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: Aspects of the present disclosure provide for future user device preference prediction based on telecom data. In one aspect, a computer-implemented method includes collecting the telecom data from at least one node of a wireless communication network, where the telecom data includes records for a plurality of occurrences of user interaction with the wireless communication network via a respective current user device. The telecom data is then applied to a predictive model to obtain a prediction of future user device preferences. The prediction of the future user device preferences may include an indication that a user will switch from the respective current user device to another user device for future use with the wireless communication network. The method further includes performing an action with respect to the wireless communication network in response to the prediction of future user device preferences.

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: 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: Aspects of the present disclosure provide for future user device preference prediction based on telecom data. In one aspect, a computer-implemented method includes collecting the telecom data from at least one node of a wireless communication network, where the telecom data includes records for a plurality of occurrences of user interaction with the wireless communication network via a respective current user device. The telecom data is then applied to a predictive model to obtain a prediction of future user device preferences. The prediction of the future user device preferences may include an indication that a user will switch from the respective current user device to another user device for future use with the wireless communication network. The method further includes performing an action with respect to the wireless communication network in response to the prediction of future user device preferences.