Have an LLM Write Your Anomaly Detector: Autonomous Discovery of Compact, Interpretable Detectors for Time Series

2026-10-01

AI Summary

This study addresses the persistent challenge of simultaneously achieving accuracy, efficiency, and interpretability in time series anomaly detection. We propose an autonomous program search framework driven by large language models (LLMs). Departing from paradigms that employ LLMs directly as detectors, this work pioneers an approach wherein the LLM autonomously generates NumPy code to construct compact and transparent detectors. By integrating local spectral feature analysis with covariance-aware distance algorithms, the proposed method operates entirely without neural network training. Evaluated on the TSB-AD benchmark, our approach comprehensively outperforms existing baselines, delivering high accuracy, computational efficiency, and GPU-free deployment. This research establishes a novel paradigm for interpretable anomaly detection.

Abstract

Time-series anomaly detection trades off predictive accuracy, computational efficiency, and interpretability. We use a large language model not as the detector but as the author of one: an autonomous research loop in which the model repeatedly edits a single short NumPy program under a leakage-free objective, keeping the best-scoring detector it finds. The loop discovers two compact detectors, one for univariate and one for multivariate series, that describe short windows by their local spectral features and compare them with the training-region distribution through a covariance-aware distance. On the TSB-AD benchmark these detectors lead the field across metrics, ahead of the strongest classical, deep, and foundation-model baselines including Time-RCD, yet they train no network and use no GPU, and the multivariate detector is faster than every similarly performing baseline. LLM-driven program search is thus a practical route to accurate, efficient, and transparent detectors.

Problem

• Time-series anomaly detection
• Interpretability
• Computational efficiency
• Predictive accuracy

Innovation

• Large Language Model
• Time Series Anomaly Detection
• Program Synthesis
• Interpretability
• Spectral Features