A Robust Graph-Based Algorithm for Detection and Characterization of Anomalies in Noisy Multivariate Time Series

@article{Cheng2008ARG,
  title={A Robust Graph-Based Algorithm for Detection and Characterization of Anomalies in Noisy Multivariate Time Series},
  author={Haibin Cheng and Pang-Ning Tan and Christopher Potter and Steven A. Klooster},
  journal={2008 IEEE International Conference on Data Mining Workshops},
  year={2008},
  pages={349-358}
}
Detection of anomalies in multivariate time series is an important data mining task with potential applications in medical diagnosis, ecosystem modeling, and network traffic monitoring. In this paper, we present a robust graph-based algorithm for detecting anomalies in noisy multivariate time series data. A key feature of the algorithm is the alignment of kernel matrices constructed from the time series. The aligned kernel enables the algorithm to capture the dependence relationship between… CONTINUE READING