A method for predicting fraudulent call activity is provided. The method includes receiving one or more datasets indicating call activity corresponding to a phone number, and analyzing the one or more datasets to identify unusual call activity. The method further includes generating a fraud prediction, based at least in part on the identified unusual call activity, that the phone number will be used for fraud.

A method for predicting fraudulent call activity is provided. The method includes receiving one or more datasets indicating call activity corresponding to a phone number, and analyzing the one or more datasets to identify unusual call activity. The method further includes generating a fraud prediction, based at least in part on the identified unusual call activity, that the phone number will be used for fraud.

The present disclosure relates generally to network diagnostics, and more specifically to techniques for determining the health of customer premises equipment (CPE) devices. In certain examples, a scoring system can determine a health score for a CPE device, the health score indicating the health of the CPE device. The health score is determined by receiving, analyzing, and integrating information from different sources, including sources in addition to CPE diagnostic data for the CPE device at the current time. Such sources can include CPE diagnostic data for the CPE device at other times, CPE diagnostic data for other CPE devices related to the CPE device (e.g., in the same household or surrounding area as the CPE device), technical specifications of the CPE device (e.g., make, model and firmware of the CPE device), or per-equalization parameters obtained by the CPE device.

Systems and methods forecast inbound telecommunications, and more particularly, analyze real-time and historical call center data, and apply a forecasting model to the data in order to predict inbound call volume. These systems and methods employ tools that manipulate call center data and generate visual representations of metrics pertaining to forecasting call center data via a dashboard.

Systems and methods forecast inbound telecommunications, and more particularly, analyze real-time and historical call center data, and apply a forecasting model to the data in order to predict inbound call volume. These systems and methods employ tools that manipulate call center data and generate visual representations of metrics pertaining to forecasting call center data via a dashboard.

A method for identifying a fraudulent phone number is provided. The method includes receiving a user report dataset indicating fraudulent activity corresponding to a phone number, and responsive to receiving the user report dataset, identifying a record in a database corresponding to the phone number. The method further includes tagging the record to identify the phone number as being associated with fraudulent activity.

A method for identifying a fraudulent phone number is provided. The method includes receiving a user report dataset indicating fraudulent activity corresponding to a phone number, and responsive to receiving the user report dataset, identifying a record in a database corresponding to the phone number. The method further includes tagging the record to identify the phone number as being associated with fraudulent activity.

A fault in a telecommunication line can be identified and located using line test data for that telecommunication line. A series of line tests can be performed on a selected telecommunication line experiencing problems. The results from the series of line tests can be analyzed to identify differences in the results between the line tests. The identified differences can then be used to determine if a fluctuation is present in the telecommunication line. If a fluctuation is determined to be present in the telecommunication line, the identified differences can also be used to determine a location of the fluctuation relative to reference location such as the distribution point where the telecommunication line originates.

A fault in a telecommunication line can be identified and located using line test data for that telecommunication line. A series of line tests can be performed on a selected telecommunication line experiencing problems. The results from the series of line tests can be analyzed to identify differences in the results between the line tests. The identified differences can then be used to determine if a fluctuation is present in the telecommunication line. If a fluctuation is determined to be present in the telecommunication line, the identified differences can also be used to determine a location of the fluctuation relative to reference location such as the distribution point where the telecommunication line originates.

A method of modeling a numerical relationship between a user quantity indicator and a resource usage indicator includes performing first regression on a first dataset that describes a numerical relationship between a feature of the user quantity indicator and a feature of a service usage indicator, to obtain a first prediction model. The method further includes performing second regression on a second dataset that describes a numerical relationship between the feature of the service usage indicator and a feature of the resource usage indicator, to obtain a second prediction model.