Amazon Technologies, Inc.

Ketan Vijayvargiya

Mohammed Talal Yassar Azam

Jill Blue Lin

Ahmed Gamal Hamed

Laurent Callot

Sadanand Murthy Sachidananda

Mohammed Saad Ather

Devesh Ratho

David Kernert

Mohammad Adnan

Lenon Alexander Minorics

Syed Furqhan Ulla

Patrick Bloebaum

Fnu Syed Furqhan Ulla

Ankur Mehrotra

Aditya Bahuguna

Ankita Verma

Ali Caner Turkmen

Gaurav Rajendra Kataria

Shashank Srivastava

Alfred Bruno Herbst

Dominik Janzing

Peter Goetz

Syed Ahsan Ishtiaque

Shekhar Agrawal

Techniques for detecting regressions with respect to the accuracy of an anomaly detection compute service in detecting anomalies in users' time series data. The techniques include providing an instrumented time series instrumented with a set of one or more anomalies to the anomaly detection service. The anomaly detection service detects a set of one or more anomalies in the instrumented time series. The precision and recall of the detected anomalies with respect to the instrumented anomalies is computed. From the computed precision and recall, an anomaly detection accuracy is computed as an F-score or F-measure. It is then determined whether a regression in anomaly detection accuracy has occurred by comparing the computed accuracy score to a threshold. If a regression has occurred, an alert can be generated or a recent change to the anomaly detection service can be rolled back.

Accuracy regression detection for time series anomaly detection compute services

Techniques for anomaly and causality detection are described. An example includes receiving time series data; performing anomaly detection on the received time series data to detect at least one anomaly using an anomaly detection model; detecting a causal relationship between measures, wherein a set of measures are related when a first of the set of measures has a causal influence on a second of the set of measures, wherein a single time series is a metric and a measure is a numerical or categorical quantity a metric describes; and outputting a result of the anomaly and causality relationship detections.

Anomaly and causality detection

Systems and methods are described for to detecting anomalies in various forms of data, including non-time series data. In one example, a data ingestion interval for customer data may be determined, where the data ingestion interval specifies a frequency at which data is analyzed to detect analogies in portions of the data corresponding to time windows. A portion of the customer data may then be obtained and aggregated from a data source according to the data ingestion interval. The portion of data may be converted into time series data by appending a time stamp corresponding to the time window of the portion of the data. The anomaly detection service may then process the time series data, using a time series data anomaly model, to detect one or more anomalies in the time series data.

Converting non time series data to time series data

Execution status of managed time series processing tasks may be tracked. Status of a time series processing task that operations on different portions of a time series may be respectively captured. A request for the status of one of the portions of the time series with respect to the time series processing task may be received. The status may be identified and returned. For failed tasks, a failure reason may be generated by the time series processing system and included in a response with a failure status.

Tracking status of managed time series processing tasks

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Redmond, WA, United States of America

Ketan Vijayvargiya