Abstract: Techniques are described herein for validating the occurrence of an anomaly and/or identifying a source and/or impact of an anomaly initially detected in time series data. A plurality of time series data instances individually corresponding to a respective entity of a plurality of entities may be obtained. An anomalous value may be detected in a first time series data instance of the plurality of time series data instances, the first time series data instance being associated with an entity. One or more correlated entity peers for the entity may be identified based at least in part on a predefined hierarchy and/or segmentation scheme. The occurrence of the anomaly and/or the source and/or impact of the anomaly may be determined based on comparing the anomaly to time series data of the time series data of the correlated entity peers.

Abstract: Techniques are described herein for validating the occurrence of an anomaly and/or identifying a source and/or impact of an anomaly initially detected in time series data. A plurality of time series data instances individually corresponding to a respective entity of a plurality of entities may be obtained. An anomalous value may be detected in a first time series data instance of the plurality of time series data instances, the first time series data instance being associated with an entity. One or more correlated entity peers for the entity may be identified based at least in part on a predefined hierarchy and/or segmentation scheme. The occurrence of the anomaly and/or the source and/or impact of the anomaly may be determined based on comparing the anomaly to time series data of the time series data of the correlated entity peers.