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Maximally Divergent Intervals for Anomaly Detection

21 October 2016
E. Rodner
Björn Barz
Y. Guanche
M. Flach
Miguel D. Mahecha
P. Bodesheim
Markus Reichstein
Joachim Denzler
    AI4TS
ArXiv (abs)PDFHTML
Abstract

We present new methods for batch anomaly detection in multivariate time series. Our methods are based on maximizing the Kullback-Leibler divergence between the data distribution within and outside an interval of the time series. An empirical analysis shows the benefits of our algorithms compared to methods that treat each time step independently from each other without optimizing with respect to all possible intervals.

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