arXiv AI July 3, 2026 By Dezheng Wang, Tong Chen, Guansong Pang, Congyan Chen, Shihua Li, Hongzhi Yin

LEFT: Learnable Fusion of Tri-view Tokens for Unsupervised Time Series Anomaly Detection

arXiv:2602. 08638v2 Announce Type: replace-cross Abstract: As a fundamental data mining task, unsupervised time series anomaly detection (TSAD) aims to build a model for identifying abnormal timestamps without assuming the availability of annotations.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. 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 AI

3d ago

WinoTS: Wavelet-based Self-Distillation for Time Series Models

WinoTS introduces a wavelet‑based self‑distillation framework for time‑series models that uses time‑frequency augmentations to create multi‑scale structural views, avoiding distortion of signal dynamics. The method outperforms state‑of‑the‑art baselines in long‑term forecasting, cross‑domain zero‑shot transfer, and unsupervised anomaly detection, and linear probing on frozen representations often beats fully supervised training from scratch. Ablation studies show WinoTS is architecture‑agnostic and demonstrates that time‑frequency transformations offer a principled alternative to vision‑style spatial augmentations.

By Noam Major, Kathy Razmadze, Yoli Shavit

computer-vision efficiency benchmarks

arXiv AI

Jul 2

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.

By Jinju Park, Seokho Kang

llms rag benchmarks

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

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

Jul 17

VAN-AD: Visual Masked Autoencoder with Normalizing Flow For Time Series Anomaly Detection

arXiv:2603. 26842v3 Announce Type: replace-cross Abstract: Time series anomaly detection (TSAD) is essential for maintaining the reliability and security of IoT-enabled service systems.

By PengYu Chen, Shang Wan, Xiaohou Shi, Yuan Chang, Yan Sun, Sajal K. Das

llms multimodal benchmarks

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

WinoTS: Wavelet-based Self-Distillation for Time Series Models

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

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

NeuCoReClass AD: Redefining Self-Supervised Time Series Anomaly Detection

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