Abstract: A Quality of Service (QoS) improvement system and method for transport network fault detection and QoS improvement so that a telecommunication network service provider can analyze the root cause on chronic performance issues and recommend potential solutions is disclosed. The system runs performance analysis on each AAV (mobile backhaul) or other transport networks and collects performance related metrics data. The system then selects a subset of the data related to certain key performance indicators (KPIs), such as latency, jitter, packet loss ratio, and availability. On this subset of KPI-related data, the system applies clustering techniques to identify clusters with similar performance issues. For each cluster, the system binds the AAV performance KPI data with one or more of the following site features—health, location, vendor, market, service type, etc.—to create a cluster map. The system can then generate inferences on root causes of the performance issues.

Abstract: An anomaly detection and analysis system generates analysis or summary of the anomalies detected from key performance indicators (KPIs). The system receives anomaly data reporting anomalies detected in key performance indicator (KPI) data. The system classifies the reported anomalies into a plurality of anomaly items, wherein anomalies from KPI data that share a set of features are assigned to one anomaly item. The system computes a ranking score for each anomaly item by assigning predefined weights for different anomaly types that are present in the anomaly item. The system sorts a list of anomaly items from the plurality of anomaly items into a sorted list of anomaly items according to the ranking scores computed for the plurality of anomaly items. The system sends the sorted list of anomaly items to a user device for presentation.

Abstract: An anomaly detection and analysis system detects anomalies in time series data from key performance indicators (KPIs). The system decomposes samples of the time series data received during a first time interval into a trend component, a seasonality component, and a randomness component. The system identifies an upper bound and a lower bound based on the trend component, the seasonality component, and a variance of the randomness component. The system reports a sample received after the first time interval as an anomaly when the sample exceeds the upper bound or the lower bound. The system recalculates the trend component, the seasonality component, and the randomness component when more than a threshold percentage of the samples of the time series data received during a second time interval are reported as being anomalous.

Abstract: An anomaly detection and analysis system generates analysis or summary of the anomalies detected from key performance indicators (KPIs). The system receives anomaly data reporting anomalies detected in key performance indicator (KPI) data. The system classifies the reported anomalies into a plurality of anomaly items, wherein anomalies from KPI data that share a set of features are assigned to one anomaly item. The system computes a ranking score for each anomaly item by assigning predefined weights for different anomaly types that are present in the anomaly item. The system sorts a list of anomaly items from the plurality of anomaly items into a sorted list of anomaly items according to the ranking scores computed for the plurality of anomaly items. The system sends the sorted list of anomaly items to a user device for presentation.

Abstract: An anomaly detection and analysis system generates analysis or summary of the anomalies detected from key performance indicators (KPIs). The system receives anomaly data reporting anomalies detected in key performance indicator (KPI) data. The system classifies the reported anomalies into a plurality of anomaly items, wherein anomalies from KPI data that share a set of features are assigned to one anomaly item. The system computes a ranking score for each anomaly item by assigning predefined weights for different anomaly types that are present in the anomaly item. The system sorts a list of anomaly items from the plurality of anomaly items into a sorted list of anomaly items according to the ranking scores computed for the plurality of anomaly items. The system sends the sorted list of anomaly items to a user device for presentation.

Abstract: A Quality of Service (QoS) improvement system and method for transport network fault detection and QoS improvement so that a telecommunication network service provider can analyze the root cause on chronic performance issues and recommend potential solutions is disclosed. The system runs performance analysis on each AAV (mobile backhaul) or other transport networks and collects performance related metrics data. The system then selects a subset of the data related to certain key performance indicators (KPIs), such as latency, jitter, packet loss ratio, and availability. On this subset of KPI-related data, the system applies clustering techniques to identify clusters with similar performance issues. For each cluster, the system binds the AAV performance KPI data with one or more of the following site features—health, location, vendor, market, service type, etc.—to create a cluster map. The system can then generate inferences on root causes of the performance issues.

Abstract: A Quality of Service (QoS) improvement system and method for transport network fault detection and QoS improvement so that a telecommunication network service provider can analyze the root cause on chronic performance issues and recommend potential solutions is disclosed. The system runs performance analysis on each AAV (mobile backhaul) or other transport networks and collects performance related metrics data. The system then selects a subset of the data related to certain key performance indicators (KPIs), such as latency, jitter, packet loss ratio, and availability. On this subset of KPI-related data, the system applies clustering techniques to identify clusters with similar performance issues. For each cluster, the system binds the AAV performance KPI data with one or more of the following site features—health, location, vendor, market, service type, etc.—to create a cluster map. The system can then generate inferences on root causes of the performance issues.

Abstract: A Quality of Service (QoS) improvement system and method for transport network fault detection and QoS improvement so that a telecommunication network service provider can analyze the root cause on chronic performance issues and recommend potential solutions is disclosed. The system runs performance analysis on each AAV (mobile backhaul) or other transport networks and collects performance related metrics data. The system then selects a subset of the data related to certain key performance indicators (KPIs), such as latency, jitter, packet loss ratio, and availability. On this subset of KPI-related data, the system applies clustering techniques to identify clusters with similar performance issues. For each cluster, the system binds the AAV performance KPI data with one or more of the following site features—health, location, vendor, market, service type, etc.—to create a cluster map. The system can then generate inferences on root causes of the performance issues.

Abstract: An anomaly detection and analysis system detects anomalies in time series data from key performance indicators (KPIs). The system decomposes samples of the time series data received during a first time interval into a trend component, a seasonality component, and a randomness component. The system identifies an upper bound and a lower bound based on the trend component, the seasonality component, and a variance of the randomness component. The system reports a sample received after the first time interval as an anomaly when the sample exceeds the upper bound or the lower bound. The system recalculates the trend component, the seasonality component, and the randomness component when more than a threshold percentage of the samples of the time series data received during a second time interval are reported as being anomalous.

Abstract: An anomaly detection and analysis system generates analysis or summary of the anomalies detected from key performance indicators (KPIs). The system receives anomaly data reporting anomalies detected in key performance indicator (KPI) data. The system classifies the reported anomalies into a plurality of anomaly items, wherein anomalies from KPI data that share a set of features are assigned to one anomaly item. The system computes a ranking score for each anomaly item by assigning predefined weights for different anomaly types that are present in the anomaly item. The system sorts a list of anomaly items from the plurality of anomaly items into a sorted list of anomaly items according to the ranking scores computed for the plurality of anomaly items. The system sends the sorted list of anomaly items to a user device for presentation.

Abstract: A Quality of Service (QoS) improvement system and method for transport network fault detection and QoS improvement so that a telecommunication network service provider can analyze the root cause on chronic performance issues and recommend potential solutions is disclosed. The system runs performance analysis on each AAV (mobile backhaul) or other transport networks and collects performance related metrics data. The system then selects a subset of the data related to certain key performance indicators (KPIs), such as latency, jitter, packet loss ratio, and availability. On this subset of KPI-related data, the system applies clustering techniques to identify clusters with similar performance issues. For each cluster, the system binds the AAV performance KPI data with one or more of the following site features—health, location, vendor, market, service type, etc.—to create a cluster map. The system can then generate inferences on root causes of the performance issues.