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WenjieDu · Apr 2, 2024 · 01ede74 · 01ede74
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diff --git a/docs/index.rst b/docs/index.rst
@@ -185,7 +185,7 @@ Imputation                     Neural Net       Informer (Beyond Efficient Trans
 Imputation                     Neural Net       Autoformer (Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting)            2021   :cite:`wu2021autoformer`
 Imputation                     Neural Net       US-GAN (Unsupervised GAN for Multivariate Time Series Imputation)                                         2021   :cite:`miao2021SSGAN`
 Imputation                     Neural Net       CSDI (Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation)                  2021   :cite:`tashiro2021csdi`
-Imputation                     Neural Net       GP-VAE (Gaussian Process Variational Autoencoder)                                                         2020   :cite:`fortuin2020GPVAEDeep`
+Imputation                     Neural Net       GP-VAE (Gaussian Process Variational Autoencoder)                                                         2020   :cite:`fortuin2020gpvae`
 Imputation, Classification     Neural Net       BRITS (Bidirectional Recurrent Imputation for Time Series)                                                2018   :cite:`cao2018BRITS`
 Imputation                     Neural Net       M-RNN (Multi-directional Recurrent Neural Network)                                                        2019   :cite:`yoon2019MRNN`
 Imputation                     Naive            LOCF/NOCB(Last Observation Carried Forward/Next Observation Carried Backward)                             /      /

diff --git a/docs/references.bib b/docs/references.bib
@@ -123,18 +123,14 @@ @article{du2023SAITS
 url = {https://arxiv.org/abs/2202.08516},
 author = {Wenjie Du and David Cote and Yan Liu},
 }
-@article{fortuin2020GPVAEDeep,
-title = {{{GP-VAE}}: {{Deep Probabilistic Time Series Imputation}}},
-author = {Fortuin, Vincent and Baranchuk, Dmitry and R{\"a}tsch, Gunnar and Mandt, Stephan},
-year = {2020},
-month = feb,
-journal = {arXiv:1907.04155 [cs, stat]},
-eprint = {1907.04155},
-eprinttype = {arxiv},
-primaryclass = {cs, stat},
-url = {http://arxiv.org/abs/1907.04155},
-archiveprefix = {arXiv},
-keywords = {Computer Science - Machine Learning,Statistics - Machine Learning}
+
+@inproceedings{fortuin2020gpvae,
+title={{GP-VAE: Deep probabilistic time series imputation}},
+author={Fortuin, Vincent and Baranchuk, Dmitry and R{\"a}tsch, Gunnar and Mandt, Stephan},
+booktitle={International conference on artificial intelligence and statistics},
+pages={1651--1661},
+year={2020},
+organization={PMLR}
 }
 
 @article{horn2019SeFT,
@@ -382,21 +378,14 @@ @article{yuan2019E2GAN
 url = {https://www.ijcai.org/Proceedings/2019/429}
 }
 
-@article{zhang2022Raindrop,
-title = {Graph-{{Guided Network}} for {{Irregularly Sampled Multivariate Time Series}}},
-author = {Zhang, Xiang and Zeman, Marko and Tsiligkaridis, Theodoros and Zitnik, Marinka},
-year = {2022},
-month = mar,
-journal = {arXiv:2110.05357 [cs]},
-eprint = {2110.05357},
-eprinttype = {arxiv},
-primaryclass = {cs},
-url = {http://arxiv.org/abs/2110.05357},
-archiveprefix = {arXiv},
-keywords = {Computer Science - Artificial Intelligence,Computer Science - Machine Learning}
+@inproceedings{zhang2022Raindrop,
+title={Graph-Guided Network for Irregularly Sampled Multivariate Time Series},
+author={Xiang Zhang and Marko Zeman and Theodoros Tsiligkaridis and Marinka Zitnik},
+booktitle={International Conference on Learning Representations},
+year={2022},
+url={https://openreview.net/forum?id=Kwm8I7dU-l5}
 }
 
-
 @inproceedings{reddi2018OnTheConvergence,
 title={On the Convergence of Adam and Beyond},
 author={Sashank J. Reddi and Satyen Kale and Sanjiv Kumar},

diff --git a/pypots/classification/brits/__init__.py b/pypots/classification/brits/__init__.py
@@ -1,8 +1,11 @@
 """
 The package of the partially-observed time-series classification model BRITS.
 
