Hugging Face Trending Papers July 9, 2026

CAAD: Causality-Aware Multivariate Time Series Anomaly Detection via Multi-Scale Alignment and Structural Causal Consistency

Read the original on Hugging Face Trending Papers →

The operational integrity of complex industrial systems relies on precise anomaly detection and diagnosis. The vast majority of existing methods narrowly focus on capturing temporal similarities of representations, often overlooking the disruption of internal causal relationships, which characterizes system failures and latent anomalies.

Summary generated by The Flow from the publisher's feed. The full article lives at Hugging Face Trending Papers.

arXiv Machine Learning

Jul 10

CAAD: Causality-Aware Multivariate Time Series Anomaly Detection via Multi-Scale Alignment and Structural Causal Consistency

arXiv:2607. 08555v1 Announce Type: new Abstract: The operational integrity of complex industrial systems relies on precise anomaly detection and diagnosis.

By Xin Wang, Yunshi Wen, Yanan He, Haotian Xu, Youlan Zhao, Michel Ferreira Cardia Haddad, Tengfei Ma

benchmarks safety

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

Jul 9

Anomaly detection in time-series via inductive biases in the latent space of conditional normalizing flows

arXiv:2603. 11756v2 Announce Type: replace Abstract: Deep generative models for anomaly detection in multivariate time-series are typically trained by maximizing observed data likelihood.

By David Baumgartner, Eliezer de Souza da Silva, I\~nigo Urteaga

diffusion

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

Jul 20

Knowledge-Assisted Multi-Graph Dependency Learning for Multivariate Time Series Anomaly Detection in Multi-Stage Industrial Processes

arXiv:2607. 15799v1 Announce Type: cross Abstract: Industrial processes often generate complex, interdependent time-series data from multiple sensors across multiple stages, forming complex dependencies among variables and process stages.

By Jaeyeong Lee, Taeseong Yoon, Wonmo Koo, Heeyoung Kim

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

Related stories

CAAD: Causality-Aware Multivariate Time Series Anomaly Detection via Multi-Scale Alignment and Structural Causal Consistency

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

Anomaly detection in time-series via inductive biases in the latent space of conditional normalizing flows

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

Knowledge-Assisted Multi-Graph Dependency Learning for Multivariate Time Series Anomaly Detection in Multi-Stage Industrial Processes

An Explainable GNN Framework for Component-Level Anomaly Diagnosis