HyperAI
الرئيسيةالأخبارأحدث الأوراق البحثيةالدروسمجموعات البياناتالموسوعةSOTAنماذج LLMلوحة الأداء GPUالفعاليات
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HyperAI

Anomaly Detection On Ucr Anomaly Archive

المقاييس

AUC ROC

النتائج

نتائج أداء النماذج المختلفة على هذا المعيار القياسي

اسم النموذج
AUC ROC
Paper TitleRepository
LSTMAD0.6432KAN-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 ±0.01Is it worth it? Comparing six deep and classical methods for unsupervised anomaly detection in time series
TranAD0.4599KAN-AD: Time Series Anomaly Detection with Kolmogorov-Arnold Networks-
ARIMA-AER: Auto-Encoder with Regression for Time Series Anomaly Detection
OFA0.5699KAN-AD: Time Series Anomaly Detection with Kolmogorov-Arnold Networks-
Robust Random Cut Forest (RRCF)0.56 ± 0.0019Is 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
FCVAE0.7145KAN-AD: Time Series Anomaly Detection with Kolmogorov-Arnold Networks-
SRCNN0.5109KAN-AD: Time Series Anomaly Detection with Kolmogorov-Arnold Networks-
LSTM-VAE-AER: Auto-Encoder with Regression for Time Series Anomaly Detection
KAN-AD0.8188 ±0.0041KAN-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 ±0.009Is it worth it? Comparing six deep and classical methods for unsupervised anomaly detection in time series
SAND0.6550KAN-AD: Time Series Anomaly Detection with Kolmogorov-Arnold Networks-
LSTM-DT-AER: Auto-Encoder with Regression for Time Series Anomaly Detection
KAN0.7489KAN-AD: Time Series Anomaly Detection with Kolmogorov-Arnold Networks-
SubLOF0.8001KAN-AD: Time Series Anomaly Detection with Kolmogorov-Arnold Networks-
TimesNet0.4536KAN-AD: Time Series Anomaly Detection with Kolmogorov-Arnold Networks-
MERLIN0.51 ± 0.0Is it worth it? Comparing six deep and classical methods for unsupervised anomaly detection in time series
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