Vector Autoregressive Model-Based Anomaly Detection in Aviation Systems

Detecting anomalies in datasets, where each data object is a multivariate time series (MTS), possibly of different length for each data object, is emerging as a key problem in certain domains. We consider the problem in the context of aviation safety, where data objects are flights of various durations, and the MTS corresponds to sensor readings. The goal then is to detect anomalous flight segments, due to mechanical, environmental, or human factors. In this paper, we present a general framework for anomaly detection in such settings, by representing each MTS using a vector autoregressive exogenous (VARX) model, constructing a distance matrix among the objects based on their respective VARX models, and finally detecting anomalies based on the object dissimilarities. The framework is scalable, due to the inherent parallel nature of most computations, and can be used to perform online anomaly detection. Experimental results on a real flight dataset illustrate that the framework can detect different types of multivariate anomalies along with the key parameters involved.