Fast, Parameter-free Time Series Anomaly Detection
Abstract: Time series anomaly detection is a common problem across many domains. Despite the existence of numerous algorithms leveraging deep learning, classical machine learning, and data mining techniques, no dominating approach has emerged. A common challenge is extensive parameter tuning and the high computational costs associated with many existing methods. To address this problem, we propose a parameter-free anomaly detection algorithm, STAN (summary statistics ensemble). STAN applies a set of summary statistics over sliding windows and compares the results to the normal behavior learned during training. STAN’s flexibility allows for integrating different statistical aggregates, which effectively handle diverse types of anomalies. Our evaluation shows that STANachieves a detection accuracy of 60.4%, close to the widely used MERLIN algorithm (63.6%) while reducing execution time by more than an order of magnitude compared to all baselines.
