AWS AmazonCloudWatch documentation change
Summary
Renamed 'outlier detection' to 'anomaly detection' throughout the documentation, including feature name, terminology, and API references.
Security assessment
This is a terminology update from 'outlier detection' to 'anomaly detection'. There is no evidence of a security vulnerability being addressed. The changes are purely cosmetic/terminological, updating feature names, descriptions, and references without altering functionality or introducing security-related content.
Diff
diff --git a/AmazonCloudWatch/latest/monitoring/CloudWatch_Anomaly_Detection.md b/AmazonCloudWatch/latest/monitoring/CloudWatch_Anomaly_Detection.md index f65589941..aae67227a 100644 --- a//AmazonCloudWatch/latest/monitoring/CloudWatch_Anomaly_Detection.md +++ b//AmazonCloudWatch/latest/monitoring/CloudWatch_Anomaly_Detection.md @@ -5 +5 @@ -How outlier detection worksOutlier detection on metric math +How anomaly detection worksAnomaly detection on metric math @@ -7 +7 @@ How outlier detection worksOutlier detection on metric math -# Using CloudWatch outlier detection +# Using CloudWatch anomaly detection @@ -9 +9 @@ How outlier detection worksOutlier detection on metric math -When you enable _outlier detection_ for a metric, CloudWatch applies statistical and machine learning algorithms. These algorithms continuously analyze metrics of systems and applications, determine normal baselines, and surface anomalies with minimal user intervention. +When you enable _anomaly detection_ for a metric, CloudWatch applies statistical and machine learning algorithms. These algorithms continuously analyze metrics of systems and applications, determine normal baselines, and surface anomalies with minimal user intervention. @@ -11 +11 @@ When you enable _outlier detection_ for a metric, CloudWatch applies statistical -The algorithms generate an outlier detection model. The model generates a range of expected values that represent normal metric behavior. +The algorithms generate an anomaly detection model. The model generates a range of expected values that represent normal metric behavior. @@ -13 +13 @@ The algorithms generate an outlier detection model. The model generates a range -You can enable outlier detection using the AWS Management Console, the AWS CLI, CloudFormation, or the AWS SDK. You can enable outlier detection on metrics vended by AWS and also on custom metrics. In an account set up as a monitoring account for CloudWatch cross-account observability, you can create anomaly detectors on metrics in source accounts in addition to metrics in the monitoring account. +You can enable anomaly detection using the AWS Management Console, the AWS CLI, CloudFormation, or the AWS SDK. You can enable anomaly detection on metrics vended by AWS and also on custom metrics. In an account set up as a monitoring account for CloudWatch cross-account observability, you can create anomaly detectors on metrics in source accounts in addition to metrics in the monitoring account. @@ -17 +17 @@ You can use the model of expected values in two ways: - * Create outlier detection alarms based on a metric's expected value. These types of alarms don't have a static threshold for determining alarm state. Instead, they compare the metric's value to the expected value based on the outlier detection model. + * Create anomaly detection alarms based on a metric's expected value. These types of alarms don't have a static threshold for determining alarm state. Instead, they compare the metric's value to the expected value based on the anomaly detection model. @@ -30 +30 @@ You can also retrieve the upper and lower values of the model's band by using th -In a graph with outlier detection, the expected range of values is shown as a gray band. If the metric's actual value goes beyond this band, it is shown as red during that time. +In a graph with anomaly detection, the expected range of values is shown as a gray band. If the metric's actual value goes beyond this band, it is shown as red during that time. @@ -32 +32 @@ In a graph with outlier detection, the expected range of values is shown as a gr -Outlier detection algorithms account for the seasonality and trend changes of metrics. The seasonality changes could be hourly, daily, or weekly, as shown in the following examples. +Anomaly detection algorithms account for the seasonality and trend changes of metrics. The seasonality changes could be hourly, daily, or weekly, as shown in the following examples. @@ -34 +34 @@ Outlier detection algorithms account for the seasonality and trend changes of me - + @@ -36 +36 @@ Outlier detection algorithms account for the seasonality and trend changes of me - + @@ -38 +38 @@ Outlier detection algorithms account for the seasonality and trend changes of me - + @@ -42 +42 @@ The longer-range trends could be downward or upward. - + @@ -44 +44 @@ The longer-range trends could be downward or upward. -Outlier detection also works well with metrics with flat patterns. +Anomaly detection also works well with metrics with flat patterns. @@ -46 +46 @@ Outlier detection also works well with metrics with flat patterns. - + @@ -48 +48 @@ Outlier detection also works well with metrics with flat patterns. -## How CloudWatch outlier detection works +## How CloudWatch anomaly detection works @@ -50 +50 @@ Outlier detection also works well with metrics with flat patterns. -When you enable outlier detection for a metric, CloudWatch applies machine learning algorithms to the metric's past data to create a model of the metric's expected values. The model assesses both trends and hourly, daily, and weekly patterns of the metric. The algorithm trains on up to two weeks of metric data, but you can enable outlier detection on a metric even if the metric does not have a full two weeks of data. +When you enable anomaly detection for a metric, CloudWatch applies machine learning algorithms to the metric's past data to create a model of the metric's expected values. The model assesses both trends and hourly, daily, and weekly patterns of the metric. The algorithm trains on up to two weeks of metric data, but you can enable anomaly detection on a metric even if the metric does not have a full two weeks of data. @@ -52 +52 @@ When you enable outlier detection for a metric, CloudWatch applies machine learn -You specify a value for the outlier detection threshold that CloudWatch uses along with the model to determine the "normal" range of values for the metric. A higher value for the outlier detection threshold produces a thicker band of "normal" values. +You specify a value for the anomaly detection threshold that CloudWatch uses along with the model to determine the "normal" range of values for the metric. A higher value for the anomaly detection threshold produces a thicker band of "normal" values. @@ -54 +54 @@ You specify a value for the outlier detection threshold that CloudWatch uses alo -The machine learning model is specific to a metric and a statistic. For example, if you enable outlier detection for a metric using the `AVG` statistic, the model is specific to the `AVG` statistic. +The machine learning model is specific to a metric and a statistic. For example, if you enable anomaly detection for a metric using the `AVG` statistic, the model is specific to the `AVG` statistic. @@ -58 +58 @@ When CloudWatch creates a model for many common metrics from AWS services, it en -After you create a model, CloudWatch outlier detection continually evaluates the model and makes adjustments to it to ensure that it is as accurate as possible. This includes re-training the model to adjust if the metric values evolve over time or have sudden changes, and also includes predictors to improve the models of metrics that are seasonal, spiky, or sparse. +After you create a model, CloudWatch anomaly detection continually evaluates the model and makes adjustments to it to ensure that it is as accurate as possible. This includes re-training the model to adjust if the metric values evolve over time or have sudden changes, and also includes predictors to improve the models of metrics that are seasonal, spiky, or sparse. @@ -60 +60 @@ After you create a model, CloudWatch outlier detection continually evaluates the -After you enable outlier detection on a metric, you can choose to exclude specified time periods of the metric from being used to train the model. This way, you can exclude deployments or other unusual events from being used for model training, ensuring the most accurate model is created. +After you enable anomaly detection on a metric, you can choose to exclude specified time periods of the metric from being used to train the model. This way, you can exclude deployments or other unusual events from being used for model training, ensuring the most accurate model is created. @@ -62 +62 @@ After you enable outlier detection on a metric, you can choose to exclude specif -Using outlier detection models for alarms incurs charges on your AWS account. For more information, see [Amazon CloudWatch Pricing](http://aws.amazon.com/cloudwatch/pricing). +Using anomaly detection models for alarms incurs charges on your AWS account. For more information, see [Amazon CloudWatch Pricing](http://aws.amazon.com/cloudwatch/pricing). @@ -64 +64 @@ Using outlier detection models for alarms incurs charges on your AWS account. Fo -## Outlier detection on metric math +## Anomaly