-Refer to the paper "Cao, W., Wang, D., Li, J., Zhou, H., Li, L., & Li, Y. (2018).
-BRITS: Bidirectional Recurrent Imputation for Time Series. NeurIPS 2018."
+Refer to the paper
+`Wei Cao, Dong Wang, Jian Li, Hao Zhou, Lei Li, and Yitan Li.
+BRITS: Bidirectional recurrent imputation for time series.
+In Advances in Neural Information Processing Systems, volume 31. Curran Associates, Inc., 2018.
+<https://papers.nips.cc/paper_files/paper/2018/file/734e6bfcd358e25ac1db0a4241b95651-Paper.pdf>`_
 
 """
 

diff --git a/pypots/classification/brits/model.py b/pypots/classification/brits/model.py
@@ -1,13 +1,14 @@
 """
 The implementation of BRITS for the partially-observed time-series classification task.
 
-Refer to the paper "Cao, W., Wang, D., Li, J., Zhou, H., Li, L., & Li, Y. (2018).
-BRITS: Bidirectional Recurrent Imputation for Time Series. NeurIPS 2018."
+Refer to the paper "Wei Cao, Dong Wang, Jian Li, Hao Zhou, Lei Li, and Yitan Li.
+BRITS: Bidirectional recurrent imputation for time series.
+In Advances in Neural Information Processing Systems, volume 31. Curran Associates, Inc., 2018."
 
 Notes
 -----
-Partial implementation uses code from https://github.com/caow13/BRITS. The bugs in the original implementation
-are fixed here.
+Partial implementation uses code from https://github.com/caow13/BRITS.
+The bugs in the original implementation are fixed here.
 
 """
 
@@ -90,13 +91,6 @@ class BRITS(BaseNNClassifier):
         better than in previous epochs.
         The "all" strategy will save every model after each epoch training.
 
-    References
-    ----------
-    .. [1] `Cao, Wei, Dong Wang, Jian Li, Hao Zhou, Lei Li, and Yitan Li.
-        "Brits: Bidirectional recurrent imputation for time series."
-        Advances in neural information processing systems 31 (2018).
-        <https://arxiv.org/pdf/1805.10572>`_
-
     """
 
     def __init__(

diff --git a/pypots/classification/grud/__init__.py b/pypots/classification/grud/__init__.py
@@ -1,8 +1,11 @@
 """
 The package of the partially-observed time-series classification model GRU-D.
 
-Refer to the paper "Che, Z., Purushotham, S., Cho, K., Sontag, D.A., & Liu, Y. (2018).
-Recurrent Neural Networks for Multivariate Time Series with Missing Values. Scientific Reports."
+Refer to the paper
+`Zhengping Che, Sanjay Purushotham, Kyunghyun Cho, David Sontag, and Yan Liu.
+Recurrent Neural Networks for Multivariate Time Series with Missing Values.
+Scientific Reports, 8(1):6085, April 2018.
+<https://www.nature.com/articles/s41598-018-24271-9.pdf>`_
 
 """
 

diff --git a/pypots/classification/grud/model.py b/pypots/classification/grud/model.py
@@ -1,8 +1,9 @@
 """
 The implementation of GRU-D for the partially-observed time-series imputation task.
 
-Refer to the paper "Che, Z., Purushotham, S., Cho, K., Sontag, D.A., & Liu, Y. (2018).
-Recurrent Neural Networks for Multivariate Time Series with Missing Values. Scientific Reports."
+Refer to the paper "Zhengping Che, Sanjay Purushotham, Kyunghyun Cho, David Sontag, and Yan Liu.
+Recurrent Neural Networks for Multivariate Time Series with Missing Values.
+Scientific Reports, 8(1):6085, April 2018."
 
