COMPUTER IMPLEMENTED METHODS, SYSTEMS AND PROGRAM INSTRUCTIONS FOR DETECTING ANOMALIES IN A CORE NETWORK OF A TELECOMMUNICATIONS NETWORK
개요
발명자
Ahmed HANY; Yosr MOHAMED
IPC 분류
CPC 분류
The computer implemented methods, systems, and program instructions detect anomalies in a core network of a telecommunications network. The method comprises: receiving data representative of streams of time series data of a plurality of Key Performance Indicators (KPIs) of the performance of nodes of the core network; comparing the received time series data for each of the KPIs to predicted time series values for each KPI generated by one or more time series analysis algorithms trained with historical data for each KPI to predict the KPI over time; determining any KPIs having deviations between the received time series data and the predicted time series data during a specific time period, wherein each deviation is an anomaly; grouping the streams of time series data for each KPI determined to be deviated to generate anomaly data; using an artificially intelligent clustering algorithm to generate a plurality of clusters, wherein each cluster comprises a subset of the KPIs determined to be deviated that have been assigned to said cluster by the artificially intelligent clustering algorithm; wherein each of the clusters is identified as having an associated root cause.
원문 (중국어)
The computer implemented methods, systems, and program instructions detect anomalies in a core network of a telecommunications network. The method comprises: receiving data representative of streams of time series data of a plurality of Key Performance Indicators (KPIs) of the performance of nodes of the core network; comparing the received time series data for each of the KPIs to predicted time series values for each KPI generated by one or more time series analysis algorithms trained with historical data for each KPI to predict the KPI over time; determining any KPIs having deviations between the received time series data and the predicted time series data during a specific time period, wherein each deviation is an anomaly; grouping the streams of time series data for each KPI determined to be deviated to generate anomaly data; using an artificially intelligent clustering algorithm to generate a plurality of clusters, wherein each cluster comprises a subset of the KPIs determined to be deviated that have been assigned to said cluster by the artificially intelligent clustering algorithm; wherein each of the clusters is identified as having an associated root cause.