The paper introduces a zero‑shot time‑series anomaly detection framework that augments traditional indexed, de‑seasonalized observations with compact frequency‑domain evidence derived from the Fast Fourier Transform. This evidence is provided at two resolutions: a global summary of sequence‑level periodicity and a local snapshot of time‑localized spectral deviations. Experiments on the AnomLLM benchmark using several large language models—including InternVL2‑LLaMA3‑76B, Qwen2.5‑VL‑72B‑Instruct, Gemini‑2.5‑Flash, and GPT‑4o—demonstrate that incorporating explicit frequency‑domain evidence improves anomaly detection performance over existing LLM‑based baselines.
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.
The paper introduces SAGE, a multi‑agent framework that uses specialized analyzers to diagnose univariate time‑series anomalies by examining point, structural, seasonal, and pattern deviations. Each analyzer produces numerical evidence and visual diagnostics, which a Detector consolidates into intervals, candidate types, and confidence scores, and a Supervisor converts these into analyst‑friendly reports. Experiments on Yahoo S5, KPI, and WSD datasets show SAGE achieving the highest average Point‑F1 score (66.26) and receiving higher usefulness ratings in a blind human study.
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.
arXiv:2604. 13924v3 Announce Type: replace-cross Abstract: Time-series anomaly detection (TSAD) is critical in domains such as industrial monitoring, healthcare, and cybersecurity, but it remains challenging due to rare and heterogeneous anomalies and the scarcity of labelled data.
arXiv:2606. 18898v1 Announce Type: new Abstract: Multivariate time series anomaly detection (MTSAD) is critical for a wide range of application areas, such as industrial monitoring, cybersecurity, or healthcare.
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.
arXiv:2603. 11756v2 Announce Type: replace Abstract: Deep generative models for anomaly detection in multivariate time-series are typically trained by maximizing observed data likelihood.
arXiv:2609.38789v1 Announce Type: new Abstract: Reconstruction errors in multivariate time-series anomaly detection may not reliably distinguish abnormal behavior from benign deviations. Language-der...
arXiv:2606. 09874v1 Announce Type: new Abstract: Reconstruction-based methods are widely used for time series anomaly detection, where models are trained to reconstruct subsequences, and anomalies are identified through reconstruction errors.
arXiv:2602. 13807v2 Announce Type: replace Abstract: Time series anomaly detection is critical in many real-world applications, where effective solutions must localize anomalous regions and support reliable decision-making under complex settings.
CAST is a framework for generating anomalous time series that addresses the scarcity and heterogeneity of anomaly data. It uses a two‑stage approach: pretraining on abundant normal data to learn system dynamics, then finetuning with anomaly structure representations to capture diverse anomaly morphologies. Experiments on real‑world datasets show that CAST outperforms existing methods in both generation quality and downstream task performance.
arXiv:2609.26820v1 Announce Type: cross Abstract: Physiological time series such as electrocardiograms (ECG) and electroencephalograms (EEG) exhibit complex temporal structure, substantial acquisitio...