[ aws . cloudwatch ]
Creates an anomaly detection model for a CloudWatch metric. You can use the model to display a band of expected normal values when the metric is graphed.
For more information, see CloudWatch Anomaly Detection .
See also: AWS API Documentation
See ‘aws help’ for descriptions of global parameters.
put-anomaly-detector
--namespace <value>
--metric-name <value>
[--dimensions <value>]
--stat <value>
[--configuration <value>]
[--cli-input-json | --cli-input-yaml]
[--generate-cli-skeleton <value>]
[--cli-auto-prompt <value>]
--namespace
(string)
The namespace of the metric to create the anomaly detection model for.
--metric-name
(string)
The name of the metric to create the anomaly detection model for.
--dimensions
(list)
The metric dimensions to create the anomaly detection model for.
(structure)
Expands the identity of a metric.
Name -> (string)
The name of the dimension.
Value -> (string)
The value representing the dimension measurement.
Shorthand Syntax:
Name=string,Value=string ...
JSON Syntax:
[
{
"Name": "string",
"Value": "string"
}
...
]
--stat
(string)
The statistic to use for the metric and the anomaly detection model.
--configuration
(structure)
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.
You can in
ExcludedTimeRanges -> (list)
An array of time ranges to exclude from use when the anomaly detection model is trained. Use this to make sure that events that could cause unusual values for the metric, such as deployments, aren’t used when CloudWatch creates the model.
(structure)
Specifies one range of days or times to exclude from use for training an anomaly detection model.
StartTime -> (timestamp)
The start time of the range to exclude. The format is
yyyy-MM-dd'T'HH:mm:ss
. For example,2019-07-01T23:59:59
.EndTime -> (timestamp)
The end time of the range to exclude. The format is
yyyy-MM-dd'T'HH:mm:ss
. For example,2019-07-01T23:59:59
.MetricTimezone -> (string)
The time zone to use for the metric. This is useful to enable the model to automatically account for daylight savings time changes if the metric is sensitive to such time changes.
To specify a time zone, use the name of the time zone as specified in the standard tz database. For more information, see tz database .
Shorthand Syntax:
ExcludedTimeRanges=[{StartTime=timestamp,EndTime=timestamp},{StartTime=timestamp,EndTime=timestamp}],MetricTimezone=string
JSON Syntax:
{
"ExcludedTimeRanges": [
{
"StartTime": timestamp,
"EndTime": timestamp
}
...
],
"MetricTimezone": "string"
}
--cli-input-json
| --cli-input-yaml
(string)
Reads arguments from the JSON string provided. The JSON string follows the format provided by --generate-cli-skeleton
. If other arguments are provided on the command line, those values will override the JSON-provided values. It is not possible to pass arbitrary binary values using a JSON-provided value as the string will be taken literally. This may not be specified along with --cli-input-yaml
.
--generate-cli-skeleton
(string)
Prints a JSON skeleton to standard output without sending an API request. If provided with no value or the value input
, prints a sample input JSON that can be used as an argument for --cli-input-json
. Similarly, if provided yaml-input
it will print a sample input YAML that can be used with --cli-input-yaml
. If provided with the value output
, it validates the command inputs and returns a sample output JSON for that command.
--cli-auto-prompt
(boolean)
Automatically prompt for CLI input parameters.
See ‘aws help’ for descriptions of global parameters.
None