Benchmarking anomaly detection methods: Insights from the UCR Time Series Anomaly Archive

Abstract

Anomaly detection, vital for identifying deviations from normative data patterns, is particularly crucial in sensor-driven real-world applications, which predominantly involve temporal data in the form of time series. Traditional evaluation of anomaly detection methods has relied on public benchmark datasets. Yet, recent revelations have uncovered inherent flaws and inadequacies in these datasets, casting doubt on the perceived progress in the field. To address this challenge, the UCR Time Series Anomaly Archive has been recently proposed—a meticulously curated database comprising 250 time series—designed to provide a robust and error-free benchmark for anomaly detection research. This paper comprehensively evaluates state-of-the-art anomaly detection techniques using the UCR Time Series Anomaly Archive. Our findings demonstrate the efficacy of current methods in accurately detecting anomalies across an important portion of datasets without additional optimization, underscoring the archive's utility as a foundational baseline for future research and development in anomaly detection methodologies.