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.

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 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 disclosed system identifies international calling performance issues of a wireless telecommunication network. The system receives network traffic data for international calls including information about call attempts to a country. The system categorizes the country into a major category and a minor category based on the call attempts information. For a subset of countries, and for each key performance indicator in a subset of selected key performance indicators, the system monitors performance using an anomaly detection model to identify an anomaly in network performance, determines an actual value of the key performance indicator for the detected anomaly, and computes a variation value of the determined actual value based on a predicted range of values. The system ranks countries using the computed variation values, to indicate problematic parts of the wireless telecommunication network.

The disclosed system identifies international calling performance issues of a wireless telecommunication network. The system receives network traffic data for international calls including information about call attempts to a country. The system categorizes the country into a major category and a minor category based on the call attempts information. For a subset of countries, and for each key performance indicator in a subset of selected key performance indicators, the system monitors performance using an anomaly detection model to identify an anomaly in network performance, determines an actual value of the key performance indicator for the detected anomaly, and computes a variation value of the determined actual value based on a predicted range of values. The system ranks countries using the computed variation values, to indicate problematic parts of the wireless telecommunication network.

Asset recommendation for a particular input dataset is provided. Candidate data analysis assets having a corresponding relatedness score associated with the particular input dataset greater than a defined relatedness score threshold value are selected. Those candidate data analysis assets having a corresponding relatedness score greater than the defined relatedness score threshold value are ranked by score. Those candidate data analysis assets having a corresponding relatedness score greater than the defined relatedness score threshold value are listed by rank from highest to lowest. A justification for each candidate data analysis asset is inserted in the ranked list of candidate data analysis assets. The ranked list of candidate data analysis assets along with each respective justification is outputted on a display device.

A sequence of call volume measurements is accessed, where each of the call volume measurements is associated with respective metadata. The respective metadata may provide information regarding a time period during which a call volume measurement was made. A window of the sequence of call volume measurements with the respective metadata is input to a machine learning model to obtain a prediction of a call volume. The machine learning model includes embedding functions that are applied to the respective metadata for the call volume measurements in the window.

A method including: receiving a user report dataset indicating fraudulent activity corresponding to a phone number; responsive to receiving the user report dataset, identifying a record in a database corresponding to the phone number; and tagging the record to identify the phone number as being associated with fraudulent activity.