← Back to all news
arXiv AI By Romain Hermary, Nesryne Mejri, Djamila Aouada

Evaluating Language Model Features for Zero-Shot Time-Series Anomaly Detection Under Isolation Assumption

Read the original on arXiv AI →

The paper investigates whether large language models (LLMs) can serve as feature extractors for zero‑shot multivariate time‑series anomaly detection (MTSAD). By tokenising each time step individually and analysing the separability of the resulting feature spaces, the authors evaluate the representations using an unsupervised Isolation Forest. They also examine whether the performance stems from the LLMs’ language pre‑training or from other factors, providing empirical insights into cross‑modal foundation models for anomaly detection.

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.

Related stories

arXiv Machine Learning
Oct 2

LEARN-TS: LLM-Enhanced Alignment and Reconstruction with Normality Guidance for Multivariate Time-Series Anomaly Detection

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
llmsmultimodalbenchmarkssafety
More like this →
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
ragbenchmarkssafety
More like this →
arXiv Machine Learning
Oct 23

Future of Time Series Anomaly Detection: From Historical Review to Unified Outlook

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
llmsdiffusionmultimodalbenchmarkssafety
More like this →
arXiv Machine Learning
Oct 22

Structured Temporal Causality for Interpretable Multivariate Time Series Anomaly Detection

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
ragdiffusionbenchmarks
More like this →
arXiv AI
Jun 2

AnomSeer: Reinforcing Multimodal LLMs to Reason for Time-Series Anomaly Detection

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
llmsreinforcement-learningmultimodal
More like this →