38 citations · 77 across the 3 of their papers we have counts for
3 papers
cs.LG2018★ 38 cited
Precision and Recall for Time Series
Nesime Tatbul, Tae Jun Lee, Stan Zdonik +2
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-base…
cs.AI2018★ 9 cited
Precision and Recall for Range-Based Anomaly Detection
Tae Jun Lee, Justin Gottschlich, Nesime Tatbul +2
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…
cs.AI2018★ 30 cited
Greenhouse: A Zero-Positive Machine Learning System for Time-Series Anomaly Detection
Tae Jun Lee, Justin Gottschlich, Nesime Tatbul +2
This short paper describes our ongoing research on Greenhouse - a zero-positive machine learning system for time-series anomaly detection.