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Class: Aws::CloudWatch::Types::PutAnomalyDetectorInput

Inherits:
Struct
  • Object
show all
Defined in:
(unknown)

Overview

Note:

When passing PutAnomalyDetectorInput as input to an Aws::Client method, you can use a vanilla Hash:

{
  namespace: "Namespace", # required
  metric_name: "MetricName", # required
  dimensions: [
    {
      name: "DimensionName", # required
      value: "DimensionValue", # required
    },
  ],
  stat: "AnomalyDetectorMetricStat", # required
  configuration: {
    excluded_time_ranges: [
      {
        start_time: Time.now, # required
        end_time: Time.now, # required
      },
    ],
    metric_timezone: "AnomalyDetectorMetricTimezone",
  },
}

See Also:

Instance Attribute Summary collapse

Instance Attribute Details

#configuration ⇒ Types::AnomalyDetectorConfiguration

The configuration specifies details about how the anomaly detection model is to be trained, including time ranges to exclude when training and updating the model. You can specify as many as 10 time ranges.

The configuration can also include the time zone to use for the metric.

Returns:

  • (Types::AnomalyDetectorConfiguration) —

    The configuration specifies details about how the anomaly detection model is to be trained, including time ranges to exclude when training and updating the model.

#dimensions ⇒ Array<Types::Dimension>

The metric dimensions to create the anomaly detection model for.

Returns:

  • (Array<Types::Dimension>) —

    The metric dimensions to create the anomaly detection model for.

#metric_name ⇒ String

The name of the metric to create the anomaly detection model for.

Returns:

  • (String) —

    The name of the metric to create the anomaly detection model for.

#namespace ⇒ String

The namespace of the metric to create the anomaly detection model for.

Returns:

  • (String) —

    The namespace of the metric to create the anomaly detection model for.

#stat ⇒ String

The statistic to use for the metric and the anomaly detection model.

Returns:

  • (String) —

    The statistic to use for the metric and the anomaly detection model.