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

2026-08-25

AI Summary

本文提出了一种基于快速傅里叶变换的证据增强零样本时间序列异常检测框架，通过添加频域证据来改进现有方法，以更好地捕捉时间序列中的周期性和局部频谱变化。

Abstract

Time-series anomalies can appear not only as pointwise deviations but also as changes in recurring temporal structure, such as shifted periodicity or localized oscillatory fluctuations. However, existing LLM-based time-series anomaly detection methods mainly expose time-domain evidence through indexed values, plots, or de-seasonalized representations, leaving spectral structure implicit. We propose an evidence-augmented zero-shot TSAD framework that preserves indexed de-seasonalized observations while adding compact frequency-domain evidence computed with the Fast Fourier Transform (FFT). The evidence is constructed at two resolutions: global frequency-domain evidence summarizes sequence-level periodic context, while local frequency-domain evidence captures time-localized spectral departures. Experiments on AnomLLM with InternVL2-LLaMA3-76B, Qwen2.5-VL-72B-Instruct, Gemini-2.5-Flash, and GPT-4o, together with evaluation on the TSB-AD-U subset, show that explicit frequency-domain evidence improves LLM-based TSAD baselines. These results suggest that frequency-domain evidence can complement indexed and de-seasonalized time-domain inputs for zero-shot LLM-based TSAD.

Problem

• time-series anomaly detection
• frequency-domain evidence
• LLM-based
• spectral structure
• de-seasonalized

Innovation

• Frequency-Domain Evidence
• Zero-Shot TSAD
• Fast Fourier Transform (FFT)
• Global and Local Evidence