Put Anomaly Detector
cloudwatch_put_anomaly_detector | R Documentation |
Creates an anomaly detection model for a CloudWatch metric¶
Description¶
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.
If you have enabled unified cross-account observability, and this
account is a monitoring account, the metric can be in the same account
or a source account. You can specify the account ID in the object you
specify in the SingleMetricAnomalyDetector
parameter.
For more information, see CloudWatch Anomaly Detection.
Usage¶
cloudwatch_put_anomaly_detector(Namespace, MetricName, Dimensions, Stat,
Configuration, MetricCharacteristics, SingleMetricAnomalyDetector,
MetricMathAnomalyDetector)
Arguments¶
Namespace |
The namespace of the metric to create the anomaly detection model for. |
MetricName |
The name of the metric to create the anomaly detection model for. |
Dimensions |
The metric dimensions to create the anomaly detection model for. |
Stat |
The statistic to use for the metric and the anomaly detection model. |
Configuration |
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. |
MetricCharacteristics |
Use this object to include parameters to provide information
about your metric to CloudWatch to help it build more accurate anomaly
detection models. Currently, it includes the |
SingleMetricAnomalyDetector |
A single metric anomaly detector to be created. When using
Instead, specify the single metric anomaly detector attributes as
part of the property |
MetricMathAnomalyDetector |
The metric math anomaly detector to be created. When using
Instead, specify the metric math anomaly detector attributes as part
of the property |
Value¶
An empty list.
Request syntax¶
svc$put_anomaly_detector(
Namespace = "string",
MetricName = "string",
Dimensions = list(
list(
Name = "string",
Value = "string"
)
),
Stat = "string",
Configuration = list(
ExcludedTimeRanges = list(
list(
StartTime = as.POSIXct(
"2015-01-01"
),
EndTime = as.POSIXct(
"2015-01-01"
)
)
),
MetricTimezone = "string"
),
MetricCharacteristics = list(
PeriodicSpikes = TRUE|FALSE
),
SingleMetricAnomalyDetector = list(
AccountId = "string",
Namespace = "string",
MetricName = "string",
Dimensions = list(
list(
Name = "string",
Value = "string"
)
),
Stat = "string"
),
MetricMathAnomalyDetector = list(
MetricDataQueries = list(
list(
Id = "string",
MetricStat = list(
Metric = list(
Namespace = "string",
MetricName = "string",
Dimensions = list(
list(
Name = "string",
Value = "string"
)
)
),
Period = 123,
Stat = "string",
Unit = "Seconds"|"Microseconds"|"Milliseconds"|"Bytes"|"Kilobytes"|"Megabytes"|"Gigabytes"|"Terabytes"|"Bits"|"Kilobits"|"Megabits"|"Gigabits"|"Terabits"|"Percent"|"Count"|"Bytes/Second"|"Kilobytes/Second"|"Megabytes/Second"|"Gigabytes/Second"|"Terabytes/Second"|"Bits/Second"|"Kilobits/Second"|"Megabits/Second"|"Gigabits/Second"|"Terabits/Second"|"Count/Second"|"None"
),
Expression = "string",
Label = "string",
ReturnData = TRUE|FALSE,
Period = 123,
AccountId = "string"
)
)
)
)