Abstract: Disclosed herein are system, method, and computer program product embodiments for generating a set of correlated applications by a recommendation system to resolve an incident for an application service (AS). The recommendation system can determine an AS-to-AS similarity matrix based on incident data and contextual data, an AS-to-AS affinity matrix based on the incident data and contextual data, and generate a set of correlated applications based on the similarity matrix and the affinity matrix. The set of correlated applications can also be generated based on pairwise application associations, where an application association between a first application and a second application indicates that the first application and the second application can occur together in an incident or to be changed together as indicated by a change record.

Abstract: A server device obtains historical transaction data regarding transactions involving a network service, obtains historical calendar data regarding static date information for a historical time period that corresponds with the historical transaction data, and processes the historical transaction data and historical calendar data to train a machine learning model using a gradient boosting machine learning technique to predict a normal transaction volume for a period of time and confidence bands associated with the normal transaction volume. The server device generates the normal transaction volume for the period of time and confidence bands using the machine learning model, obtains real-time data concerning a transaction volume during the period of time, detects a transaction volume anomaly based on comparing the real-time data and normal transaction volume and confidence bands, and sends an alert, based on the transaction volume anomaly, to cause a remote device to display the alert and perform an action.

Abstract: A server device obtains historical transaction data regarding transactions involving a network service, obtains historical calendar data regarding static date information for a historical time period that corresponds with the historical transaction data, and processes the historical transaction data and historical calendar data to train a machine learning model using a gradient boosting machine learning technique to predict a normal transaction volume for a period of time and confidence bands associated with the normal transaction volume. The server device generates the normal transaction volume for the period of time and confidence bands using the machine learning model, obtains real-time data concerning a transaction volume during the period of time, detects a transaction volume anomaly based on comparing the real-time data and normal transaction volume and confidence bands, and sends an alert, based on the transaction volume anomaly, to cause a remote device to display the alert and perform an action.

Abstract: A server device obtains historical transaction data regarding transactions involving a network service, obtains historical calendar data regarding static date information for a historical time period that corresponds with the historical transaction data, and processes the historical transaction data and historical calendar data to train a machine learning model using a gradient boosting machine learning technique to predict a normal transaction volume for a period of time and confidence bands associated with the normal transaction volume. The server device generates the normal transaction volume for the period of time and confidence bands using the machine learning model, obtains real-time data concerning a transaction volume during the period of time, detects a transaction volume anomaly based on comparing the real-time data and normal transaction volume and confidence bands, and sends an alert, based on the transaction volume anomaly, to cause a remote device to display the alert and perform an action.