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Time Series Anomaly Detection

Papers

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Showing 176–200 of 264 papers

TitleStatusHype
PUPAE: Intuitive and Actionable Explanations for Time Series Anomalies—0
Quantum Autoencoder for Multivariate Time Series Anomaly Detection—0
Reconstruct Anomaly to Normal: Adversarial Learned and Latent Vector-constrained Autoencoder for Time-series Anomaly Detection—0
Recurrent Auto-Encoder With Multi-Resolution Ensemble and Predictive Coding for Multivariate Time-Series Anomaly Detection—0
Refining the Optimization Target for Automatic Univariate Time Series Anomaly Detection in Monitoring Services—0
Refining Time Series Anomaly Detectors using Large Language Models—0
RePAD2: Real-Time, Lightweight, and Adaptive Anomaly Detection for Open-Ended Time Series—0
ReRe: A Lightweight Real-time Ready-to-Go Anomaly Detection Approach for Time Series—0
RLAD: Time Series Anomaly Detection through Reinforcement Learning and Active Learning—0
Robust Anomaly Detection for Time-series Data—0
Robust Learning of Deep Time Series Anomaly Detection Models with Contaminated Training Data—0
RobustTAD: Robust Time Series Anomaly Detection via Decomposition and Convolutional Neural Networks—0
RoLA: A Real-Time Online Lightweight Anomaly Detection System for Multivariate Time Series—0
RSM-GAN: A Convolutional Recurrent GAN for Anomaly Detection in Contaminated Seasonal Multivariate Time Series—0
See it, Think it, Sorted: Large Multimodal Models are Few-shot Time Series Anomaly Analyzers—0
Self-Supervised Time-Series Anomaly Detection Using Learnable Data Augmentation—0
Sketching Multidimensional Time Series for Fast Discord Mining—0
Stacked Residuals of Dynamic Layers for Time Series Anomaly Detection—0
STRIC: Stacked Residuals of Interpretable Components for Time Series Anomaly Detection—0
Successive Data Injection in Conditional Quantum GAN Applied to Time Series Anomaly Detection—0
Time-series Anomaly Detection based on Difference Subspace between Signal Subspaces—0
Time Series Anomaly Detection; Detection of anomalous drops with limited features and sparse examples in noisy highly periodic data—0
Time Series Anomaly Detection for Smart Grids: A Survey—0
Time Series Anomaly Detection in Smart Homes: A Deep Learning Approach—0
Time Series Anomaly Detection Using Convolutional Neural Networks and Transfer Learning—0
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All datasetsUCR Anomaly ArchiveKPIMSLSMAPSMDSWaTWADIYahoo A1

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TimeVQVAE-ADaccuracy0.71—Unverified
2Matrix Profile STUMPYaccuracy0.51—Unverified
3MDIaccuracy0.47—Unverified
4Matrix Profile SCRIMPaccuracy0.42—Unverified
5RCFaccuracy0.39—Unverified
6IFaccuracy0.38—Unverified
7Convolutional AEaccuracy0.35—Unverified
8SR-CNNaccuracy0.3—Unverified
9USADaccuracy0.28—Unverified
10AEaccuracy0.24—Unverified
#ModelMetricClaimedVerifiedStatus
1CARLAAUPR0.3—Unverified
#ModelMetricClaimedVerifiedStatus
1CARLAAUPR0.5—Unverified
#ModelMetricClaimedVerifiedStatus
1CARLAAUPR0.45—Unverified
#ModelMetricClaimedVerifiedStatus
1CARLAAUPR0.51—Unverified
#ModelMetricClaimedVerifiedStatus
1CARLAAUPR0.68—Unverified
#ModelMetricClaimedVerifiedStatus
1CARLAAUPR0.13—Unverified
#ModelMetricClaimedVerifiedStatus
1CARLAAUPR0.65—Unverified