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SOTA
Anomaly Detection
Anomaly Detection On Ucr Anomaly Archive
Anomaly Detection On Ucr Anomaly Archive
评估指标
AUC ROC
评测结果
各个模型在此基准测试上的表现结果
Columns
模型名称
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
0 of 24 row(s) selected.
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