LLM-Generated Feature Pools for Time Series Anomaly Detection

2026-09-18

AI Summary

研究使用统计方法在时间序列异常检测中通过生成特征池并选择最优特征子集，达到优于现有神经和统计模型的效果。

Abstract

We study how far a simple statistical pipeline can go on univariate time series anomaly detection under a strict selection protocol. The method extracts a small pool of statistics over sliding windows, scores each window with a transductive robust (MAD) model, and selects a feature subset per domain on a held-out tuning split. On TSB-AD-U it reaches $0.529$ per-series VUS-PR, above the best neural ($0.45$) and statistical ($0.44$) entries on the public leaderboard and within $0.06$ of the strongest pretrained foundation model, several of which use more supervision than ours. Ablations locate the cause: across three selection strategies and a hindsight oracle the score moves by $0.031$, and across the aggregation grid by $0.096$, while changing the candidate pool moves it by $0.226$. The candidate pool sets the ceiling; the search over it is second-order. We therefore generate a pool per domain by prompting a multimodal LLM with in-context example windows from that domain. The generated pools match the hand-crafted one under matched selection, and the two cover different domains: selecting over their union improves on the generated pool in all twelve generator-seed pairs and lifts the pipeline to $0.588$, matching the performance of the best entry on the leaderboard.

Problem

• time series
• anomaly detection
• statistical pipeline
• feature selection
• LLM

Innovation

• LLM-Generated Feature Pools
• Time Series Anomaly Detection
• Transductive Robust Model
• Feature Subset Selection
• Domain-Specific Adaptation