Chakrabourty, PousalyPousalyChakrabourtyCorici, MariusMariusCoriciMagedanz, ThomasThomasMagedanz2022-03-1427.7.20212021https://publica.fraunhofer.de/handle/publica/41156710.1109/ICCWorkshops50388.2021.94735305G network is very flexible because of the two concepts Network Functions Virtualization (NFV) and the Software Defined Networks (SDN). There are various use cases for 5G technology and for different cases different configuration of the network will be needed. 5G Technology will bring intelligence within the network. The ability to support massive connectivity across diverse devices will result in enormous data volume within the 5G network. Continuous monitoring and traffic log analysis in such a complex architecture will not be sufficient to ensure availability and reliability within the network. The integration of data analytics within the 5G network can leverage the potential of automation. By introducing automation in the monitoring process better Quality of Services (QoS) can be achieved and analysing the network traffic load for better bandwidth utilization within the network. This article proposes a solution to integrate time series based analytics with 5G core and predicting any threats within the system which can lead to system failure. To validate the proposal Fraunhofer FOKUS Open5GCore toolkit is used.enmachine learningtime series forecasting3GPP 5G CoreOpen5GCore toolkitfailure prediction004conference paper