Multi-variant anomaly detection from application telemetry

A method for anomaly detection using application telemetry, the method comprising:

• receiving telemetry data originating from a plurality of client applications, the telemetry data including data points representing errors associated with one or more operations of the plurality of client applications;
• classifying the telemetry data based a plurality of classes of data;
• converting the plurality of classes of data into one or more metrics based on a plurality of dimensions;
• aggregating the one or more metrics for the classes of data across the plurality of dimensions;
• accessing a predictive scoring model for a prediction error associated with a dimension of interest;
• detecting an anomaly based on an item selected from the group consisting of the prediction error and a static threshold;
• and
• outputting an alert message associated with the anomaly.

A non-transitory computer-readable medium containing instructions that when executed by one or more electronic processors cause the one or more electronic processors to:

• receive telemetry data originating from a plurality of client applications, the telemetry data including data points associated with errors associated with one or more operations;
• classify the telemetry data based a plurality of classes of data;
• convert the plurality of classes of data into one or more metrics based on a plurality of dimensions;
• aggregate the one or more metrics for the classes of data by all the dimensions;
• access a predictive scoring model for a stored metric associated with a dimension of interest;
• determine a prediction error associated with the dimension of interest;
• detect an anomaly based on an item selected from the group consisting of the prediction error and a static threshold;
• and
• transmit an alert message, generate a bug report and store the bug report in a database.