Greenhouse: A Zero-Positive Machine Learning System for Time-Series Anomaly Detection
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Abstract (arXiv)
This short paper describes our ongoing research on Greenhouse - a zero-positive machine learning system for time-series anomaly detection.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Jan 2018.
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Citing papers per year: 2021: 1; 2022: 3; 2023: 3.
7 papers.
- 2022Deep Learning for Time Series Anomaly Detection: A Surveycited by 130
- 2022Local Evaluation of Time Series Anomaly Detection Algorithmscited by 79
- 2022Series2Graph: Graph-based Subsequence Anomaly Detection for Time Seriescited by 13
- 2021SALAD: Self-Adaptive Lightweight Anomaly Detection for Real-time Recurrent Time Seriescited by 8
- 2023RePAD2: Real-Time, Lightweight, and Adaptive Anomaly Detection for Open-Ended Time Seriescited by 6
- 2023NP-Free: A Real-Time Normalization-free and Parameter-tuning-free Representation Approach for Open-ended Time Seriescited by 1
- 2023Impact of Deep Learning Libraries on Online Adaptive Lightweight Time Series Anomaly Detectioncited by 1
- 2023Impact of Deep Learning Libraries on Online Adaptive Lightweight Time Series Anomaly Detection30 Apr 2023
- 2023NP-Free: A Real-Time Normalization-free and Parameter-tuning-free Representation Approach for Open-ended Time Series12 Apr 2023
- 2023RePAD2: Real-Time, Lightweight, and Adaptive Anomaly Detection for Open-Ended Time Series1 Mar 2023
- 2022Deep Learning for Time Series Anomaly Detection: A Survey9 Nov 2022
- 2022Series2Graph: Graph-based Subsequence Anomaly Detection for Time Series25 Jul 2022
- 2022Local Evaluation of Time Series Anomaly Detection Algorithms27 Jun 2022
- 2021SALAD: Self-Adaptive Lightweight Anomaly Detection for Real-time Recurrent Time Series19 Apr 2021