LEARN-TS is a method for multivariate time‑series anomaly detection that uses a frozen language model to generate two semantic representations: one that captures window‑specific observation context without exact numerical values, and another that provides a fixed, dataset‑agnostic normality prompt. These representations guide a patch‑masked reconstruction process, allowing the model to estimate normality discrepancy and produce timestamp‑level anomaly evidence. Experiments on four benchmarks show that LEARN‑TS achieves the best mean performance in 13 of 16 dataset‑metric comparisons, and ablation studies confirm the benefits of observation conditioning, joint normality alignment, and semantic references.
By Jahyeob Koo, Kio Yun, Byoungmo Koo, Jun-Geol Baek
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
The paper surveys recent advances in time series anomaly detection (TSAD), highlighting new architectures such as diffusion models, state space models, and large language models. It introduces a refined anomaly taxonomy, evaluates existing metrics through extensive experiments, and releases an open-source benchmark with algorithms, datasets, and evaluation tools. The authors also identify open research challenges, including dynamic thresholding, interpretability, zero‑shot transfer, multi‑modal alignment, and foundational models for time series.
By Zhijie Zhong, Zhiwen Yu, Pengyu Li, Liujian Zhang, Qiaoli Ni, Jiang Jun, Jianming Lv, Kaixiang Yang, C. L. Philip Chen
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
Structured Temporal Causality for Interpretable Multivariate Time Series Anomaly Detection introduces OracleAD, an unsupervised framework that encodes each variable’s past into a causal embedding to jointly predict the present and reconstruct the input window. The embeddings are projected via self‑attention into a shared latent space, capturing dynamic spatial relationships that are aligned to a Stable Latent Structure (SLS) representing normal-state relationships. Anomalies are detected through a dual scoring mechanism based on prediction error and deviation from the SLS, allowing fine‑grained diagnosis and root‑cause identification at the variable level.
By Dongchan Cho, Jiho Han, Keumyeong Kang, Minsang Kim, Honggyu Ryu, Namsoon Jung
arXiv:2602. 08868v2 Announce Type: replace-cross Abstract: Time-series anomaly detection (TSAD) with multimodal large language models (MLLMs) is an emerging area, yet a persistent challenge remains: MLLMs rely on coarse time-series heuristics but struggle with multi-dimensional, detailed reasoning, which is vital for understanding complex time-series data.
By Junru Zhang, Lang Feng, Haoran Shi, Xu Guo, Han Yu, Yabo Dong, Duanqing Xu
arXiv:2609.08200v1 Announce Type: new
Abstract: Token-level text anomaly detection, as an emerging trend of text anomaly detection, moves beyond coarse-grained document-level detection by localizing...
By Kehan Yan, Yue Tan, Qingfeng Chen, Shiyuan Li, Yu Zheng, Yixin Liu
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
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.
By Dezheng Wang, Tong Chen, Guansong Pang, Congyan Chen, Shihua Li, Hongzhi Yin
TENET is a Time-point Encoding Network designed for unsupervised multivariate time series anomaly detection. It reframes the task as estimating current-point system-state compatibility by constructing a latent representation of the target time point from variable-level temporal context within an input window. The model uses a temporal layer to retrieve context relative to the current point, encodes time with point-centered embeddings, and adaptively mixes this context with the current representation, then evaluates the latent state with a fixed standard-normal negative log-likelihood score, achieving state-of-the-art performance across benchmark datasets.
By Dongchan Cho, Juwon Hwang, Keumyeong Kang, Jiho Han, Namsoon Jung
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
arXiv:2608. 03391v1 Announce Type: new Abstract: Precise anomaly localization over long-context time series is a crucial task in monitoring applications across clinical care, industrial operations, financial services, and logistics, where brief evidence may hide inside long spans of high-frequency data.
By Nicolas Zumarraga, Lorenzo Steno, Ning Wang, Max Rosenblattl, Thomas Kaar, Maxwell A. Xu, Kevin O'Sullivan, Markus Kreft, Elgar Fleisch, Paul Schmiedmayer, Patrick Langer, Robert Jakob