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arXiv AI By Jungwook Seo, Sangwon Son, Minjeong Kim, Seungmin Han, Seojin Yoo, Sungyong Baik

Structured Frequency-Domain Evidence for LLM-Based Time-Series Anomaly Detection

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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.

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