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

This framework leverages LLMs to encode human expertise into interpretable logic rules for time series anomaly detection in supply chains. It outperforms unsupervised methods in accuracy and interpretability and offers consistent, low-cost results suitable for production deployment, bridging the gap between automation and expert decision-making.