 """
 
@@ -80,13 +81,6 @@ class GRUD(BaseNNClassifier):
         better than in previous epochs.
         The "all" strategy will save every model after each epoch training.
 
-    References
-    ----------
-    .. [1] `Che, Zhengping, Sanjay Purushotham, Kyunghyun Cho, David Sontag, and Yan Liu.
-        "Recurrent neural networks for multivariate time series with missing values."
-        Scientific reports 8, no. 1 (2018): 6085.
-        <https://www.nature.com/articles/s41598-018-24271-9.pdf>`_
-
     """
 
     def __init__(

diff --git a/pypots/classification/raindrop/__init__.py b/pypots/classification/raindrop/__init__.py
@@ -1,8 +1,11 @@
 """
 The package of the partially-observed time-series classification model Raindrop.
 
-Refer to the paper "Zhang, X., Zeman, M., Tsiligkaridis, T., & Zitnik, M. (2022).
-Graph-Guided Network for Irregularly Sampled Multivariate Time Series. ICLR 2022."
+Refer to the paper
+`Xiang Zhang, Marko Zeman, Theodoros Tsiligkaridis, and Marinka Zitnik.
+Graph-guided network for irregularly sampled multivariate time series.
+In ICLR, 2022.
+<https://openreview.net/forum?id=Kwm8I7dU-l5>`_
 
 """
 

diff --git a/pypots/classification/raindrop/model.py b/pypots/classification/raindrop/model.py
@@ -1,8 +1,9 @@
 """
 The implementation of Raindrop for the partially-observed time-series classification task.
 
-Refer to the paper "Zhang, X., Zeman, M., Tsiligkaridis, T., & Zitnik, M. (2022).
-Graph-Guided Network for Irregularly Sampled Multivariate Time Series. ICLR 2022."
+Refer to the paper "Xiang Zhang, Marko Zeman, Theodoros Tsiligkaridis, and Marinka Zitnik.
+Graph-guided network for irregularly sampled multivariate time series.
+In ICLR, 2022."
 
 """
 
@@ -106,12 +107,6 @@ class Raindrop(BaseNNClassifier):
         better than in previous epochs.
         The "all" strategy will save every model after each epoch training.
 
-    References
-    ----------
-    .. [1] `Zhang, Xiang, Marko Zeman, Theodoros Tsiligkaridis, and Marinka Zitnik.
-        "Graph-guided network for irregularly sampled multivariate time series."
-        International Conference on Learning Representations (ICLR). 2022.
-        <https://openreview.net/forum?id=Kwm8I7dU-l5>`_
     """
 
     def __init__(

diff --git a/pypots/clustering/crli/__init__.py b/pypots/clustering/crli/__init__.py
@@ -1,8 +1,12 @@
 """
 The package of the partially-observed time-series clustering model CRLI.
 
-Refer to the paper "Ma, Q., Chen, C., Li, S., & Cottrell, G. W. (2021).
-Learning Representations for Incomplete Time Series Clustering. AAAI 2021."
+Refer to the paper
+`Qianli Ma, Chuxin Chen, Sen Li, and Garrison W. Cottrell.
+Learning Representations for Incomplete Time Series Clustering.
+In AAAI, 35(10):8837–8846, May 2021.
+<https://ojs.aaai.org/index.php/AAAI/article/view/17070>`_
+
 
 """
 

diff --git a/pypots/clustering/crli/model.py b/pypots/clustering/crli/model.py
@@ -2,8 +2,9 @@
 The implementation of CRLI (Clustering Representation Learning on Incomplete time-series data) for
 the partially-observed time-series clustering task.
 
-Refer to the paper "Ma, Q., Chen, C., Li, S., & Cottrell, G. W. (2021).
-Learning Representations for Incomplete Time Series Clustering. AAAI 2021."
+Refer to the paper "Qianli Ma, Chuxin Chen, Sen Li, and Garrison W. Cottrell.
+Learning Representations for Incomplete Time Series Clustering.
+In AAAI, 35(10):8837–8846, May 2021."
 
