AWS AmazonCloudWatch documentation change
Summary
Updated terminology from 'outlier detection' to 'anomaly detection' throughout the document, including section headers, UI labels, and conceptual explanations
Security assessment
This is a terminology standardization change without any security implications. Updates involve renaming 'outlier detection' to 'anomaly detection' consistently across the documentation. No vulnerabilities, security controls, or incident response procedures are mentioned. The core functionality described (creating detection models, setting thresholds, alarm behavior) remains conceptually unchanged.
Diff
diff --git a/AmazonCloudWatch/latest/monitoring/Create_Anomaly_Detection_Alarm.md b/AmazonCloudWatch/latest/monitoring/Create_Anomaly_Detection_Alarm.md index d2eb5f744..9cab7fadd 100644 --- a//AmazonCloudWatch/latest/monitoring/Create_Anomaly_Detection_Alarm.md +++ b//AmazonCloudWatch/latest/monitoring/Create_Anomaly_Detection_Alarm.md @@ -5 +5 @@ -Editing an outlier detection modelDeleting an outlier detection model +Editing an anomaly detection modelDeleting an anomaly detection model @@ -9 +9 @@ Editing an outlier detection modelDeleting an outlier detection model -You can create an alarm based on CloudWatch outlier detection, which analyzes past metric data and creates a model of expected values. The expected values take into account the typical hourly, daily, and weekly patterns in the metric. +You can create an alarm based on CloudWatch anomaly detection, which analyzes past metric data and creates a model of expected values. The expected values take into account the typical hourly, daily, and weekly patterns in the metric. @@ -11 +11 @@ You can create an alarm based on CloudWatch outlier detection, which analyzes pa -You set a value for the outlier detection threshold, and CloudWatch uses this threshold with the model to determine the "normal" range of values for the metric. A higher value for the threshold produces a thicker band of "normal" values. +You set a value for the anomaly detection threshold, and CloudWatch uses this threshold with the model to determine the "normal" range of values for the metric. A higher value for the threshold produces a thicker band of "normal" values. @@ -15 +15 @@ You can choose whether the alarm is triggered when the metric value is above the -You also can create outlier detection alarms on single metrics and the outputs of metric math expressions. You can use these expressions to create graphs that visualize anomaly detection bands. +You also can create anomaly detection alarms on single metrics and the outputs of metric math expressions. You can use these expressions to create graphs that visualize anomaly detection bands. @@ -19 +19 @@ In an account set up as a monitoring account for CloudWatch cross-account observ -For more information, see [Using CloudWatch outlier detection](./CloudWatch_Anomaly_Detection.html). +For more information, see [Using CloudWatch anomaly detection](./CloudWatch_Anomaly_Detection.html). @@ -23 +23 @@ For more information, see [Using CloudWatch outlier detection](./CloudWatch_Anom -If you're already using outlier detection for visualization purposes on a metric in the Metrics console and you create an outlier detection alarm on that same metric, then the threshold that you set for the alarm doesn't change the threshold that you already set for visualization. For more information, see [Creating a graph](./graph_a_metric.html#create-metric-graph). +If you're already using anomaly detection for visualization purposes on a metric in the Metrics console and you create an anomaly detection alarm on that same metric, then the threshold that you set for the alarm doesn't change the threshold that you already set for visualization. For more information, see [Creating a graph](./graph_a_metric.html#create-metric-graph). @@ -25 +25 @@ If you're already using outlier detection for visualization purposes on a metric -###### To create an alarm that's based on outlier detection +###### To create an alarm that's based on anomaly detection @@ -49 +49 @@ If you're already using outlier detection for visualization purposes on a metric -When CloudWatch evaluates your alarm, it aggragates the period into a single datapoint. For an outlier detection alarm, the evaluation period must be one minute or longer. +When CloudWatch evaluates your alarm, it aggragates the period into a single datapoint. For an anomaly detection alarm, the evaluation period must be one minute or longer. @@ -55 +55 @@ When CloudWatch evaluates your alarm, it aggragates the period into a single dat - 1. Choose **Outlier detection**. + 1. Choose **Anomaly detection**. @@ -57 +57 @@ When CloudWatch evaluates your alarm, it aggragates the period into a single dat -If the model for this metric and statistic already exists, CloudWatch displays a preview of the outlier detection band in the graph at the top of the screen. After you create your alarm, it can take up to 15 minutes for the actual outlier detection band to appear in the graph. Before that, the band that you see is an approximation of the outlier detection band. +If the model for this metric and statistic already exists, CloudWatch displays a preview of the anomaly detection band in the graph at the top of the screen. After you create your alarm, it can take up to 15 minutes for the actual anomaly detection band to appear in the graph. Before that, the band that you see is an approximation of the anomaly detection band. @@ -63 +63 @@ To see the graph at the top of the screen in a longer time frame, choose **Edit* -If the model for this metric and statistic doesn't already exist, CloudWatch generates the outlier detection band after you finish creating your alarm. For new