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الرئيسية
SOTA
كشف الأخطاء
Anomaly Detection On Ucr Anomaly Archive
Anomaly Detection On Ucr Anomaly Archive
المقاييس
AUC ROC
النتائج
نتائج أداء النماذج المختلفة على هذا المعيار القياسي
Columns
اسم النموذج
AUC ROC
Paper Title
Repository
LSTMAD
0.6432
KAN-AD: Time Series Anomaly Detection with Kolmogorov-Arnold Networks
LSTM-AE
-
AER: Auto-Encoder with Regression for Time Series Anomaly Detection
Autoencoder (AE)
0.58 ±0.01
Is it worth it? Comparing six deep and classical methods for unsupervised anomaly detection in time series
TranAD
0.4599
KAN-AD: Time Series Anomaly Detection with Kolmogorov-Arnold Networks
ARIMA
-
AER: Auto-Encoder with Regression for Time Series Anomaly Detection
OFA
0.5699
KAN-AD: Time Series Anomaly Detection with Kolmogorov-Arnold Networks
Robust Random Cut Forest (RRCF)
0.56 ± 0.0019
Is it worth it? Comparing six deep and classical methods for unsupervised anomaly detection in time series
TadGAN
-
AER: Auto-Encoder with Regression for Time Series Anomaly Detection
FCVAE
0.7145
KAN-AD: Time Series Anomaly Detection with Kolmogorov-Arnold Networks
SRCNN
0.5109
KAN-AD: Time Series Anomaly Detection with Kolmogorov-Arnold Networks
LSTM-VAE
-
AER: Auto-Encoder with Regression for Time Series Anomaly Detection
KAN-AD
0.8188 ±0.0041
KAN-AD: Time Series Anomaly Detection with Kolmogorov-Arnold Networks
Auto-Encoder with Regression (AER)
-
AER: Auto-Encoder with Regression for Time Series Anomaly Detection
Graph Augmented Normalizing Flows (GANF)
0.63 ±0.009
Is it worth it? Comparing six deep and classical methods for unsupervised anomaly detection in time series
SAND
0.6550
KAN-AD: Time Series Anomaly Detection with Kolmogorov-Arnold Networks
LSTM-DT
-
AER: Auto-Encoder with Regression for Time Series Anomaly Detection
KAN
0.7489
KAN-AD: Time Series Anomaly Detection with Kolmogorov-Arnold Networks
SubLOF
0.8001
KAN-AD: Time Series Anomaly Detection with Kolmogorov-Arnold Networks
TimesNet
0.4536
KAN-AD: Time Series Anomaly Detection with Kolmogorov-Arnold Networks
MERLIN
0.51 ± 0.0
Is it worth it? Comparing six deep and classical methods for unsupervised anomaly detection in time series
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