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AWS::APS::AnomalyDetector RandomCutForestConfiguration
Configuration for the Random Cut Forest algorithm used for anomaly detection in time-series data.
Syntax
To declare this entity in your Amazon CloudFormation template, use the following syntax:
JSON
{ "IgnoreNearExpectedFromAbove" :IgnoreNearExpected, "IgnoreNearExpectedFromBelow" :IgnoreNearExpected, "Query" :String, "SampleSize" :Integer, "ShingleSize" :Integer}
YAML
IgnoreNearExpectedFromAbove:IgnoreNearExpectedIgnoreNearExpectedFromBelow:IgnoreNearExpectedQuery:StringSampleSize:IntegerShingleSize:Integer
Properties
IgnoreNearExpectedFromAbove-
Configuration for ignoring values that are near expected values from above during anomaly detection.
Required: No
Type: IgnoreNearExpected
Update requires: No interruption
IgnoreNearExpectedFromBelow-
Configuration for ignoring values that are near expected values from below during anomaly detection.
Required: No
Type: IgnoreNearExpected
Update requires: No interruption
Query-
The Prometheus query used to retrieve the time-series data for anomaly detection.
Important
Random Cut Forest queries must be wrapped by a supported PromQL aggregation operator. For more information, see Aggregation operators
on the Prometheus docs website. Supported PromQL aggregation operators:
avg,count,group,max,min,quantile,stddev,stdvar, andsum.Required: Yes
Type: String
Minimum:
1Update requires: No interruption
SampleSize-
The number of data points sampled from the input stream for the Random Cut Forest algorithm. The default number is 256 consecutive data points.
Required: No
Type: Integer
Minimum:
256Maximum:
1024Update requires: No interruption
ShingleSize-
The number of consecutive data points used to create a shingle for the Random Cut Forest algorithm. The default number is 8 consecutive data points.
Required: No
Type: Integer
Minimum:
2Maximum:
1024Update requires: No interruption