arXiv AI July 2, 2026 By Jinju Park, Seokho Kang

PaAno: Patch-Based Representation Learning for Time-Series Anomaly Detection

arXiv:2602. 01359v3 Announce Type: replace-cross Abstract: Although recent studies on time-series anomaly detection have increasingly adopted ever-larger neural network architectures such as transformers and foundation models, they incur high computational costs and memory usage, making them impractical for real-time and resource-constrained scenarios.

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

arXiv Machine Learning

Jun 19

PaAno+: Multiscale Encoding and Cross-Variable Attention for Time Series Anomaly Detection

arXiv:2606. 20055v1 Announce Type: new Abstract: Time-series anomaly detection has significant practical value for industrial and medical monitoring, as well as other critical domains.

By Youji Zhu, Hongbing Wang, Wenchao Liu, Xiaodong Liu, Xiangguang Xiong

llms rag benchmarks

arXiv Machine Learning

Jun 10

Disjoint or Overlapping? Inference Windowing for Reconstruction-Based Time Series Anomaly Detection

arXiv:2606. 09874v1 Announce Type: new Abstract: Reconstruction-based methods are widely used for time series anomaly detection, where models are trained to reconstruct subsequences, and anomalies are identified through reconstruction errors.

By Guillaume Coulaud (UM, IROKO), Reza Akbarinia (IROKO), Florent Masseglia (IROKO)

llms benchmarks

arXiv Machine Learning

6d ago

Patch-based Memory Gate Model in Time Series Foundation Model

arXiv:2509. 18751v4 Announce Type: replace Abstract: Recently reconstruction-based deep models have been widely used for time series anomaly detection, but as their capacity and generalization capability increase, these models tend to over-generalize, often reconstructing unseen anomalies accurately.

By Samuel Yoon, Jongwon Kim, Juyoung Ha, Young Myoung Ko

fine-tuning 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

arXiv AI

Jun 2

ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection

arXiv:2606. 01300v1 Announce Type: cross Abstract: Time series anomaly detection is a crucial task in various domains, including finance, healthcare, and industry.

By Uzair Khan, Luigi Capogrosso, Francesco Biondani, Michele Magno, Franco Fummi, Francesco Setti, Marco Cristani

rag benchmarks

Related stories

PaAno+: Multiscale Encoding and Cross-Variable Attention for Time Series Anomaly Detection

Disjoint or Overlapping? Inference Windowing for Reconstruction-Based Time Series Anomaly Detection

Patch-based Memory Gate Model in Time Series Foundation Model

NeuCoReClass AD: Redefining Self-Supervised Time Series Anomaly Detection

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

ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection