Detecting Time Series Anomalies Like an Expert: A Multi-Agent LLM Framework with Specialized Analyzers
TL;DR
This work introduces SAGE (Specialized Analyzer Group for Expert-like Detection), a multi-agent framework for evidence-grounded diagnosis of univariate time series, which achieves an average Point-F1 of 66.26, the highest among the evaluated methods.
Abstract
Time-series anomaly detection often returns scores or intervals, while analysts need to understand the abnormal behavior and the evidence supporting it. We introduce SAGE (Specialized Analyzer Group for Expert-like Detection), a multi-agent framework for evidence-grounded diagnosis of univariate time series. Four specialized Analyzers examine point, structural, seasonal, and pattern anomalies using numerical tools and diagnostic visualizations. A Detector integrates their evidence into intervals, candidate types, and evidence-strength confidence scores; a Supervisor translates these records into analyst-facing reports. Synthetic in-context references are constructed from normal-reference training segments, reducing dependence on real anomalous demonstrations. Across Yahoo S5, KPI, and WSD, SAGE achieves an average Point-F1 of 66.26, the highest among the evaluated methods. Controlled synthetic evaluation examines localization and type diagnosis, while component ablations support the detection contribution of specialized evidence generation. In a method-blind human study, evaluators rate SAGE's diagnostic outputs as more useful than those of the compared methods.
