Research License — EBAD

Exemplar-Based Anomaly Detection for detecting anomalies in time series.

Anomaly detection in real-valued time series has important applications in many diverse areas. We have developed a general algorithm for detecting anomalies in real-valued time series that is computationally very efficient. Our algorithm is exemplar-based which means a set of exemplars are first learned from a normal time series (i.e. not containing any anomalies) which effectively summarizes all normal windows in the training time series. Anomalous windows of a testing time series can then be efficiently detected using the exemplar-based model.

The provided code implements our hierarchical exemplar learning algorithm, our exemplar-based anomaly detection algorithm, and a baseline brute-force Euclidean distance anomaly detection algorithm. Two simple time series are also provided to test the code.

  •  Jones, M., Nikovski, D., Imamura, M., Hirata, T., "Anomaly Detection in Real-valued Multidimensional Time Series", ASE Bigdata/Socialcom/Cyber Security Conference, June 2014.
    BibTeX TR2014-042 PDF Software
    • @inproceedings{Jones2014jun,
    • author = {Jones, M. and Nikovski, D. and Imamura, M. and Hirata, T.},
    • title = {Anomaly Detection in Real-valued Multidimensional Time Series},
    • booktitle = {ASE Bigdata/Socialcom/Cyber Security Conference},
    • year = 2014,
    • month = jun,
    • publisher = {Academy of Science and Engineering (ASE)},
    • isbn = {978-1-62561-000-3},
    • url = {https://www.merl.com/publications/TR2014-042}
    • }

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