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Time Series Anomaly Detection

Papers

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Showing 1–50 of 264 papers

TitleStatusHype
mTSBench: Benchmarking Multivariate Time Series Anomaly Detection and Model Selection at ScaleCode0
Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time SeriesCode0
TAB: Unified Benchmarking of Time Series Anomaly Detection MethodsCode2
Cluster-Aware Causal Mixer for Online Anomaly Detection in Multivariate Time Series—0
Enhancing Network Anomaly Detection with Quantum GANs and Successive Data Injection for Multivariate Time Series—0
Anomaly Detection for Non-stationary Time Series using Recurrent Wavelet Probabilistic Neural Network—0
FreCT: Frequency-augmented Convolutional Transformer for Robust Time Series Anomaly Detection—0
CICADA: Cross-Domain Interpretable Coding for Anomaly Detection and Adaptation in Multivariate Time Series—0
Quantum Autoencoder for Multivariate Time Series Anomaly Detection—0
DConAD: A Differencing-based Contrastive Representation Learning Framework for Time Series Anomaly DetectionCode0
PatchTrAD: A Patch-Based Transformer focusing on Patch-Wise Reconstruction Error for Time Series Anomaly Detection—0
AMAD: AutoMasked Attention for Unsupervised Multivariate Time Series Anomaly Detection—0
iADCPS: Time Series Anomaly Detection for Evolving Cyber-physical Systems via Incremental Meta-learning—0
VISTA: Unsupervised 2D Temporal Dependency Representations for Time Series Anomaly Detection—0
Refining Time Series Anomaly Detectors using Large Language Models—0
RoCA: Robust Contrastive One-class Time Series Anomaly Detection with Contaminated DataCode0
Multivariate Time Series Anomaly Detection in Industry 5.0—0
KDSelector: A Knowledge-Enhanced and Data-Efficient Model Selector Learning Framework for Time Series Anomaly DetectionCode0
Federated Koopman-Reservoir Learning for Large-Scale Multivariate Time-Series Anomaly Detection—0
OIPR: Evaluation for Time-series Anomaly Detection Inspired by Operator InterestCode0
Can Multimodal LLMs Perform Time Series Anomaly Detection?Code1
dtaianomaly: A Python library for time series anomaly detectionCode2
VUS: Effective and Efficient Accuracy Measures for Time-Series Anomaly DetectionCode2
GenIAS: Generator for Instantiating Anomalies in time Series—0
MAAT: Mamba Adaptive Anomaly Transformer with association discrepancy for time seriesCode1
Federated Learning with Reservoir State Analysis for Time Series Anomaly DetectionCode0
GDformer: Going Beyond Subsequence Isolation for Multivariate Time Series Anomaly DetectionCode0
A Review on Self-Supervised Learning for Time Series Anomaly Detection: Recent Advances and Open ChallengesCode1
Argos: Agentic Time-Series Anomaly Detection with Autonomous Rule Generation via Large Language Models—0
GCAD: Anomaly Detection in Multivariate Time Series from the Perspective of Granger Causality—0
Noise-Resilient Point-wise Anomaly Detection in Time Series Using Weak Segment LabelsCode1
Synergizing Large Language Models and Task-specific Models for Time Series Anomaly Detection—0
Counterfactual Explanation for Auto-Encoder Based Time-Series Anomaly Detection—0
Multivariate Time Series Anomaly Detection using DiffGAN ModelCode0
Dive into Time-Series Anomaly Detection: A Decade ReviewCode0
Graph Mixture of Experts and Memory-augmented Routers for Multivariate Time Series Anomaly Detection—0
PASTA: Neural Architecture Search for Anomaly Detection in Multivariate Time SeriesCode0
A New Perspective on Time Series Anomaly Detection: Faster Patch-based Broad Learning System—0
F-SE-LSTM: A Time Series Anomaly Detection Method with Frequency Domain InformationCode1
TSINR: Capturing Temporal Continuity via Implicit Neural Representations for Time Series Anomaly DetectionCode1
From CNN to CNN + RNN: Adapting Visualization Techniques for Time-Series Anomaly Detection—0
Enhanced Real-Time Threat Detection in 5G Networks: A Self-Attention RNN Autoencoder Approach for Spectral Intrusion Analysis—0
Advancing Cyber-Attack Detection in Power Systems: A Comparative Study of Machine Learning and Graph Neural Network Approaches—0
See it, Think it, Sorted: Large Multimodal Models are Few-shot Time Series Anomaly Analyzers—0
KAN-AD: Time Series Anomaly Detection with Kolmogorov-Arnold NetworksCode1
MIXAD: Memory-Induced Explainable Time Series Anomaly DetectionCode0
Hypergraph-based multi-scale spatio-temporal graph convolution network for Time-Series anomaly detection—0
MultiRC: Joint Learning for Time Series Anomaly Prediction and Detection with Multi-scale Reconstructive Contrast—0
CATCH: Channel-Aware multivariate Time Series Anomaly Detection via Frequency PatchingCode2
Interdependency Matters: Graph Alignment for Multivariate Time Series Anomaly DetectionCode0
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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