed datasets), PyPOTS - our coffee pot is the tool here to help brew them into a cup of delicious coffee, namely the advanced analysis results that we want. PyPOTS includes dozens of algorithms, supporting imputation, classification, clustering, and forecasting tasks on POTS data end-to-end. More state-of-the-art algorithms are on the way. Since 2022, PyPOTS has been successfully used in scientific researches and has been cited and referenced. Here is an incomplete list of them.


The repository for tutorials about brewing POTS datasets.

Now the beans, grinder, and pot are ready, please have a seat on the bench and let's think about how to brew us a cup of coffee. Tutorials are necessary! Considering the future workload, PyPOTS tutorials are 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!

We are PyPOTS