Merge pull request #33 from WenjieDu/dev

Update README.md and requirement.txt

README.md

@@ -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)                                  | -    | -         |
@@ -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>

requirements.txt

@@ -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