A computer-implemented method comprising: receiving a request to detect anomalies, using an anomaly detection service , in time series data using one or more of a plurality of different detectors ; configuring the anomaly detection service by: receiving the time series data, analyzing the time series data and determining attributes of the time series data, wherein the attributes of the time series data include one or more of: a metric resource type; a metric type; metric features; metric limit thresholds; and metric domain knowledge, generating a configuration for use by an anomaly generator of the anomaly detection service by identifying, based on at least the time series data and the attributes, at least one particular detector of the one or more of the plurality of different detectors to use for detecting anomalies in the time series data for a particular problem, and configuring the anomaly detection service using the generated configuration; evaluating the time series data for an anomaly using the configured anomaly detection service by: ingesting the time series data, observing potentially anomalous behavior using the identified at least one particular detector of the one or more of the plurality of different detectors , and generating an anomaly indication; and providing the anomaly indication to a user. receiving a request to detect anomalies, using an anomaly detection service , in time series data using one or more of a plurality of different detectors ; configuring the anomaly detection service by: receiving the time series data, analyzing the time series data and determining attributes of the time series data, wherein the attributes of the time series data include one or more of: a metric resource type; a metric type; metric features; metric limit thresholds; and metric domain knowledge, generating a configuration for use by an anomaly generator of the anomaly detection service by identifying, based on at least the time series data and the attributes, at least one particular detector of the one or more of the plurality of different detectors to use for detecting anomalies in the time series data for a particular problem, and configuring the anomaly detection service using the generated configuration; receiving the time series data, analyzing the time series data and determining attributes of the time series data, wherein the attributes of the time series data include one or more of: a metric resource type; a metric type; metric features; metric limit thresholds; and metric domain knowledge, generating a configuration for use by an anomaly generator of the anomaly detection service by identifying, based on at least the time series data and the attributes, at least one particular detector of the one or more of the plurality of different detectors to use for detecting anomalies in the time series data for a particular problem, and configuring the anomaly detection service using the generated configuration; evaluating the time series data for an anomaly using the configured anomaly detection service by: ingesting the time series data, observing potentially anomalous behavior using the identified at least one particular detector of the one or more of the plurality of different detectors , and generating an anomaly indication; and ingesting the time series data, observing potentially anomalous behavior using the identified at least one particular detector of the one or more of the plurality of different detectors , and generating an anomaly indication; and providing the anomaly indication to a user. <EOS>