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: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:2610.01223v1 Announce Type: cross
Abstract: Time-series anomaly detection trades off predictive accuracy, computational efficiency, and interpretability. We use a large language model not as th...
By David Berghaus
arXiv:2610.20243v1 Announce Type: new
Abstract: Anomaly detection is a fundamental task for time-series analytics, with important implications for the downstream performance of many applications. Des...
By Christos Panourgias, Roberto Stanzione, Adrien Petralia, Themis Palpanas, Paul Boniol
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
arXiv:2509. 05663v4 Announce Type: replace Abstract: Truly unsupervised approaches for time series anomaly detection are rare in the literature.
By Lucas Correia, Jan-Christoph Goos, Thomas B\"ack, Anna V. Kononova
arXiv:2603. 10926v2 Announce Type: replace-cross Abstract: Automotive anomaly detectors are often selected from accuracy only benchmarks on workstation class hardware, whereas in-vehicle monitoring requires predictable scoring latency under limited CPU parallelism.
By Kadir-Kaan \"Ozer, Ren\'e Ebeling, Markus Enzweiler
arXiv:2609.21801v1 Announce Type: new
Abstract: We study how far a simple statistical pipeline can go on univariate time series anomaly detection under a strict selection protocol. The method extract...
By Youssef Attia El Hili, Malik Tiomoko, Corinne Ancourt
arXiv:2609.23883v1 Announce Type: new
Abstract: We present a collaborative streaming anomaly detection system for high-speed data streams that explicitly integrates human analysts into the decision l...
By Diogo Risca, Afonso Louren\c{c}o, Ricardo Martins, Goreti Marreiros
arXiv:2608. 15965v1 Announce Type: new Abstract: Progress in streaming, edge-level graph anomaly detection (GAD) has been marked by increasingly elaborate architectures, from count-min-sketch chi square tests to memory-augmented attention networks.
By Omair Shafi Ahmed, Zohair Shafi
arXiv:2509. 06419v2 Announce Type: replace Abstract: Time-series anomaly detection is crucial in AIOps for maintaining large-scale service reliability.
By Xudong Mou, Rui Wang, Tiejun Wang, Zexin Wu, Fangda Guo, Jie Sun, Shiru Chen, Penghao Zhang, Tiezi Zhang, Tianyu Wo, Hao Peng, Chunming Hu, Xudong Liu, Renyu Yang
arXiv:2610.28560v1 Announce Type: new
Abstract: Visual anomaly detection aims to detect abnormal objects, while anomaly samples are often scarce during model training. Assembling multiple pretrained...
By Shangquan Sun, Wenjun Luo, Xu He, Yoonsoo Lee, Shijian Lu