Detecting anomalies in datasets, where each data object is a multivariate time series, possibly of different length for each data object, is emerging as a key problem in certain domains. The problem is considered in the context of aviation safety, where data objects are flights of various durations and the multivariate time series corresponds to sensor readings. The goal is then to detect anomalous flight segments, due to mechanical, environmental, or human factors. In this paper, a general framework is presented for anomaly detection in such settings by representing each multivariate time series using a vector autoregressive exogenous model, constructing a distance matrix among the objects based on their respective vector autoregressive exogenous 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.
Bibliographical noteFunding Information:
The research was supported by NASA Cooperative Agreement NNX12AQ39A and National Science Foundation grants IIS-1447566, IIS-1422557, CCF-1451986, CNS-1314560, IIS-0953274, and IIS-1029711.