detection on metric math @@ -66 +66 @@ Using outlier detection models for alarms incurs charges on your AWS account. Fo -Outlier detection on metric math is a feature that you can use to create outlier detection alarms on the output metric math expressions. You can use these expressions to create graphs that visualize outlier detection bands. The feature supports basic arithmetic functions, comparison and logical operators, and most other functions. For information about functions that are not supported, see [Using metric math](https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/using-metric-math.html#using-anomaly-detection-on-metric-math) in the _Amazon CloudWatch User Guide_. +Anomaly detection on metric math is a feature that you can use to create anomaly detection alarms on the output metric math expressions. You can use these expressions to create graphs that visualize anomaly detection bands. The feature supports basic arithmetic functions, comparison and logical operators, and most other functions. For information about functions that are not supported, see [Using metric math](https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/using-metric-math.html#using-anomaly-detection-on-metric-math) in the _Amazon CloudWatch User Guide_. @@ -68 +68 @@ Outlier detection on metric math is a feature that you can use to create outlier -You can create outlier detection models based on metric math expressions similar to how you already create outlier detection models. From the CloudWatch console, you can apply outlier detection to metric math expressions and select outlier detection as a threshold type for these expressions. +You can create anomaly detection models based on metric math expressions similar to how you already create anomaly detection models. From the CloudWatch console, you can apply anomaly detection to metric math expressions and select anomaly detection as a threshold type for these expressions. @@ -72 +72 @@ You can create outlier detection models based on metric math expressions similar -Outlier detection on metric math only can be enabled and edited in the latest version of the metrics user interface. When you create anomaly detectors based on metric math expressions in the new version of the interface, you can view them in the old version, but not edit them. +Anomaly detection on metric math only can be enabled and edited in the latest version of the metrics user interface. When you create anomaly detectors based on metric math expressions in the new version of the interface, you can view them in the old version, but not edit them. @@ -74 +74 @@ Outlier detection on metric math only can be enabled and edited in the latest ve -For information about how to create, edit, and delete alarms and models for outlier detection and metric math, see the following sections: +For information about how to create, edit, and delete alarms and models for anomaly detection and metric math, see the following sections: @@ -78 +78 @@ For information about how to create, edit, and delete alarms and models for outl - * [Editing an outlier detection model](./Create_Anomaly_Detection_Alarm.html#Modify_Anomaly_Detection_Model) + * [Editing an anomaly detection model](./Create_Anomaly_Detection_Alarm.html#Modify_Anomaly_Detection_Model) @@ -80 +80 @@ For information about how to create, edit, and delete alarms and models for outl - * [Deleting an outlier detection model](./Create_Anomaly_Detection_Alarm.html#Delete_Anomaly_Detection_Model) + * [Deleting an anomaly detection model](./Create_Anomaly_Detection_Alarm.html#Delete_Anomaly_Detection_Model) @@ -87 +87 @@ For information about how to create, edit, and delete alarms and models for outl -You also can create, delete, and discover outlier detection models based on metric math expressions using the CloudWatch API with `PutAnomalyDetector`, `DeleteAnomalyDetector`, and `DescribeAnomalyDetectors`. For information about these API actions, see the following sections in the _Amazon CloudWatch API Reference_. +You also can create, delete, and discover anomaly detection models based on metric math expressions using the CloudWatch API with `PutAnomalyDetector`, `DeleteAnomalyDetector`, and `DescribeAnomalyDetectors`. For information about these API actions, see the following sections in the _Amazon CloudWatch API Reference_. @@ -98 +98 @@ You also can create, delete, and discover outlier detection models based on metr -For information about how outlier detection alarms are priced, see [Amazon CloudWatch pricing](http://aws.amazon.com/cloudwatch/pricing). +For information about how anomaly detection alarms are priced, see [Amazon CloudWatch pricing](http://aws.amazon.com/cloudwatch/pricing).