Abstract: Time-series data is received. Using an identifier of the time-series data, contextual reference anomaly detection parameters are identified from a repository. A data trend of the time-series data is classified. Based on the classified data trend, a type of model to be generated for the time-series data is selected and a model having generated anomaly detection parameters is generated. A history of anomaly detection parameters determined for the time-series data is identified, and the generated anomaly detection parameters are adjusted based on the contextual reference anomaly detection parameters and the history of anomaly detection parameters.

Abstract: Time-series data is received. Using an identifier of the time-series data, contextual reference anomaly detection parameters are identified from a repository. A data trend of the time-series data is classified. Based on the classified data trend, a type of model to be generated for the time-series data is selected and a model having generated anomaly detection parameters is generated. A history of anomaly detection parameters determined for the time-series data is identified, and the generated anomaly detection parameters are adjusted based on the contextual reference anomaly detection parameters and the history of anomaly detection parameters.

Abstract: In the present application, improved techniques for anomaly detection are disclosed. A plurality of metric data streams is obtained. A first subset of the plurality of metric data streams is identified based on determining that each of the first subset of the plurality of metric data streams satisfies a monitoring criticality criterion. A second subset of the plurality of metric data streams is identified from the first subset of the plurality of metric data streams based on determining that each of the second subset of metric data streams satisfies a metric independence criterion. Anomaly detection is performed with respect to the second subset of the plurality of metric data streams.

Abstract: Time-series data is received. Using an identifier of the time-series data, contextual reference anomaly detection parameters are identified from a repository. A data trend of the time-series data is classified. Based on the classified data trend, a type of model to be generated for the time-series data is selected and a model having generated anomaly detection parameters is generated. A history of anomaly detection parameters determined for the time-series data is identified, and the generated anomaly detection parameters are adjusted based on the contextual reference anomaly detection parameters and the history of anomaly detection parameters.