Abstract: Systems and methods of the present disclosure include at least one processor that receives a data set of a data stream from a data source, where the data set includes a time-varying data points. The processor determines event observations associated with data points of the time-varying data points based on a detection model to identify types of the event observations, including: i) anomalies, ii) change-points, iii) patterns, or iv) outliers. The processor generates anomaly records in an event data store based on the event observations and automatically generates event records for at least one of the anomaly records based on variables of at least one dimension of the time-varying data points, where the event record links one or more event observations. The processor automatically applies changes in the event record to each event observation of the one or more event observations based on the linking by the event record.

Abstract: Systems and methods of the present disclosure include at least one processor that receives a data set of a data stream from a data source, where the data set includes a time-varying data points. The processor determines event observations associated with data points of the time-varying data points based on a detection model to identify types of the event observations, including: i) anomalies, ii) change-points, iii) patterns, or iv) outliers. The processor generates anomaly records in an event data store based on the event observations and automatically generates event records for at least one of the anomaly records based on variables of at least one dimension of the time-varying data points, where the event record links one or more event observations. The processor automatically applies changes in the event record to each event observation of the one or more event observations based on the linking by the event record.

Abstract: Systems and methods of the present disclosure include at least one processor that receives a data set of a data stream from a data source, where the data set includes a time-varying data points. The processor determines event observations associated with data points of the time-varying data points based on a detection model to identify types of the event observations, including: i) anomalies, ii) change-points, iii) patterns, or iv) outliers. The processor generates anomaly records in an event data store based on the event observations and automatically generates event records for at least one of the anomaly records based on variables of at least one dimension of the time-varying data points, where the event record links one or more event observations. The processor automatically applies changes in the event record to each event observation of the one or more event observations based fon the linking by the event record.