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Time Series Anomaly Detection

Papers

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Showing 51–100 of 264 papers

TitleStatusHype
Can LLMs Understand Time Series Anomalies?Code1
Applying Quantum Autoencoders for Time Series Anomaly Detection—0
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 Series—0
Training-Free Time-Series Anomaly Detection: Leveraging Image Foundation Models—0
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 Detection—0
Online Model-based Anomaly Detection in Multivariate Time Series: Taxonomy, Survey, Research Challenges and Future Directions—0
Can LLMs Serve As Time Series Anomaly Detectors?—0
Time Series Anomaly Detection with CNN for Environmental Sensors in Healthcare-IoT—0
Impact of Recurrent Neural Networks and Deep Learning Frameworks on Real-time Lightweight Time Series Anomaly Detection—0
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 Augmentation—0
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 Models—0
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 Detection—0
Towards a General Time Series Anomaly Detector with Adaptive Bottlenecks and Dual Adversarial Decoders—0
Large language models can be zero-shot anomaly detectors for time series?Code2
SimAD: A Simple Dissimilarity-based Approach for Time Series Anomaly DetectionCode1
RESTAD: REconstruction and Similarity based Transformer for time series Anomaly DetectionCode0
DeepHYDRA: Resource-Efficient Time-Series Anomaly Detection in Dynamically-Configured SystemsCode0
A Reliable Framework for Human-in-the-Loop Anomaly Detection in Time Series—0
Position: Quo Vadis, Unsupervised Time Series Anomaly Detection?Code1
Sub-Adjacent Transformer: Improving Time Series Anomaly Detection with Reconstruction Error from Sub-Adjacent NeighborhoodsCode0
Warped Time Series Anomaly Detection—0
DACAD: Domain Adaptation Contrastive Learning for Anomaly Detection in Multivariate Time SeriesCode1
Fault Detection in Mobile Networks Using Diffusion Models—0
TSA on AutoPilot: Self-tuning Self-supervised Time Series Anomaly DetectionCode0
From Chaos to Clarity: Time Series Anomaly Detection in Astronomical ObservationsCode0
Exploring the Influence of Dimensionality Reduction on Anomaly Detection Performance in Multivariate Time SeriesCode0
Towards efficient deep autoencoders for multivariate time series anomaly detection—0
Unsupervised Distance Metric Learning for Anomaly Detection Over Multivariate Time Series—0
Generative Adversarial Network with Soft-Dynamic Time Warping and Parallel Reconstruction for Energy Time Series Anomaly DetectionCode1
TimeSeriesBench: An Industrial-Grade Benchmark for Time Series Anomaly Detection ModelsCode2
Revisiting VAE for Unsupervised Time Series Anomaly Detection: A Frequency PerspectiveCode2
Understanding Time Series Anomaly State Detection through One-Class Classification—0
Large Language Model Guided Knowledge Distillation for Time Series Anomaly Detection—0
Edge Conditional Node Update Graph Neural Network for Multi-variate Time Series Anomaly Detection—0
DACR: Distribution-Augmented Contrastive Reconstruction for Time-Series Anomaly Detection—0
TreeMIL: A Multi-instance Learning Framework for Time Series Anomaly Detection with Inexact SupervisionCode1
PatchAD: A Lightweight Patch-based MLP-Mixer for Time Series Anomaly DetectionCode1
PUPAE: Intuitive and Actionable Explanations for Time Series Anomalies—0
Graph Spatiotemporal Process for Multivariate Time Series Anomaly Detection with Missing ValuesCode0
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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