LLM-Assisted Logic Rule Learning: Scaling Human Expertise for Time Series Anomaly Detection

TL;DR

This paper introduces a framework that uses large language models to encode human expertise into interpretable logic rules for supply chain time series anomaly detection, improving accuracy and interpretability over traditional methods.

Contribution

The paper presents a novel LLM-based framework for encoding domain knowledge into logic rules, enabling scalable, interpretable, and accurate anomaly detection in supply chain data.

Findings

Outperforms unsupervised methods in detection accuracy.

Provides more interpretable results than black-box models.

Achieves low latency suitable for production deployment.

Abstract

Time series anomaly detection is critical for supply chain management to take proactive operations, but faces challenges: classical unsupervised anomaly detection based on exploiting data patterns often yields results misaligned with business requirements and domain knowledge, while manual expert analysis cannot scale to millions of products in the supply chain. We propose a framework that leverages large language models (LLMs) to systematically encode human expertise into interpretable, logic-based rules for detecting anomaly patterns in supply chain time series data. Our approach operates in three stages: 1) LLM-based labeling of training data instructed by domain knowledge, 2) automated generation and iterative improvements of symbolic rules through LLM-driven optimization, and 3) rule augmentation with business-relevant anomaly categories supported by LLMs to enhance interpretability. The experiment results showcase that our approach outperforms the unsupervised learning methods in both detection accuracy and interpretability. Furthermore, compared to direct LLM deployment for time series anomaly detection, our approach provides consistent, deterministic results with low computational latency and cost, making it ideal for production deployment. The proposed framework thus demonstrates how LLMs can bridge the gap between scalable automation and expert-driven decision-making in operational settings.