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.
@article{arxiv.1610.06761,
title = {Maximally Divergent Intervals for Anomaly Detection},
author = {Erik Rodner and Björn Barz and Yanira Guanche and Milan Flach and Miguel Mahecha and Paul Bodesheim and Markus Reichstein and Joachim Denzler},
journal= {arXiv preprint arXiv:1610.06761},
year = {2016}
}