Papers with Code · CC BY-SA 4.0

UCR Anomaly Archive

아카이브 평가 24건 · 마지막 적재/변경 2026-10-30

평가 기록

과거 수집 기록이며 최신 순위가 아닙니다. 데이터셋 버전·split·실험 조건과 지표 방향이 검증되지 않은 결과는 직접 비교할 수 없습니다. 날짜는 논문 날짜이며 최신 평가일을 뜻하지 않습니다.

표가 화면보다 넓으면 좌우로 스크롤하여 논문과 구현 링크를 확인하세요.

원본 기록 순서 · 순위 미부여
모델	원본 지표·값	논문	구현
Auto-Encoder with Regression (AER)	Average F1 0.470	AER: Auto-Encoder with Regression for Time Series Anomaly Detection 2022-12-27	코드 1 · 코드 2 · 코드 3
LSTM-DT	Average F1 0.391	AER: Auto-Encoder with Regression for Time Series Anomaly Detection 2022-12-27	코드 1 · 코드 2 · 코드 3
LSTM-VAE	Average F1 0.317	AER: Auto-Encoder with Regression for Time Series Anomaly Detection 2022-12-27	코드 1 · 코드 2 · 코드 3
LSTM-AE	Average F1 0.314	AER: Auto-Encoder with Regression for Time Series Anomaly Detection 2022-12-27	코드 1 · 코드 2 · 코드 3
MERLIN	Average F1 0.27 ±0.0 AUC ROC 0.51 ± 0.0	Is it worth it? Comparing six deep and classical methods for unsupervised anomaly detection in time series 2022-12-21	코드 1
Maximally Divergent Intervals (MDI)	Average F1 0.25 ±0.0 AUC ROC 0.66 ± 0.0	Is it worth it? Comparing six deep and classical methods for unsupervised anomaly detection in time series 2022-12-21	코드 1
Graph Augmented Normalizing Flows (GANF)	Average F1 0.23 ±0.021 AUC ROC 0.63 ±0.009	Is it worth it? Comparing six deep and classical methods for unsupervised anomaly detection in time series 2022-12-21	코드 1
Transformer Network for Anomaly Detection (TranAD)	Average F1 0.18 ±0.003 AUC ROC 0.56 ±0.003	Is it worth it? Comparing six deep and classical methods for unsupervised anomaly detection in time series 2022-12-21	코드 1
TadGAN	Average F1 0.162	AER: Auto-Encoder with Regression for Time Series Anomaly Detection 2022-12-27	코드 1 · 코드 2 · 코드 3
Autoencoder (AE)	Average F1 0.16 ± 0.013 AUC ROC 0.58 ±0.01	Is it worth it? Comparing six deep and classical methods for unsupervised anomaly detection in time series 2022-12-21	코드 1
ARIMA	Average F1 0.124	AER: Auto-Encoder with Regression for Time Series Anomaly Detection 2022-12-27	코드 1 · 코드 2 · 코드 3
Robust Random Cut Forest (RRCF)	Average F1 0.07 ±0.011 AUC ROC 0.56 ± 0.0019	Is it worth it? Comparing six deep and classical methods for unsupervised anomaly detection in time series 2022-12-21	코드 1
KAN-AD	AUC ROC 0.8188 ±0.0041	KAN-AD: Time Series Anomaly Detection with Kolmogorov-Arnold Networks 2024-11-01	코드 1
SubLOF	AUC ROC 0.8001	KAN-AD: Time Series Anomaly Detection with Kolmogorov-Arnold Networks 2024-11-01	코드 1
KAN	AUC ROC 0.7489	KAN-AD: Time Series Anomaly Detection with Kolmogorov-Arnold Networks 2024-11-01	코드 1
FCVAE	AUC ROC 0.7145	KAN-AD: Time Series Anomaly Detection with Kolmogorov-Arnold Networks 2024-11-01	코드 1
SAND	AUC ROC 0.6550	KAN-AD: Time Series Anomaly Detection with Kolmogorov-Arnold Networks 2024-11-01	코드 1
LSTMAD	AUC ROC 0.6432	KAN-AD: Time Series Anomaly Detection with Kolmogorov-Arnold Networks 2024-11-01	코드 1
FITS	AUC ROC 0.5969	KAN-AD: Time Series Anomaly Detection with Kolmogorov-Arnold Networks 2024-11-01	코드 1
OFA	AUC ROC 0.5699	KAN-AD: Time Series Anomaly Detection with Kolmogorov-Arnold Networks 2024-11-01	코드 1
AT	AUC ROC 0.5458	KAN-AD: Time Series Anomaly Detection with Kolmogorov-Arnold Networks 2024-11-01	코드 1
SRCNN	AUC ROC 0.5109	KAN-AD: Time Series Anomaly Detection with Kolmogorov-Arnold Networks 2024-11-01	코드 1
TranAD	AUC ROC 0.4599	KAN-AD: Time Series Anomaly Detection with Kolmogorov-Arnold Networks 2024-11-01	코드 1
TimesNet	AUC ROC 0.4536	KAN-AD: Time Series Anomaly Detection with Kolmogorov-Arnold Networks 2024-11-01	코드 1