  Failure detection in telecommunication networks is a vital task. So far,
several supervised and unsupervised solutions have been provided for
discovering failures in such networks. Among them unsupervised approaches has
attracted more attention since no label data is required. Often, network
devices are not able to provide information about the type of failure. In such
cases the type of failure is not known in advance and the unsupervised setting
is more appropriate for diagnosis. Among unsupervised approaches, Principal
Component Analysis (PCA) is a well-known solution which has been widely used in
the anomaly detection literature and can be applied to matrix data (e.g.
Users-Features). However, one of the important properties of network data is
their temporal sequential nature. So considering the interaction of dimensions
over a third dimension, such as time, may provide us better insights into the
nature of network failures. In this paper we demonstrate the power of three-way
analysis to detect events and anomalies in time-evolving network data.
