arXiv AI By Seunghan Lee, Jun Seo, Jaehoon Lee, Junhyeok Kang, Sangjun Han, Sungdong Yoo, Minjae Kim, Tae Yoon Lim, Dongwan Kang, Hwanil Choi, Soonyoung Lee, Wonbin Ahn

OneShot: Label-Efficient Detector Weighting for Time Series Anomaly Detection

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arXiv Machine Learning
Oct 22

Distilling Multi-Teacher Scoring Principles for Unsupervised Time Series Anomaly Detection

The paper introduces AnoMix, a method that distills the complementary anomaly scoring principles of multiple unsupervised time‑series detectors into a single lightweight model. By normalizing each teacher’s score sequence and applying a multi‑teacher ranking‑distillation objective called CoRank, AnoMix learns a student detector that uses pairwise supervision derived from teacher consensus. Experiments on seven benchmark datasets show that the compact student (under 80k parameters) outperforms individual detectors and static ensembles, and a zero‑shot variant can even surpass large pretrained time‑series foundation models on unseen data.

By Dongchan Cho, Jiho Han, Juwon Hwang, Changhee Min, Minsang Kim, Namsoon Jung
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
arXiv Machine Learning
6d ago

LEAP: a Latent Embedding Anomaly Patch-Transformer for time-series anomaly detection

LEAP is a new time‑series anomaly detection model that avoids reconstruction by using masked prediction and latent‑embedding evidence. It removes patches from a window, predicts them from remaining context, and scores prediction errors as anomaly evidence. The model incorporates three label‑free novelty signals—latent‑embedding novelty, local temporal contrast, and per‑channel novelty—and achieves strong performance on the TSB‑AD benchmark, with statistically significant gains from each component.

By Cristhian Moya-Mota, Ignacio Aguilera-Martos, Diego Garc\'ia-Gil, Juli\'an Luengo