Navigating the Metric Maze: A Taxonomy of Evaluation Metrics for Anomaly Detection in Time Series (2023)
Data Mining and Knowledge Discovery
Abstract The field of time series anomaly detection is constantly advancing, with several methods available, making it a challenge to determine the most appropriate method for a specific domain. The evaluation of these methods is facilitated by the use of metrics, which vary widely in their properties. Despite the existence of new evaluation metrics, there is limited agreement on which metrics are best suited for specific scenarios and domain, and the most commonly used metrics have faced criticism in the literature.