LLM-AP: LLM-Based Anomaly Detection with Synthetic Time-Series Data Augmentation

Abstract

Detecting anomalies in time-series data has significant implications in various domains, including the early detection of equipment and facility conditions in industrial settings and the identification of risk factors in financial markets. However, sensor-based time-series data obtained from real-world sites predominantly consist of normal patterns, with anomalies being extremely rare, resulting in insufficient data for model training. In this paper, we present a method that addresses the imbalance between normal and anomaly data by transforming time-series data into a structured sentence format and using a large language model to generate and augment diverse anomaly scenarios. The generated sentences are then converted back into time-series data and subjected to an internal validation process. This approach enables the construction of a broader range of anomaly data than existing techniques.The proposed method is designed for univariate, continuous valued time-series and is anticipated to serve as an effective data augmentation tool, with extension to broader domains left to future work.