Anomaly Detection in Real-Valued Multidimensional Time Series

    •  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 = {}
      • }
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  • Research Areas:

    Artificial Intelligence, Data Analytics


We present a new algorithm for detecting anomalies in real valued multidimensional time series. Our algorithm uses an exemplar-based model that is used to detect anomalies in single dimensions of the time series and a function that predicts one dimension from a related one to detect anomalies in multiple dimensions. The algorithm is shown to work on a variety of different types of time series as well as to detect a variety of different types of anomalies. We compare our algorithm to other algorithms for both one-dimensional and multidimensional time series and demonstrate that it improves over the state-of-the-art.


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