  Anomaly detection is a common analytical task that aims to identify rare
cases that differ from the typical cases that make up the majority of a
dataset. When applied to the analysis of event sequence data, the task of
anomaly detection can be complex because the sequential and temporal nature of
such data results in diverse definitions and flexible forms of anomalies. This,
in turn, increases the difficulty in interpreting detected anomalies. In this
paper, we propose an unsupervised anomaly detection algorithm based on
Variational AutoEncoders (VAE) to estimate underlying normal progressions for
each given sequence represented as occurrence probabilities of events along the
sequence progression. Events in violation of their occurrence probability are
identified as abnormal. We also introduce a visualization system, EventThread3,
to support interactive exploration and interpretations of anomalies within the
context of normal sequence progressions in the dataset through comprehensive
one-to-many sequence comparison. Finally, we quantitatively evaluate the
performance of our anomaly detection algorithm and demonstrate the
effectiveness of our system through a case study.
