arXiv Machine Learning

Collaborative Streaming Anomaly Detection with Interactive Explanations and Ensemble Consensus

arXiv AI

3d ago

In a Streaming World, Should You Stand Still? A Comprehensive Benchmark of Anomaly Detection in Streams

arXiv:2609.39215v1 Announce Type: cross Abstract: Time series anomaly detection (TSAD) is increasingly deployed in streaming settings, where data arrive sequentially and may exhibit non-stationarity....

By Magali Parrino, Antoine Ajenjo, Emmanuel Remy, Pierre Stephan, Pierre Senellart, Paul Boniol

benchmarks

arXiv Machine Learning

Aug 4

CAPMix: Robust KPI Anomaly Detection for AIOps in Noisy and Dynamic Environments

arXiv:2509. 06419v2 Announce Type: replace Abstract: Time-series anomaly detection is crucial in AIOps for maintaining large-scale service reliability.

By Xudong Mou, Rui Wang, Tiejun Wang, Zexin Wu, Fangda Guo, Jie Sun, Shiru Chen, Penghao Zhang, Tiezi Zhang, Tianyu Wo, Hao Peng, Chunming Hu, Xudong Liu, Renyu Yang

benchmarks

arXiv Machine Learning

Aug 12

BREAD: Baseline-Referenced Explanations for Anomaly Diagnosis

arXiv:2608. 10587v1 Announce Type: new Abstract: Artificial Intelligence (AI)-based prospective anomaly detection methods are increasingly deployed in high-dimensional and nonlinear settings.

By Jiaqi Qiu, Rob Goedhart, Jannis Kurtz, Inez M. Zwetsloot

arXiv AI

3d ago

What Streaming Anomaly Detection Finds (and Misses) in Industrial Time Series

arXiv:2609.39232v1 Announce Type: cross Abstract: EDF relies on continuous monitoring of its power plants to detect anomalies as soon as they occur. Given the absence of a universally optimal streami...

By Magali Parrino, Antoine Ajenjo, Emmanuel Remy, Pierre Stephan, Paul Boniol

benchmarks

arXiv Machine Learning

Jun 5

Cluster-Aware Causal Mixer for Online Anomaly Detection in Multivariate Time Series

arXiv:2506. 00188v2 Announce Type: replace Abstract: Early and accurate detection of anomalies in time-series data is critical due to the substantial risks associated with false or missed detections.

By Md Mahmuddun Nabi Murad, Yasin Yilmaz

rag benchmarks

arXiv AI

Jun 30

Redefining Maritime Anomaly Detection via Equation-Grounded Synthetic Anomalies

arXiv:2606. 29721v1 Announce Type: cross Abstract: Maritime anomaly detection is essential for ensuring maritime safety, security, and efficient traffic management at sea, with Automatic Identification System (AIS) data serving as a primary data source.

By Youngseok Hwang, Sungho Bae, Dohun Lee, Jaeeun Seo, Jeehong Kim, Wonhee Lee, Hyunwoo Park

llms benchmarks safety

arXiv Machine Learning

Sep 15

Optimal Transport for Efficient, Unsupervised Anomaly Detection on Industrial Data

arXiv:2609.13940v1 Announce Type: new Abstract: Effective anomaly detection frameworks are a central pillar of the Industry 4.0 paradigm. In this paper, we introduce an Optimal Transport (OT)-based f...

By Abigail Langbridge, Fearghal O'Donncha, James T Rayfield, Bradley Eck

benchmarks

arXiv AI

Jul 22

Towards Principled Continual Anomaly Detection: A Systematic Framework and Benchmark Scenarios

arXiv:2607. 18289v1 Announce Type: cross Abstract: Continual anomaly detection (CAD) studies how models can adapt to evolving data distributions while retaining performance on previously observed regimes.

By Kamil Faber, Mateusz Smendowski, Roberto Corizzo

benchmarks

arXiv Machine Learning

Jun 10

AnomaMind: Agentic Time Series Anomaly Detection with Tool-Augmented Reasoning

arXiv:2602. 13807v2 Announce Type: replace Abstract: Time series anomaly detection is critical in many real-world applications, where effective solutions must localize anomalous regions and support reliable decision-making under complex settings.

By Xiaoyu Tao, Yuchong Wu, Mingyue Cheng, Ze Guo, Tian Gao

agents safety

arXiv Computer Vision

Sep 10

ReactVAU: A Slow-Fast Decoupled Framework for Streaming Video Anomaly Understanding

arXiv:2609.07941v2 Announce Type: replace Abstract: In this paper, we propose ReactVAU, a Slow-Fast Decoupled Framework for real-time streaming Video Anomaly Understanding (VAU). Existing VAU methods...

By Chia-Hui Chen, Shih-Ying Yeh, Fu-En Yang, Min-Hung Chen, Shang-Hong Lai

benchmarks

arXiv Machine Learning

Jul 23

NeuCoReClass AD: Redefining Self-Supervised Time Series Anomaly Detection

arXiv:2508. 00909v2 Announce Type: replace Abstract: Time series anomaly detection plays a critical role in a wide range of real-world applications.

By Aitor S\'anchez-Ferrera, Usue Mori, Borja Calvo, Jose A. Lozano

benchmarks

arXiv AI

Jun 12

ASTER: Latent Pseudo-Anomaly Generation for Unsupervised Time-Series Anomaly Detection

arXiv:2604. 13924v3 Announce Type: replace-cross Abstract: Time-series anomaly detection (TSAD) is critical in domains such as industrial monitoring, healthcare, and cybersecurity, but it remains challenging due to rare and heterogeneous anomalies and the scarcity of labelled data.

By Romain Hermary, Samet Hicsonmez, Dan Pineau, Abd El Rahman Shabayek, Djamila Aouada

llms rag diffusion benchmarks

Related stories