  Continuous-time event sequences represent discrete events occurring in
continuous time. Such sequences arise frequently in real-life. Usually we
expect the sequences to follow some regular pattern over time. However,
sometimes these patterns may be interrupted by unexpected absence or
occurrences of events. Identification of these unexpected cases can be very
important as they may point to abnormal situations that need human attention.
In this work, we study and develop methods for detecting outliers in
continuous-time event sequences, including unexpected absence and unexpected
occurrences of events. Since the patterns that event sequences tend to follow
may change in different contexts, we develop outlier detection methods based on
point processes that can take context information into account. Our methods are
based on Bayesian decision theory and hypothesis testing with theoretical
guarantees. To test the performance of the methods, we conduct experiments on
both synthetic data and real-world clinical data and show the effectiveness of
the proposed methods.
