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Add D3R #47

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add D3R
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@ForestsKing
ForestsKing committed Jul 4, 2024
commit afb621c50bf16af6fc8a71c04ab83551bacd371e
21 changes: 11 additions & 10 deletions README.rst
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Expand Up @@ -342,15 +342,16 @@ A Comprehensive Survey on Graph Anomaly Detection with Deep Learning
4.4. Time Series Outlier Detection
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================
Paper Title Venue Year Ref Materials
================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================
Outlier detection for temporal data: A survey TKDE 2014 [#Gupta2014Outlier]_ `[PDF] <https://www.microsoft.com/en-us/research/wp-content/uploads/2014/01/gupta14_tkde.pdf>`_
Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding KDD 2018 [#Hundman2018Detecting]_ `[PDF] <https://arxiv.org/pdf/1802.04431.pdf>`_, `[Code] <https://github.com/khundman/telemanom>`_
Time-Series Anomaly Detection Service at Microsoft KDD 2019 [#Ren2019Time]_ `[PDF] <https://arxiv.org/pdf/1906.03821.pdf>`_
Revisiting Time Series Outlier Detection: Definitions and Benchmarks NeurIPS 2021 [#Lai2021Revisiting]_ `[PDF] <https://openreview.net/pdf?id=r8IvOsnHchr>`_, `[Code] <https://github.com/datamllab/tods/tree/benchmark>`_
Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series ICLR 2022 [#Dai2022Graph]_ `[PDF] <https://openreview.net/pdf?id=45L_dgP48Vd>`_, `[Code] <https://github.com/EnyanDai/GANF>`_
================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================
===================================================================================================================================== ============================ ===== ============================ ==========================================================================================================================================================================
Paper Title Venue Year Ref Materials
===================================================================================================================================== ============================ ===== ============================ ==========================================================================================================================================================================
Outlier detection for temporal data: A survey TKDE 2014 [#Gupta2014Outlier]_ `[PDF] <https://www.microsoft.com/en-us/research/wp-content/uploads/2014/01/gupta14_tkde.pdf>`_
Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding KDD 2018 [#Hundman2018Detecting]_ `[PDF] <https://arxiv.org/pdf/1802.04431.pdf>`_, `[Code] <https://github.com/khundman/telemanom>`_
Time-Series Anomaly Detection Service at Microsoft KDD 2019 [#Ren2019Time]_ `[PDF] <https://arxiv.org/pdf/1906.03821.pdf>`_
Revisiting Time Series Outlier Detection: Definitions and Benchmarks NeurIPS 2021 [#Lai2021Revisiting]_ `[PDF] <https://openreview.net/pdf?id=r8IvOsnHchr>`_, `[Code] <https://github.com/datamllab/tods/tree/benchmark>`_
Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series ICLR 2022 [#Dai2022Graph]_ `[PDF] <https://openreview.net/pdf?id=45L_dgP48Vd>`_, `[Code] <https://github.com/EnyanDai/GANF>`_
Drift doesn't matter: dynamic decomposition with diffusion reconstruction for unstable multivariate time series anomaly detection NeurIPS 2023 [#Wang2023Drift]_ `[PDF] <https://openreview.net/pdf?id=aW5bSuduF1>`_, `[Code] <https://github.com/ForestsKing/D3R>`_
===================================================================================================================================== ============================ ===== ============================ ==========================================================================================================================================================================


4.5. Feature Selection in Outlier Detection
Expand Down Expand Up @@ -860,4 +861,4 @@ References

.. [#Zong2018Deep] Zong, B., Song, Q., Min, M.R., Cheng, W., Lumezanu, C., Cho, D. and Chen, H., 2018. Deep autoencoding gaussian mixture model for unsupervised anomaly detection. International Conference on Learning Representations (ICLR).


.. [#Wang2023Drift] Wang, C., Zhuang, Z., Qi, Q., Wang, J., Wang, X., Sun, H., & Liao, J. (2023). Drift doesn't matter: dynamic decomposition with diffusion reconstruction for unstable multivariate time series anomaly detection. Advances in Neural Information Processing Systems, 36.
18 changes: 12 additions & 6 deletions README_CN.rst
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Expand Up @@ -235,12 +235,16 @@ Anomaly detection in dynamic networks: a survey
4.4. Time Series Outlier Detection
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================
Paper Title Venue Year Ref Materials
================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================
Outlier detection for temporal data: A survey TKDE 2014 [#Gupta2014Outlier]_ `[PDF] <https://www.microsoft.com/en-us/research/wp-content/uploads/2014/01/gupta14_tkde.pdf>`_
Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding KDD 2018 [#Hundman2018Detecting]_ `[PDF] <https://arxiv.org/pdf/1802.04431.pdf>`_, `[Code] <https://github.com/khundman/telemanom>`_
================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================
===================================================================================================================================== ============================ ===== ============================ ==========================================================================================================================================================================
Paper Title Venue Year Ref Materials
===================================================================================================================================== ============================ ===== ============================ ==========================================================================================================================================================================
Outlier detection for temporal data: A survey TKDE 2014 [#Gupta2014Outlier]_ `[PDF] <https://www.microsoft.com/en-us/research/wp-content/uploads/2014/01/gupta14_tkde.pdf>`_
Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding KDD 2018 [#Hundman2018Detecting]_ `[PDF] <https://arxiv.org/pdf/1802.04431.pdf>`_, `[Code] <https://github.com/khundman/telemanom>`_
Time-Series Anomaly Detection Service at Microsoft KDD 2019 [#Ren2019Time]_ `[PDF] <https://arxiv.org/pdf/1906.03821.pdf>`_
Revisiting Time Series Outlier Detection: Definitions and Benchmarks NeurIPS 2021 [#Lai2021Revisiting]_ `[PDF] <https://openreview.net/pdf?id=r8IvOsnHchr>`_, `[Code] <https://github.com/datamllab/tods/tree/benchmark>`_
Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series ICLR 2022 [#Dai2022Graph]_ `[PDF] <https://openreview.net/pdf?id=45L_dgP48Vd>`_, `[Code] <https://github.com/EnyanDai/GANF>`_
Drift doesn't matter: dynamic decomposition with diffusion reconstruction for unstable multivariate time series anomaly detection NeurIPS 2023 [#Wang2023Drift]_ `[PDF] <https://openreview.net/pdf?id=aW5bSuduF1>`_, `[Code] <https://github.com/ForestsKing/D3R>`_
===================================================================================================================================== ============================ ===== ============================ ==========================================================================================================================================================================


4.5. Feature Selection in Outlier Detection
Expand Down Expand Up @@ -551,3 +555,5 @@ References
.. [#Zimek2014Ensembles] Zimek, A., Campello, R.J. and Sander, J., 2014. Ensembles for unsupervised outlier detection: challenges and research questions a position paper. *ACM Sigkdd Explorations Newsletter*\ , 15(1), pp.11-22.

.. [#Zong2018Deep] Zong, B., Song, Q., Min, M.R., Cheng, W., Lumezanu, C., Cho, D. and Chen, H., 2018. Deep autoencoding gaussian mixture model for unsupervised anomaly detection. International Conference on Learning Representations (ICLR).

.. [#Wang2023Drift] Wang, C., Zhuang, Z., Qi, Q., Wang, J., Wang, X., Sun, H., & Liao, J. (2023). Drift doesn't matter: dynamic decomposition with diffusion reconstruction for unstable multivariate time series anomaly detection. Advances in Neural Information Processing Systems, 36.