Enhancing Time Series Anomaly Detection: A Knowledge Distillation Approach with Image Transformation

Abstract

Anomaly detection is critical in safety-sensitive fields, but faces challenges from scarce abnormal data and costly expert labeling. Time series anomaly detection is relatively challenging due to its reliance on sequential data, which imposes high computational and memory costs. In particular, it is often composed of real-time collected data that tends to be noisy, making preprocessing an essential step. In contrast, image anomaly detection has leveraged advancements in technologies for analyzing spatial patterns and visual features, achieving high accuracy and promoting research aimed at improving efficiency. We propose a novel framework that bridges image anomaly detection with time series data. Using Gramian Angular Field (GAF) transformations, we convert time series into images and apply state-of-the-art techniques, Reverse Distillation (RD) and EfficientAD (EAD), for efficient and accurate anomaly detection. Tailored preprocessing and transformations further enhance performance and interoperability. When evaluated on the multivariate time series anomaly detection dataset Secure Water Treatment (SWaT) and the univariate datasets University of California, Riverside (UCR) and Numenta Anomaly Benchmark (NAB), our approach demonstrated high recall overall and achieved approximately 99% F1 scores on some univariate datasets, proving its effectiveness as a novel solution for time series anomaly detection.