Tatbul, NesimeLee, Tae JZdonik, StanAlam, MejbahGottschlich, Justin E2023-05-222023-05-222018-01-012020-12-18https://repository.upenn.edu/handle/20.500.14332/8495Classical anomaly detection is principally concerned with point-based anomalies, those anomalies that occur at a single point in time. Yet, many real-world anomalies are range-based, meaning they occur over a period of time. Motivated by this observation, we present a new mathematical model to evaluate the accuracy of time series classification algorithms. Our model expands the well-known Precision and Recall metrics to measure ranges, while simultaneously enabling customization support for domain-specific preferences.Presentation