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Reduce cost surprises and enhance control without slowing innovation with AWS Cost Anomaly Detection. AWS Cost Anomaly Detection leverages advanced Machine Learning technologies to identify anomalous spend and root causes, so you can quickly take action. With three simple steps, you can create your own contextualized monitor and receive alerts when any anomalous spend is detected. Let builders build and let AWS Cost Anomaly Detection monitor your spend and reduce the risk of billing surprises.
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Get started by creating AWS Cost Anomaly Detection via AWS Cost Explorer API, or directly in the Cost Management Console. Once you set up your monitor and alert preference, AWS will notify you with individual alerts or daily or weekly summary via Amazon Simple Notification Service (Amazon SNS) or emails. You can also monitor and do your own anomaly analysis in AWS Cost Explorer.
Save time on investigating spend anomalies by receiving automated root cause analysis, pin-pointing potential cost drivers, such as usage type (e.g. data transfer cost), specific AWS service, Region, and Member Account(s).
Stay informed of spend anomalies through automated detection alert, via email or Amazon SNS topic, at the frequency of your choice (individual alerts, daily summary, or weekly summary). With Amazon SNS topics, you can send alerts to your Slack channel or Amazon Chime chat room to support collaboration and timely resolution of alerts.
The cost monitor creation process allows you to create spend segments and evaluate spend anomalies in a preferred granular level. For example: an individual Linked Account, an individual Cost Category value, or an individual Cost Allocation tag.
Stay informed of spend anomalies through automated detection alert, via email or Amazon SNS topic, at the frequency of your choice. With Amazon SNS topics, you can send alerts to your Slack channel or Amazon Chime chat room to support collaboration and timely resolution of alerts.
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.
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.
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.
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.
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.
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.
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.
Using anomaly detection for an alarm accrues charges. As a best practice, if your alarm no longer needs an anomaly dection model, delete the alarm first and the model second. When anomaly dection 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.
(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.
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.
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.
You can enable anomaly detection using the AWS Management Console, the AWS CLI, AWS CloudFormation, or the AWS SDK. You can enable anomaly detection on metrics vended by AWS and also on custom metrics.
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.
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.
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.
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.
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 in the Amazon CloudWatch User Guide.
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.
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.
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.
This code provides experimental and simples tools for differents operations on climate data, mainly obtaining climatologies and anomalies values, in addition to others operations such as data extraction from continent, ocean or a shapefile.
As the nature of anomaly varies over different cases, a model may not workuniversally for all anomaly detection problems. Choosing and combiningdetection algorithms (detectors), feature engineering methods (transformers),and ensemble methods (aggregators) properly is the key to build an effectiveanomaly detection model.
This package offers a set of common detectors, transformers and aggregatorswith unified APIs, as well as pipe classes that connect them together into amodel. It also provides some functions to process and visualize time series andanomaly events.
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