Navigating the metric maze: a taxonomy of evaluation metrics for anomaly detection in time series

• Published in: Data mining and knowledge discovery Vol. 38; no. 3; pp. 1027 - 1068
• Main Authors: Sørbø, Sondre, Ruocco, Massimiliano
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.05.2024; Springer Nature B.V
• Subjects: Anomalies; Artificial Intelligence; Chemistry and Earth Sciences; Computer Science; Data Mining and Knowledge Discovery; Information Storage and Retrieval; Physics; Statistics for Engineering; Taxonomy; Time series; Evaluation; Taxonomy; Anomaly detection; Time series

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. Despi...