Merge pull request #35 from real-lhj/main

add DCdetector
xuhongzuo · Sep 21, 2023 · 0015128 · 0015128
2 parents 859e902 + 9d5ff3f
commit 0015128
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diff --git a/README.rst b/README.rst
@@ -18,7 +18,7 @@ Python Deep Outlier/Anomaly Detection (DeepOD)
 and `Anomaly Detection <https://en.wikipedia.org/wiki/Anomaly_detection>`_. ``DeepOD`` supports tabular anomaly detection and time-series anomaly detection.
 
 
-DeepOD includes **25** deep outlier detection / anomaly detection algorithms (in unsupervised/weakly-supervised paradigm). 
+DeepOD includes **26** deep outlier detection / anomaly detection algorithms (in unsupervised/weakly-supervised paradigm).
 More baseline algorithms will be included later.
 
 
@@ -184,6 +184,7 @@ Implemented Models
  :header: "Model", "Venue", "Year", "Type", "Title"
  :widths: 4, 4, 4, 8, 20 
 
+ DCdetector, KDD, 2023, unsupervised, DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection [#Yang2023dcdetector]_
  TimesNet, ICLR, 2023, unsupervised, TIMESNET: Temporal 2D-Variation Modeling for General Time Series Analysis [#Wu2023timesnet]_
  AnomalyTransformer, ICLR, 2022, unsupervised, Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy [#Xu2022transformer]_
  TranAD, VLDB, 2022, unsupervised, TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data  
@@ -244,4 +245,6 @@ Reference
 
 .. [#Xu2022transformer] Xu Jiehui, et al. "Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy". ICLR, 2022.
 
-.. [#Wu2023timesnet] Wu Haixu, et al. "TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis". ICLR. 2023.
+.. [#Wu2023timesnet] Wu Haixu, et al. "TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis". ICLR. 2023.
+
+.. [#Yang2023dcdetector] Yang Yiyuan et al. "DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection". KDD. 2023
diff --git a/deepod/models/__init__.py b/deepod/models/__init__.py
@@ -20,6 +20,7 @@
 from deepod.models.time_series.dif import DeepIsolationForestTS
 from deepod.models.time_series.dsvdd import DeepSVDDTS
 
+from deepod.models.time_series.dcdetector import DCdetector
 from deepod.models.time_series.timesnet import TimesNet
 from deepod.models.time_series.anomalytransformer import AnomalyTransformer
 from deepod.models.time_series.tranad import TranAD
@@ -31,7 +32,7 @@
 __all__ = [
     'RCA', 'DeepSVDD', 'GOAD', 'NeuTraL', 'RDP', 'ICL', 'SLAD', 'DeepIsolationForest',
     'DeepSAD', 'DevNet', 'PReNet', 'FeaWAD', 'REPEN', 'RoSAS',
-    'TimesNet', 'AnomalyTransformer', 'TranAD', 'COUTA', 'USAD', 'TcnED',
+    'DCdetector', 'TimesNet', 'AnomalyTransformer', 'TranAD', 'COUTA', 'USAD', 'TcnED',
     'DeepIsolationForestTS', 'DeepSVDDTS',
     'PReNetTS', 'DeepSADTS', 'DevNetTS'
 ]
diff --git a/deepod/models/time_series/__init__.py b/deepod/models/time_series/__init__.py
@@ -7,6 +7,7 @@
 from .tcned import TcnED
 from .anomalytransformer import AnomalyTransformer
 from .timesnet import TimesNet
+from .dcdetector import DCdetector
 
 # weakly-supervised
 from .dsad import DeepSADTS
@@ -15,4 +16,4 @@
 
 
 __all__ = ['DeepIsolationForestTS', 'DeepSVDDTS', 'TranAD', 'USAD', 'COUTA',
-           'DeepSADTS', 'DevNetTS', 'PReNetTS', 'AnomalyTransformer', 'TimesNet']
+           'DeepSADTS', 'DevNetTS', 'PReNetTS', 'AnomalyTransformer', 'TimesNet', 'DCdetector']