⦿ 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.
Install the latest release from PyPI:
pip install pypots
Install with the latest code on GitHub:
pip install
https://github.com/WenjieDu/PyPOTS/archive/main.zip
| Task | Type | Algorithm | Year | Reference |
|---|---|---|---|---|
| Imputation | Neural Network | SAITS: Self-Attention-based Imputation for Time Series | 2022 | 1 |
| Imputation | Neural Network | Transformer | 2017 | 2 1 |
| Imputation, Classification |
Neural Network | BRITS (Bidirectional Recurrent Imputation for Time Series) | 2018 | 3 |
| Imputation | Naive | LOCF (Last Observation Carried Forward) | - | - |
| Classification | Neural Network | GRU-D | 2018 | 4 |
| Classification | Neural Network | Raindrop | 2022 | 5 |
| Clustering | Neural Network | CRLI (Clustering Representation Learning on Incomplete time-series data) | 2021 | 6 |
| Clustering | Neural Network | VaDER (Variational Deep Embedding with Recurrence) | 2019 | 7 |
Thank you all for your attention! 😃
Footnotes
-
Du, W., Cote, D., & Liu, Y. (2022). SAITS: Self-Attention-based Imputation for Time Series. ArXiv, abs/2202.08516. ↩ ↩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. NeurIPS 2017. ↩
-
Cao, W., Wang, D., Li, J., Zhou, H., Li, L., & Li, Y. (2018). BRITS: Bidirectional Recurrent Imputation for Time Series. NeurIPS 2018. ↩
-
Che, Z., Purushotham, S., Cho, K., Sontag, D.A., & Liu, Y. (2018). Recurrent Neural Networks for Multivariate Time Series with Missing Values. Scientific Reports, 8. ↩
-
Zhang, X., Zeman, M., Tsiligkaridis, T., & Zitnik, M. (2022). Graph-Guided Network for Irregularly Sampled Multivariate Time Series. ICLR 2022. ↩
-
Ma, Q., Chen, C., Li, S., & Cottrell, G. W. (2021). Learning Representations for Incomplete Time Series Clustering. AAAI 2021. ↩
-
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. GigaScience, 8. ↩