SOTAVerified

Time Series Anomaly Detection

Papers

Showing 5175 of 264 papers

TitleStatusHype
Can LLMs Understand Time Series Anomalies?Code1
Applying Quantum Autoencoders for Time Series Anomaly Detection0
The Elephant in the Room: Towards A Reliable Time-Series Anomaly Detection BenchmarkCode3
Towards Unbiased Evaluation of Time-series Anomaly DetectorCode0
Matrix Profile for Anomaly Detection on Multidimensional Time Series0
Training-Free Time-Series Anomaly Detection: Leveraging Image Foundation Models0
Multivariate Time-Series Anomaly Detection based on Enhancing Graph Attention Networks with Topological AnalysisCode1
Unveiling the Flaws: A Critical Analysis of Initialization Effect on Time Series Anomaly Detection0
Online Model-based Anomaly Detection in Multivariate Time Series: Taxonomy, Survey, Research Challenges and Future Directions0
Can LLMs Serve As Time Series Anomaly Detectors?0
Time Series Anomaly Detection with CNN for Environmental Sensors in Healthcare-IoT0
Impact of Recurrent Neural Networks and Deep Learning Frameworks on Real-time Lightweight Time Series Anomaly Detection0
Infinite-dimensional Mahalanobis Distance with Applications to Kernelized Novelty DetectionCode0
An Autoencoder Architecture for L-band Passive Microwave Retrieval of Landscape Freeze-Thaw CycleCode0
Self-Supervised Spatial-Temporal Normality Learning for Time Series Anomaly DetectionCode1
European Space Agency Benchmark for Anomaly Detection in Satellite TelemetryCode2
Self-Supervised Time-Series Anomaly Detection Using Learnable Data Augmentation0
Unraveling Anomalies in Time: Unsupervised Discovery and Isolation of Anomalous Behavior in Bio-regenerative Life Support System TelemetryCode0
Chimera: Effectively Modeling Multivariate Time Series with 2-Dimensional State Space Models0
PeFAD: A Parameter-Efficient Federated Framework for Time Series Anomaly DetectionCode1
ContextFlow++: Generalist-Specialist Flow-based Generative Models with Mixed-Variable Context EncodingCode0
Joint Selective State Space Model and Detrending for Robust Time Series Anomaly DetectionCode0
Large Language Models can Deliver Accurate and Interpretable Time Series Anomaly Detection0
Towards a General Time Series Anomaly Detector with Adaptive Bottlenecks and Dual Adversarial Decoders0
Large language models can be zero-shot anomaly detectors for time series?Code2
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Benchmark Results

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