arXiv Machine Learning

Optimal Transport for Efficient, Unsupervised Anomaly Detection on Industrial Data

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 AI

Oct 1

From Benchmarks to Production: Transferring Time Series Anomaly Detection Methods for Electricity Production Monitoring

arXiv:2609.39257v1 Announce Type: cross Abstract: Accurate forecasting of electricity production is essential for maintaining the operational efficiency and strategic planning of energy utilities. In...

By Nicolas Vautier, Paul Caron, Nardi Xhepi, F\'elicie Bizeul, Manel Boumghar, Christophe Degouy, Paul Boniol

benchmarks data-engineering

arXiv AI

Oct 27

AdaTrendLike: Adaptive Hierarchical Detrending and Trend Likelihood Scoring for Time Series Anomaly Detection

arXiv:2610.35400v1 Announce Type: new Abstract: The rapid proliferation of Industrial Internet of Things devices has led to increasingly large-scale and non-stationary time-series data, posing signif...

By Van Kwan Zhi Koh, Songnan Lin, Rongmo Luo, Zhiping Lin, Bihan Wen

rag benchmarks safety

arXiv AI

Aug 11

An Explainable GNN Framework for Component-Level Anomaly Diagnosis

arXiv:2608. 09246v1 Announce Type: new Abstract: Industrial processes are complex systems composed of multiple interacting sensors that generate multivariate time series (MTS).

By Sena Ozgunay (IMT, ANITI, LAAS-DISCO, LAAS, Comue de Toulouse), Louise Trav\'e-Massuy\`es (LAAS-DISCO, Comue de Toulouse, ANITI), Jean-Michel Loubes (IMT, REGALIA), Raul Sena Ferreira (LAAS)

safety

arXiv Machine Learning

Jun 18

Anomaly Detection for Sparse and Irregular Multivariate Time Series with Latent SDEs

arXiv:2606. 18898v1 Announce Type: new Abstract: Multivariate time series anomaly detection (MTSAD) is critical for a wide range of application areas, such as industrial monitoring, cybersecurity, or healthcare.

By Martin Uray, Dominik Geng, Florian Graf, Stefan Huber, Roland Kwitt

efficiency benchmarks safety

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

arXiv AI

Jul 7

Fun-TSG: A Function-Driven Multivariate Time Series Generator with Variable-Level Anomaly Labeling

arXiv:2604. 14221v2 Announce Type: replace Abstract: Reliable evaluation of anomaly detection methods in multivariate time series remains an open challenge, largely due to the limitations of existing benchmark datasets.

By Pierre Lotte (EPE UT, IRIT), Andr\'e P\'eninou (UT2J, IRIT-SIG, IRIT), Olivier Teste (IRIT-SIG, IRIT, UT2J, Comue de Toulouse)

benchmarks

arXiv Machine Learning

Sep 24

CAST: Context- and Anomaly Structure-Conditioned Time Series Anomaly Generation

CAST is a framework for generating anomalous time series that addresses the scarcity and heterogeneity of anomaly data. It uses a two‑stage approach: pretraining on abundant normal data to learn system dynamics, then finetuning with anomaly structure representations to capture diverse anomaly morphologies. Experiments on real‑world datasets show that CAST outperforms existing methods in both generation quality and downstream task performance.

By Haochen Zhang, Jie Peng, Songyuan Sui, Yu-Chao Huang, Xiangqi Zhu, Tianlong Chen

fine-tuning benchmarks safety

arXiv Machine Learning

Jun 5

Anomaly Detection for Electro-Hydrostatic Actuators using LSTM Autoencoder

arXiv:2606. 05274v1 Announce Type: new Abstract: Electro-Hydrostatic Actuators (EHAs) are widely used in aerospace and industrial systems, where timely detection of sensor anomalies is essential to ensure safe and reliable operation.

By Nehal Afifi, Abdelmonem Elhendawi, Felix Leitenberger, Nadine Piat, Sven Matthiesen

arXiv Machine Learning

Jul 3

Fast and Accurate Anomaly Detection in Time Series

arXiv:2607. 02046v1 Announce Type: new Abstract: Anomaly detection is a critical and evolving field in Machine Learning, with applications targeting different domains such as cybersecurity, finance, healthcare, manufacturing and IoT (Internet of Things) systems.

By Emanuele Mele, Massimo Cafaro, Angelo Coluccia, Italo Epicoco

benchmarks safety

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

Jul 2

Detecting the Undetectable: Enhancing Unsupervised time series Anomaly Detection via Active Learning

arXiv:2607. 00720v1 Announce Type: cross Abstract: Despite the increasing sophistication of industrial AI systems, the ability to reliably detect subtle and noisy anomalies in complex time series data remains a critical yet unresolved challenge.

By Seung Hun Han, Hyeongwon Kang, Jinwoo Park, Pilsung Kang

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