Search Results for author: Tae Jun Lee

Found 3 papers, 1 papers with code

Precision and Recall for Time Series

4 code implementations NeurIPS 2018 Nesime Tatbul, Tae Jun Lee, Stan Zdonik, Mejbah Alam, Justin Gottschlich

Classical anomaly detection is principally concerned with point-based anomalies, those anomalies that occur at a single point in time.

Anomaly Detection General Classification +1

Precision and Recall for Range-Based Anomaly Detection

no code implementations9 Jan 2018 Tae Jun Lee, Justin Gottschlich, Nesime Tatbul, Eric Metcalf, Stan Zdonik

Classical anomaly detection is principally concerned with point-based anomalies, anomalies that occur at a single data point.

Anomaly Detection

Greenhouse: A Zero-Positive Machine Learning System for Time-Series Anomaly Detection

no code implementations9 Jan 2018 Tae Jun Lee, Justin Gottschlich, Nesime Tatbul, Eric Metcalf, Stan Zdonik

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

Anomaly Detection BIG-bench Machine Learning +1

Cannot find the paper you are looking for? You can Submit a new open access paper.