  The massive amount of data available in operational mobile networks offers an
invaluable opportunity for operators to detect and analyze possible anomalies
and predict network performance. In particular, application of advanced machine
learning (ML) techniques on data aggregated from multiple sources can lead to
important insights, not only for the detection of anomalous behavior but also
for performance forecasting, thereby complementing classic network operation
and maintenance solutions with intelligent monitoring tools. In this paper, we
propose a novel framework that aggregates diverse data sets (e.g.
configuration, performance, inventory, locations, user speeds) from an
operational LTE network and applies ML algorithms to diagnose network issues
and analyze their impact on key performance indicators. To this end, pattern
identification and time-series forecasting algorithms are used on the ingested
data. Results show that proposed framework can indeed be leveraged to automate
the identification of anomalous behaviors associated with the spatial-temporal
characteristics, and predict customer impact in an accurate manner.
