The tutorials help you brew Partially-Observed Time Series
In this project, partially observed time series datasets are taken as coffee beans.
As you can see, there is a coffee pot in the PyPOTS logo on the left,
and the tutorials in this repo are going to show you how to use this pot (i.e., PyPOTS) to brew your
coffee beans (i.e., POTS data) into a cup of delicious coffee (i.e., what you want).
The tutorials here are for PyPOTS users to quick start their practice, not for achieving the state-of-the-art performance. So we didn't fine-tune the hyperparameters of models in the tutorials. You can tune the hyperparameters by yourself to get better performance on the tutorial dataset PhysioNet-2012 or on your own datasets.
Besides BrewPOTS, you can also find a simple and quick-start tutorial notebook on Google Colab
Enjoy it! ☕️ And have fun!
The paper introducing PyPOTS is available on arXiv, A short version of it is accepted by the 9th SIGKDD international workshop on Mining and Learning from Time Series (MiLeTS'23)). Additionally, PyPOTS has been included as a PyTorch Ecosystem project. 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 this repository to make others notice this library. 🤗
There are scientific research projects using PyPOTS and referencing in their papers. Here is an incomplete list of them.
@article{du2023pypots,
title={{PyPOTS: a Python toolbox for data mining on Partially-Observed Time Series}},
author={Wenjie Du},
journal={arXiv preprint arXiv:2305.18811},
year={2023},
}or
Wenjie Du. PyPOTS: a Python toolbox for data mining on Partially-Observed Time Series. arXiv, abs/2305.18811, 2023.