 """
 
@@ -109,14 +110,6 @@ class CRLI(BaseNNClusterer):
         better than in previous epochs.
         The "all" strategy will save every model after each epoch training.
 
-    References
-    ----------
-    .. [1] `Ma, Qianli, Chuxin Chen, Sen Li, and Garrison W. Cottrell. 2021.
-        "Learning Representations for Incomplete Time Series Clustering".
-        Proceedings of the AAAI Conference on Artificial Intelligence 35 (10):8837-46.
-        https://doi.org/10.1609/aaai.v35i10.17070.
-        <https://ojs.aaai.org/index.php/AAAI/article/view/17070>`_
-
     """
 
     def __init__(

diff --git a/pypots/clustering/vader/__init__.py b/pypots/clustering/vader/__init__.py
@@ -1,9 +1,12 @@
 """
 The package of the partially-observed time-series clustering model VaDER.
 
-Refer to the paper "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."
+Refer to the paper
+`Johann de Jong, Mohammad Asif Emon, Ping Wu, Reagon Karki, Meemansa Sood, Patrice Godard, Ashar Ahmad, Henri Vrooman,
+Martin Hofmann-Apitius, and Holger Fröhlich.
+Deep learning for clustering of multivariate clinical patient trajectories with missing values.
+GigaScience, 8(11):giz134, November 2019.
+<https://academic.oup.com/gigascience/article-pdf/8/11/giz134/30797160/giz134.pdf>`_
 
 """
 

diff --git a/pypots/clustering/vader/model.py b/pypots/clustering/vader/model.py
@@ -1,9 +1,10 @@
 """
 The implementation of VaDER for the partially-observed time-series clustering task.
 
-Refer to the paper "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."
+Refer to the paper "Johann de Jong, Mohammad Asif Emon, Ping Wu, Reagon Karki, Meemansa Sood, Patrice Godard,
+Ashar Ahmad, Henri Vrooman, Martin Hofmann-Apitius, and Holger Fröhlich.
+Deep learning for clustering of multivariate clinical patient trajectories with missing values.
+GigaScience, 8(11):giz134, November 2019."
 
 """
 
@@ -92,16 +93,6 @@ class VaDER(BaseNNClusterer):
         better than in previous epochs.
         The "all" strategy will save every model after each epoch training.
 
-    References
-    ----------
-    .. [1] `de Jong, Johann, Mohammad Asif Emon, Ping Wu, Reagon Karki, Meemansa Sood, Patrice Godard,
-    Ashar Ahmad, Henri Vrooman, Martin Hofmann-Apitius, and Holger Fröhlich.
-    "Deep learning for clustering of multivariate clinical patient trajectories with missing values."
-    GigaScience 8, no. 11 (2019): giz134.
-    <https://academic.oup.com/gigascience/article-pdf/8/11/giz134/30797160/giz134.pdf>`_
-
-
-
     """
 
     def __init__(

diff --git a/pypots/forecasting/bttf/__init__.py b/pypots/forecasting/bttf/__init__.py
@@ -1,9 +1,11 @@
 """
 The package of the partially-observed time-series forecasting model BTTF.
 
-Refer to the paper "Chen, X., & Sun, L. (2021).
+Refer to the paper
+`Xinyu Chen and Lijun Sun.
 Bayesian Temporal Factorization for Multidimensional Time Series Prediction.
-IEEE transactions on pattern analysis and machine intelligence."
+IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 1–1, 2021.
+<https://arxiv.org/pdf/1910.06366>`_
 
 """
 

diff --git a/pypots/forecasting/bttf/model.py b/pypots/forecasting/bttf/model.py
@@ -2,9 +2,9 @@
 The implementation of BTTF (Bayesian Temporal Tensor Factorization) for the partially-observed time-series
 forecasting task.
 
