Software & Data Downloads — EBAD

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.

• Related Publications

   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}
  • }

Access software at https://github.com/merlresearch/EBAD.