Multi-contextual anomaly detection

A non-transitory computer-readable storage medium storing instructions that, when executed by a processor, programs the processor to:

• access a data value of a metric for which an anomaly prediction is to be made;
• provide the data value to a plurality of machine learning models;
• generate, via the plurality of models, a plurality of anomaly scores comprising at least a first anomaly score generated by the first model based on a first behavior of historical data values of the metric and at least a second anomaly score generated by the second model based on the second behavior of the historical data values of the metric, wherein each anomaly score from among the plurality of anomaly scores represents a prediction that the data value is anomalous based on a respective machine learning model that models a corresponding behavior of the historical data values of the metric;
• and
• generate an aggregate anomaly score based on the plurality of anomaly scores, the aggregate anomaly score representing an aggregate prediction that the data value is anomalous.

A method, comprising:

• accessing, by a computer system, a data value of a metric for which an anomaly prediction is to be made, wherein the metric is identified by a metric identifier that is stored in association with a label identifier that identifies a label, the label indicating a source of the data value;
• providing, by the computer system, the data value to a plurality of machine learning models trained to detect one or more anomalies based on behaviors of historical data values of the metric;
• generating, by the computer system, based on execution of the plurality of models, a plurality of anomaly scores comprising at least a first anomaly score generated by a first model trained to detect the one or more anomalies based on a first behavior of the historical data values of the metric and at least a second anomaly score generated by a second model trained to detect the one or more anomalies based on a second behavior of the historical data values of the metric, wherein each anomaly score from among the plurality of anomaly scores represents a prediction that the data value is anomalous based on a respective machine learning model that models a corresponding behavior of the historical data values of the metric;
• and
• generating, by the computer system, an aggregate anomaly score based on the plurality of anomaly scores, the aggregate anomaly score representing an aggregate prediction that the data value is anomalous;
• and
• identifying, by the computer system, a mitigative action to take based on the aggregate anomaly score.