Tae Jun Lee

Nesime Tatbul, Tae Jun Lee, Stan Zdonik et al.

Classical 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.

Tae Jun Lee, Justin Gottschlich, Nesime Tatbul et al.

Classical anomaly detection is principally concerned with point-based anomalies, anomalies that occur at a single data point. In this paper, we present a new mathematical model to express range-based anomalies, anomalies that occur over a range (or period) of time.

Tae Jun Lee, Justin Gottschlich, Nesime Tatbul et al.

This short paper describes our ongoing research on Greenhouse - a zero-positive machine learning system for time-series anomaly detection.