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The massive amount of data available in operational mobile networks offers aninvaluable opportunity for operators to detect and analyze possible anomaliesand predict network performance. In particular, application of advanced machinelearning (ML) techniques on data aggregated from multiple sources can lead toimportant insights, not only for the detection of anomalous behavior but alsofor performance forecasting, thereby complementing classic network operationand maintenance solutions with intelligent monitoring tools. In this paper, wepropose a novel framework that aggregates diverse data sets (e.g.configuration, performance, inventory, locations, user speeds) from anoperational LTE network and applies ML algorithms to diagnose network issuesand analyze their impact on key performance indicators. To this end, patternidentification and time-series forecasting algorithms are used on the ingesteddata. Results show that proposed framework can indeed be leveraged to automatethe identification of anomalous behaviors associated with the spatial-temporalcharacteristics, and predict customer impact in an accurate manner.

Big Data-driven Automated Anomaly Detection and Performance Forecasting in Mobile Networks