  In this work we consider the problem of anomaly detection in heterogeneous,
multivariate, variable-length time series datasets. Our focus is on the
aviation safety domain, where data objects are flights and time series are
sensor readings and pilot switches. In this context the goal is to detect
anomalous flight segments, due to mechanical, environmental, or human factors
in order to identifying operationally significant events and provide insights
into the flight operations and highlight otherwise unavailable potential safety
risks and precursors to accidents. For this purpose, we propose a framework
which represents each flight using a semi-Markov switching vector
autoregressive (SMS-VAR) model. Detection of anomalies is then based on
measuring dissimilarities between the model's prediction and data observation.
The framework is scalable, due to the inherent parallel nature of most
computations, and can be used to perform online anomaly detection. Extensive
experimental results on simulated and real datasets illustrate that the
framework can detect various types of anomalies along with the key parameters
involved.
