arXiv Machine Learning July 20, 2026 By Yanan He, Yunshi Wen, Xin Wang, Tengfei Ma

SC-JEPA: Stabilizing Latent Predictive Learning for Time-Series Anomaly Prediction

Read the original on arXiv Machine Learning →

arXiv:2602. 04643v2 Announce Type: replace Abstract: Time-series anomaly prediction aims to forecast future system failures before they fully emerge, making latent predictive models such as JEPA a promising framework for capturing precursor dynamics.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

6d ago

JAPE: Joint Anomaly Prediction and Intrinsic Explanation in Multivariate Time Series

arXiv:2608. 11801v1 Announce Type: new Abstract: Multivariate time-series anomaly prediction aims to identify whether and when anomalies will occur over a future horizon from historical observations.

By Yian Wei, Yuanyuan Yao, Lu Chen, Xiangmin Zhou, Tianyi Li

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

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

Jul 15

Exploring Zero-Shot Foundation Models for Multivariate Time Series Anomaly Detection

arXiv:2607. 12454v1 Announce Type: new Abstract: Multivariate Time Series Anomaly Detection (MTSAD) is essential for reliability and safety in domains such as industrial process monitoring and financial risk management, yet conventional approaches rely on application-specific models that are costly to train and hard to scale.

By Martin Uray, Saverio Messineo, Roland Kwitt, Stefan Huber

rag benchmarks safety

arXiv AI

Jun 16

Phys-JEPA: Physics-Informed Latent World Models for Multivariate Time-Series Forecasting

arXiv:2606. 16076v1 Announce Type: cross Abstract: Multivariate forecasting in physical systems requires models that predict coupled temporal variables while preserving meaningful state evolution.

By Weizhi Nie, Weichao Liu, Honglin Guo, Yuting Su

rag

arXiv Machine Learning

Jun 17

Conditional Attribution for Root Cause Analysis in Time-Series Anomaly Detection

arXiv:2604. 17616v3 Announce Type: replace Abstract: Root cause analysis (RCA) for time-series anomaly detection is critical for the reliable operation of complex real-world systems.

By Shashank Mishra, Karan Patil, Cedric Schockaert, Didier Stricker, Jason Rambach

rag benchmarks

Related stories

JAPE: Joint Anomaly Prediction and Intrinsic Explanation in Multivariate Time Series

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

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

Exploring Zero-Shot Foundation Models for Multivariate Time Series Anomaly Detection

Phys-JEPA: Physics-Informed Latent World Models for Multivariate Time-Series Forecasting

Conditional Attribution for Root Cause Analysis in Time-Series Anomaly Detection