class Amazonite::CloudWatch::PutAnomalyDetectorInput

Included Modules

Defined in:

cloudwatch/put_anomaly_detector_input.cr

Constructors

Instance Method Summary

Constructor Detail

def self.new(pull : JSON::PullParser) #

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def self.new(namespace : String | Nil = nil, metric_name : String | Nil = nil, dimensions : Array(Dimension) | Nil = nil, stat : String | Nil = nil, configuration : AnomalyDetectorConfiguration | Nil = nil, metric_characteristics : MetricCharacteristics | Nil = nil, single_metric_anomaly_detector : SingleMetricAnomalyDetector | Nil = nil, metric_math_anomaly_detector : MetricMathAnomalyDetector | Nil = nil) #

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Instance Method Detail

def ==(other : self) #
Description copied from class Reference

Returns true if this reference is the same as other. Invokes same?.


def configuration : AnomalyDetectorConfiguration | Nil #

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.


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def configuration=(configuration : AnomalyDetectorConfiguration | Nil) #

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.


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def dimensions : Array(Dimension) | Nil #

The metric dimensions to create the anomaly detection model for.


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def dimensions=(dimensions : Array(Dimension) | Nil) #

The metric dimensions to create the anomaly detection model for.


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def hash(hasher) #
Description copied from class Reference

See Object#hash(hasher)


def metric_characteristics : MetricCharacteristics | Nil #

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 PeriodicSpikes parameter.


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def metric_characteristics=(metric_characteristics : MetricCharacteristics | Nil) #

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 PeriodicSpikes parameter.


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def metric_math_anomaly_detector : MetricMathAnomalyDetector | Nil #

The metric math anomaly detector to be created.

When using MetricMathAnomalyDetector, you cannot include the following parameters in the same operation:

Instead, specify the metric math anomaly detector attributes as part of the property MetricMathAnomalyDetector.


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def metric_math_anomaly_detector=(metric_math_anomaly_detector : MetricMathAnomalyDetector | Nil) #

The metric math anomaly detector to be created.

When using MetricMathAnomalyDetector, you cannot include the following parameters in the same operation:

Instead, specify the metric math anomaly detector attributes as part of the property MetricMathAnomalyDetector.


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def metric_name : String | Nil #

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


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def metric_name=(metric_name : String | Nil) #

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


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def namespace : String | Nil #

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


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def namespace=(namespace : String | Nil) #

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


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def single_metric_anomaly_detector : SingleMetricAnomalyDetector | Nil #

A single metric anomaly detector to be created.

When using SingleMetricAnomalyDetector, you cannot include the following parameters in the same operation:

Instead, specify the single metric anomaly detector attributes as part of the property SingleMetricAnomalyDetector.


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def single_metric_anomaly_detector=(single_metric_anomaly_detector : SingleMetricAnomalyDetector | Nil) #

A single metric anomaly detector to be created.

When using SingleMetricAnomalyDetector, you cannot include the following parameters in the same operation:

Instead, specify the single metric anomaly detector attributes as part of the property SingleMetricAnomalyDetector.


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def stat : String | Nil #

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


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def stat=(stat : String | Nil) #

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


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def validate! : Nil #

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