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: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.
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:2609.39257v1 Announce Type: cross Abstract: Accurate forecasting of electricity production is essential for maintaining the operational efficiency and strategic planning of energy utilities. In...
arXiv:2608. 10587v1 Announce Type: new Abstract: Artificial Intelligence (AI)-based prospective anomaly detection methods are increasingly deployed in high-dimensional and nonlinear settings.
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.
TraceBench is a simulation-based framework that generates controlled root‑cause attribution tasks for time‑series data. In each task, an LLM agent must determine whether a system parameter was altered during a simulation of a physical dynamical system and identify the altered parameter. The authors evaluated four LLM agents on tasks derived from three interpretable mechanical systems, finding that agents perform better with domain context, rely mainly on numerical console output, and struggle more when required to produce Python scripts for labeling than when submitting direct predictions.
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.
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: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:2608. 14270v1 Announce Type: new Abstract: Time series analysis in high-stakes domains relies on recurring data releases, where new observations can alter the evidence base and the validity of later conclusions.
arXiv:2606. 28467v1 Announce Type: cross Abstract: Appliance-level energy monitoring in office buildings produces noisy alerts that non-expert facility managers struggle to use.
arXiv:2601. 23204v2 Announce Type: replace Abstract: Time series data are integral to critical applications across domains such as finance, healthcare, transportation, and environmental science.