Maximally Divergent Intervals for Anomaly Detection

Understand

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.