Abstract: A method for detecting an anomaly is provided. The method receives and organizes data as a function of a plurality of time periods and a number of updates for each time period of the plurality of time periods. The organized data approximates first and second unimodalities. The first and second unimodalities have respective first and second peaks. The method identifies a dip across the plurality of time periods and determines a density of the dip. The density is compared with a predetermined threshold in order to determine if the dip is an alternative modality. A unimodality analysis is then performed on the first and second unimodalities in response to the dip being an alternative modality. The method also determines whether the second unimodality is an anomaly based on the unimodality analysis and discards the updates associated with the second unimodality when the second unimodality is an anomaly.

Abstract: A method for detecting an anomaly is provided. The method receives and organizes data as a function of a plurality of time periods and a number of updates for each time period of the plurality of time periods. The organized data approximates first and second unimodalities. The first and second unimodalities have respective first and second peaks. The method identifies a dip across the plurality of time periods and determines a density of the dip. The density is compared with a predetermined threshold in order to determine if the dip is an alternative modality. A unimodality analysis is then performed on the first and second unimodalities in response to the dip being an alternative modality. The method also determines whether the second unimodality is an anomaly based on the unimodality analysis and discards the updates associated with the second unimodality when the second unimodality is an anomaly.