models, it can take up to 3 hours for the actual outlier detection band to appear in your graph. It can take up to two weeks for the new model to train, so the outlier detection band shows more accurate expected values. +If the model for this metric and statistic doesn't already exist, CloudWatch generates the anomaly detection band after you finish creating your alarm. For new models, it can take up to 3 hours for the actual anomaly detection band to appear in your graph. It can take up to two weeks for the new model to train, so the anomaly detection band shows more accurate expected values. @@ -67 +67 @@ If the model for this metric and statistic doesn't already exist, CloudWatch gen - 3. For **Outlier detection threshold** , choose the number to use for the outlier detection threshold. A higher number creates a thicker band of "normal" values that is more tolerant of metric changes. A lower number creates a thinner band that will go to `ALARM` state with smaller metric deviations. The number does not have to be a whole number. + 3. For **Anomaly detection threshold** , choose the number to use for the anomaly detection threshold. A higher number creates a thicker band of "normal" values that is more tolerant of metric changes. A lower number creates a thinner band that will go to `ALARM` state with smaller metric deviations. The number does not have to be a whole number. @@ -108 +108 @@ The alarm name must contain only UTF-8 characters, and can't contain ASCII contr -## Editing an outlier detection model +## Editing an anomaly detection model @@ -110 +110 @@ The alarm name must contain only UTF-8 characters, and can't contain ASCII contr -After you create an alarm, you can adjust the outlier detection model. You can exclude certain time periods from being used in the model creation. It is critical that you exclude unusual events such as system outages, deployments, and holidays from the training data. You can also specify whether to adjust the model for Daylight Savings Time changes. +After you create an alarm, you can adjust the anomaly detection model. You can exclude certain time periods from being used in the model creation. It is critical that you exclude unusual events such as system outages, deployments, and holidays from the training data. You can also specify whether to adjust the model for Daylight Savings Time changes. @@ -112 +112 @@ After you create an alarm, you can adjust the outlier detection model. You can e -###### To edit the outlier detection model for an alarm +###### To edit the anomaly detection model for an alarm @@ -122 +122 @@ After you create an alarm, you can adjust the outlier detection model. You can e - 5. In the **Details** column, choose the **ANOMALY_DETECTION_BAND** keyword, and then choose **Edit outlier detection model** in the popup. + 5. In the **Details** column, choose the **ANOMALY_DETECTION_BAND** keyword, and then choose **Edit anomaly detection model** in the popup. @@ -135 +135 @@ After you create an alarm, you can adjust the outlier detection model. You can e -## Deleting an outlier detection model +## Deleting an anomaly detection model @@ -137 +137 @@ After you create an alarm, you can adjust the outlier detection model. You can e -Using outlier detection for an alarm accrues charges. As a best practice, if your alarm no longer needs an outlier detection model, delete the alarm first and the model second. When outlier detection alarms are evaluated, any missing anomaly detectors are created on your behalf. If you delete the model without deleting the alarm, the alarm automatically recreates the model. +Using anomaly detection for an alarm accrues charges. As a best practice, if your alarm no longer needs an anomaly detection model, delete the alarm first and the model second. When anomaly detection alarms are evaluated, any missing anomaly detectors are created on your behalf. If you delete the model without deleting the alarm, the alarm automatically recreates the model. @@ -154 +154 @@ Using outlier detection for an alarm accrues charges. As a best practice, if you -###### To delete an outlier detection model that was used for an alarm +###### To delete an anomaly detection model that was used for an alarm @@ -160 +160 @@ Using outlier detection for an alarm accrues charges. As a best practice, if you - 3. Choose **Browse** , and then select the metric that includes the outlier detection model. You can search for your metric in the search box or select your metric by choosing through the options. + 3. Choose **Browse** , and then select the metric that includes the anomaly detection model. You can search for your metric in the search box or select your metric by choosing through the options. @@ -162 +162 @@ Using outlier detection for an alarm accrues charges. As a best practice, if you - * (Optional) If you're using the original interface, choose **All metrics** , and then choose the metric that includes the outlier detection model. You can search for your metric in the search box or select your metric by choosing through the options. + * (Optional) If you're using the original interface, choose **All metrics** , and then choose the metric that includes the anomaly detection model. You can search for your metric in the search box or select your metric by choosing through the options. @@ -166 +166 @@ Using outlier detection for an alarm accrues charges. As a best practice, if you - 5. In the **Graphed metrics** tab, in the **Details** column, choose the **ANOMALY_DETECTION_BAND** keyword, and then choose **Delete outlier detection model** in the popup. + 5. In the **Graphed metrics** tab, in the **Details** column, choose the **ANOMALY_DETECTION_BAND** keyword, and then choose **Delete anomaly detection model** in the popup.