-Refer to the paper "Chen, X., & Sun, L. (2021).
+Refer to the paper "Xinyu Chen and Lijun Sun.
 Bayesian Temporal Factorization for Multidimensional Time Series Prediction.
-IEEE transactions on pattern analysis and machine intelligence."
+IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 1–1, 2021."
 
 Notes
 -----
@@ -69,13 +69,6 @@ class BTTF(BaseForecaster):
 
     2). ``n_steps - pred_step`` must be larger than ``max(time_lags)``;
 
-    References
-    ----------
-    .. [1] `Chen, Xinyu, and Lijun Sun.
-        "Bayesian temporal factorization for multidimensional time series prediction."
-        IEEE Transactions on Pattern Analysis and Machine Intelligence 44, no. 9 (2021): 4659-4673.
-        <https://arxiv.org/pdf/1910.06366>`_
-
     """
 
     def __init__(

diff --git a/pypots/imputation/autoformer/__init__.py b/pypots/imputation/autoformer/__init__.py
@@ -1,8 +1,11 @@
 """
 The package of the partially-observed time-series imputation model Autoformer.
 
-Refer to the paper "Wu, H., Xu, J., Wang, J., & Long, M. (2021).
-Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting. NeurIPS 2021.".
+Refer to the paper
+`Haixu Wu, Jiehui Xu, Jianmin Wang, and Mingsheng Long.
+Autoformer: Decomposition transformers with autocorrelation for long-term series forecasting.
+In Advances in Neural Information Processing Systems, volume 34, pages 22419–22430. Curran Associates, Inc., 2021.
+<https://proceedings.neurips.cc/paper/2021/file/bcc0d400288793e8bdcd7c19a8ac0c2b-Paper.pdf>`_
 
 """
 

diff --git a/pypots/imputation/autoformer/model.py b/pypots/imputation/autoformer/model.py
@@ -1,12 +1,13 @@
 """
 The implementation of Autoformer for the partially-observed time-series imputation task.
 
-Refer to the paper "Wu, H., Xu, J., Wang, J., & Long, M. (2021).
-Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting. NeurIPS 2021.".
+Refer to the paper "Haixu Wu, Jiehui Xu, Jianmin Wang, and Mingsheng Long.
+Autoformer: Decomposition transformers with autocorrelation for long-term series forecasting.
+In Advances in Neural Information Processing Systems, volume 34, pages 22419–22430. Curran Associates, Inc., 2021."
 
 Notes
 -----
-Partial implementation uses code from https://github.com/thuml/Time-Series-Library
+Partial implementation uses code from https://github.com/thuml/Autoformer
 
 """
 
@@ -101,13 +102,6 @@ class Autoformer(BaseNNImputer):
         better than in previous epochs.
         The "all" strategy will save every model after each epoch training.
 
-    References
-    ----------
-    .. [1] `Wu, Haixu, Jiehui Xu, Jianmin Wang, and Mingsheng Long.
-        "Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting".
-        Advances in neural information processing systems 34 (2021): 22419-22430.
-        <https://proceedings.neurips.cc/paper/2021/file/bcc0d400288793e8bdcd7c19a8ac0c2b-Paper.pdf>`_
-
     """
 
     def __init__(

diff --git a/pypots/imputation/brits/__init__.py b/pypots/imputation/brits/__init__.py
@@ -1,8 +1,11 @@
 """
 The package of the partially-observed time-series imputation model BRITS.
 
-Refer to the paper "Cao, W., Wang, D., Li, J., Zhou, H., Li, L., & Li, Y. (2018).
-BRITS: Bidirectional Recurrent Imputation for Time Series. NeurIPS 2018."
+Refer to the paper
+`Wei Cao, Dong Wang, Jian Li, Hao Zhou, Lei Li, and Yitan Li.
+BRITS: Bidirectional recurrent imputation for time series.
+In Advances in Neural Information Processing Systems, volume 31. Curran Associates, Inc., 2018.
+<https://papers.nips.cc/paper_files/paper/2018/file/734e6bfcd358e25ac1db0a4241b95651-Paper.pdf>`_
 
 """