DAMP: accurate time series anomaly detection on trillions of datapoints and ultra-fast arriving data streams

Author:

Lu YueORCID,Wu Renjie,Mueen Abdullah,Zuluaga Maria A.,Keogh Eamonn

Funder

National Science Foundation: NSF

French National Research Agency

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Computer Science Applications,Information Systems

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. An Interactive Dive into Time-Series Anomaly Detection;2024 IEEE 40th International Conference on Data Engineering (ICDE);2024-05-13

2. Unraveling the ‘Anomaly’ in Time Series Anomaly Detection: A Self-supervised Tri-domain Solution;2024 IEEE 40th International Conference on Data Engineering (ICDE);2024-05-13

3. Enhancing data efficiency for autonomous vehicles: Using data sketches for detecting driving anomalies;Machine Learning with Applications;2024-03

4. Multidimensional Matrix Profile-Based Anomaly Detection for Power Grid with High Variable Renewable Energy Penetration;2023 2nd Asia Power and Electrical Technology Conference (APET);2023-12-28

5. Matrix Profile XXX: MADRID: A Hyper-Anytime and Parameter-Free Algorithm to Find Time Series Anomalies of all Lengths;2023 IEEE International Conference on Data Mining (ICDM);2023-12-01

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