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WenjieDu authored Mar 29, 2023
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26 changes: 17 additions & 9 deletions README.md
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<p align='center'>
<!-- Python version -->
<img src='https://img.shields.io/badge/python-v3-yellowgreen'>
<img src='https://img.shields.io/badge/python-v3-yellow'>
<!-- PyTorch-->
<img src='https://img.shields.io/static/v1?label=%E2%9D%A4%EF%B8%8F&message=PyTorch&color=DC583A'>
<img src='https://img.shields.io/static/v1?label=PyTorch&message=%E2%9D%A4%EF%B8%8F&color=DC583A&logo=pytorch'>
<!-- PyPI version -->
<a alt='PyPI download number' href='https://pypi.org/project/pypots'>
<img alt="PyPI" src="https://img.shields.io/pypi/v/pypots?color=green&label=PyPI">
<img alt="PyPI" src="https://img.shields.io/pypi/v/pypots?color=yellowgreen&label=PyPI&logo=pypi&logoColor=white">
</a>
<!-- on Anaconda -->
<a alt='on anaconda' href='https://anaconda.org/conda-forge/pypots'>
<img alt="on anaconda" src="https://img.shields.io/conda/vn/conda-forge/pypots?color=green&label=Conda&logo=anaconda" />
</a>
<!-- License -->
<a alt='GPL3 license' href='https://github.com/WenjieDu/PyPOTS/blob/main/LICENSE'>
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</a>
<!-- PyPI download number -->
<a alt='PyPI download number' href='https://pepy.tech/project/pypots'>
<img src='https://static.pepy.tech/personalized-badge/pypots?period=total&units=none&left_color=grey&right_color=blue&left_text=Downloads'>
<img src='https://static.pepy.tech/personalized-badge/pypots?period=total&units=international_system&left_color=grey&right_color=blue&left_text=Downloads'>
</a>
<!-- Zenodo DOI -->
<a alt='Zenodo DOI' href='https://zenodo.org/badge/latestdoi/475477908'>
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<br clear='left'>

## ❖ Installation
PyPOTS now is available on <a alt='Anaconda' href='https://anaconda.org/conda-forge/pypots'><img align='center' src='https://img.shields.io/badge/Anaconda--lightgreen?style=social&logo=anaconda'></a>❗️

Install it with `conda install pypots`, you may need to specify the channel with option `-c conda-forge`

Install the latest release from PyPI:
> pip install pypots
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[^1]: Du, W., Cote, D., & Liu, Y. (2023). [SAITS: Self-Attention-based Imputation for Time Series](https://doi.org/10.1016/j.eswa.2023.119619). *Expert systems with applications*.
[^2]: Vaswani, A., Shazeer, N.M., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., & Polosukhin, I. (2017). [Attention is All you Need](https://papers.nips.cc/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html). *NeurIPS* 2017.
[^3]: Cao, W., Wang, D., Li, J., Zhou, H., Li, L., & Li, Y. (2018). [BRITS: Bidirectional Recurrent Imputation for Time Series](https://papers.nips.cc/paper/2018/hash/734e6bfcd358e25ac1db0a4241b95651-Abstract.html). *NeurIPS* 2018.
[^2]: Vaswani, A., Shazeer, N.M., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., & Polosukhin, I. (2017). [Attention is All you Need](https://papers.nips.cc/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html). *NeurIPS 2017*.
[^3]: Cao, W., Wang, D., Li, J., Zhou, H., Li, L., & Li, Y. (2018). [BRITS: Bidirectional Recurrent Imputation for Time Series](https://papers.nips.cc/paper/2018/hash/734e6bfcd358e25ac1db0a4241b95651-Abstract.html). *NeurIPS 2018*.
[^4]: Che, Z., Purushotham, S., Cho, K., Sontag, D.A., & Liu, Y. (2018). [Recurrent Neural Networks for Multivariate Time Series with Missing Values](https://www.nature.com/articles/s41598-018-24271-9). *Scientific Reports*.
[^5]: Zhang, X., Zeman, M., Tsiligkaridis, T., & Zitnik, M. (2022). [Graph-Guided Network for Irregularly Sampled Multivariate Time Series](https://arxiv.org/abs/2110.05357). *ICLR* 2022.
[^6]: Ma, Q., Chen, C., Li, S., & Cottrell, G. W. (2021). [Learning Representations for Incomplete Time Series Clustering](https://ojs.aaai.org/index.php/AAAI/article/view/17070). *AAAI* 2021.
[^5]: Zhang, X., Zeman, M., Tsiligkaridis, T., & Zitnik, M. (2022). [Graph-Guided Network for Irregularly Sampled Multivariate Time Series](https://arxiv.org/abs/2110.05357). *ICLR 2022*.
[^6]: Ma, Q., Chen, C., Li, S., & Cottrell, G. W. (2021). [Learning Representations for Incomplete Time Series Clustering](https://ojs.aaai.org/index.php/AAAI/article/view/17070). *AAAI 2021*.
[^7]: Jong, J.D., Emon, M.A., Wu, P., Karki, R., Sood, M., Godard, P., Ahmad, A., Vrooman, H.A., Hofmann-Apitius, M., & Fröhlich, H. (2019). [Deep learning for clustering of multivariate clinical patient trajectories with missing values](https://academic.oup.com/gigascience/article/8/11/giz134/5626377). *GigaScience*.
[^8]: Chen, X., & Sun, L. (2021). [Bayesian Temporal Factorization for Multidimensional Time Series Prediction](https://arxiv.org/abs/1910.06366). *IEEE transactions on pattern analysis and machine intelligence*.

<details>
<summary>🏠 Visits</summary>
<img align='left' src='https://hits.seeyoufarm.com/api/count/incr/badge.svg?url=https%3A%2F%2Fgithub.com%2FPyPOTS%2FPyPOTS&count_bg=%23009A0A&title_bg=%23555555&icon=&icon_color=%23E7E7E7&title=Visits+since+April+2022&edge_flat=false'>
<img align='left' src='https://hits.seeyoufarm.com/api/count/incr/badge.svg?url=https%3A%2F%2Fgithub.com%2FPyPOTS%2FPyPOTS&count_bg=%23009A0A&title_bg=%23555555&icon=&icon_color=%23E7E7E7&title=Hits&edge_flat=false'>
</details>

4 changes: 2 additions & 2 deletions docs/index.rst
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============================== ================ ========================================================================= ====== =========
Task Type Algorithm Year Reference
============================== ================ ========================================================================= ====== =========
Imputation Neural Network SAITS (Self-Attention-based Imputation for Time Series) 2022 :cite:`du2022SAITS`
Imputation Neural Network Transformer 2017 :cite:`vaswani2017Transformer`, :cite:`du2022SAITS`
Imputation Neural Network SAITS (Self-Attention-based Imputation for Time Series) 2022 :cite:`du2023SAITS`
Imputation Neural Network Transformer 2017 :cite:`vaswani2017Transformer`, :cite:`du2023SAITS`
Imputation, Classification Neural Network BRITS (Bidirectional Recurrent Imputation for Time Series) 2018 :cite:`cao2018BRITS`
Imputation Naive LOCF (Last Observation Carried Forward) / /
Classification Neural Network GRU-D 2018 :cite:`che2018GRUD`
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