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Update README.md and requirement.txt #33

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Update README.md and requirement.txt #33

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doc: update README;
WenjieDu Jan 8, 2023
456293a
doc: update README;
WenjieDu Feb 8, 2023
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feat: add limitations on lib dependencies;
WenjieDu Jan 17, 2023
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18 changes: 9 additions & 9 deletions README.md
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Expand Up @@ -85,7 +85,7 @@ mae = cal_mae(imputation, X_intact, indicating_mask) # calculate mean absolute
## ❖ Available Algorithms
| Task | Type | Algorithm | Year | Reference |
|-------------------------------|----------------|--------------------------------------------------------------------------|------|-----------|
| Imputation | Neural Network | SAITS (Self-Attention-based Imputation for Time Series) | 2022 | [^1] |
| Imputation | Neural Network | SAITS (Self-Attention-based Imputation for Time Series) | 2023 | [^1] |
| Imputation | Neural Network | Transformer | 2017 | [^2] [^1] |
| Imputation,<br>Classification | Neural Network | BRITS (Bidirectional Recurrent Imputation for Time Series) | 2018 | [^3] |
| Imputation | Naive | LOCF (Last Observation Carried Forward) | - | - |
Expand Down Expand Up @@ -120,14 +120,14 @@ The documentation and tutorials are under construction. And a short paper introd
Thank you all for your attention! 😃


[^1]: 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.
[^3]: Cao, W., Wang, D., Li, J., Zhou, H., Li, L., & Li, Y. (2018). BRITS: Bidirectional Recurrent Imputation for Time Series. 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. Scientific Reports, 8.
[^5]: Zhang, X., Zeman, M., Tsiligkaridis, T., & Zitnik, M. (2022). Graph-Guided Network for Irregularly Sampled Multivariate Time Series. ICLR 2022.
[^6]: Ma, Q., Chen, C., Li, S., & Cottrell, G. W. (2021). Learning Representations for Incomplete Time Series Clustering. 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. GigaScience, 8.
[^8]: Chen, X., & Sun, L. (2021). Bayesian Temporal Factorization for Multidimensional Time Series Prediction. IEEE transactions on pattern analysis and machine intelligence, PP.
[^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.
[^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, 8.
[^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.
[^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, PP.

<details>
<summary>🏠 Visits</summary>
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6 changes: 3 additions & 3 deletions requirements.txt
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@@ -1,8 +1,8 @@
matplotlib
numpy
scikit_learn
scipy
numpy >= 1.23.3
scikit_learn >= 0.24.1
torch == 1.11.0
scipy
tensorboard
pandas
pycorruptor
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