Abstract: A method may include determining first network data corresponding to a first time period, including alarm data and incident data collected by a set of network devices of a core network during the first time period. The method may include creating, based on the first network data, an incident prediction model. The method may include receiving second network data, associated with the core network, corresponding to a second time period. The second network data may include alarm data and incident data collected by a network device, of the set of network devices, during the second time period. The method may include generating, based on the second network data and the incident prediction model, an incident prediction that includes a prediction whether the network device will experience an incident during a third time period.

Abstract: In an example, a performance of an access point in a wireless network is optimized based on a statistical ranking of independent variables. A device analyzer may calculate a dependent variable for the performance of the access point and independent variables that impact the dependent variable from a set of independent variables based on real-time access point data received from a plurality of access points. A predictive modeler may generate a model to forecast the performance of the access point and to determine an impact ranking for the independent variables from the dependent and independent variables. The impact ranking may sequence the independent variables according to their impact on the dependent variable. Accordingly, a configuration circuit may adjust a controllable parameter of the access points according to the impact ranking.

Abstract: A method may include determining first network data corresponding to a first time period, including alarm data and incident data collected by a set of network devices of a core network during the first time period. The method may include creating, based on the first network data, an incident prediction model. The method may include receiving second network data, associated with the core network, corresponding to a second time period. The second network data may include alarm data and incident data collected by a network device, of the set of network devices, during the second time period. The method may include generating, based on the second network data and the incident prediction model, an incident prediction that includes a prediction whether the network device will experience an incident during a third time period.

Abstract: A device may determine a performance metric associated with a network service management process. The device may determine a key question that may identify a business issue associated with improving the performance metric. The device may perform a root cause analysis that identifies a solution to the key question. The solution may identify a manner in which the network service management process is to be modified in order to improve the performance metric. The device may forecast, based on the solution, a network service demand that may identify a quantity of expected future network service actions expected based on implementing the solution. The device may perform, based on the forecasted network service demand, capacity planning that may identify network service resources required to satisfy the forecasted network service demand. The device may schedule the network service resources such that the solution is implemented within the network service management process.

Abstract: In an example, a performance of an access point in a wireless network is optimized based on a statistical ranking of independent variables. A device analyzer may calculate a dependent variable for the performance of the access point and independent variables that impact the dependent variable from a set of independent variables based on real-time access point data received from a plurality of access points. A predictive modeler may generate a model to forecast the performance of the access point and to determine an impact ranking for the independent variables from the dependent and independent variables. The impact ranking may sequence the independent variables according to their impact on the dependent variable. Accordingly, a configuration circuit may adjust a controllable parameter of the access points according to the impact ranking.