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# Azure Cognitive Services Anomaly Detector client library for .NET
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Microsoft Azure Cognitive Services Anomaly Detector API enables you to monitor and detect abnormalities in your time series data with machine learning.
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[Anomaly Detector](https://learn.microsoft.com/azure/cognitive-services/Anomaly-Detector/overview) is an AI service with a set of APIs, which enables you to monitor and detect anomalies in your time series data with little machine learning (ML) knowledge, either batch validation or real-time inference.
* An existing Cognitive Services or Anomaly Detector resource.
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This table shows the relationship between SDK versions and supported API versions of the service:
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For more information about creating the resource or how to get the location and sku information see [here][cognitive_resource_cli].
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|SDK version|Supported API version of service |
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|-------------|---------------|
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|3.0.0-preview.6| 1.1|
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|3.0.0-preview.4, 3.0.0-preview.5| 1.1-preview-1|
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|3.0.0-beta.3 | 1.1-preview|
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|3.0.0-preview.1, 3.0.0-preview.2 | 1.0 |
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### Authenticate the client
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In order to interact with the Anomaly Detector service, you'll need to create an instance of the [`AnomalyDetectorClient`][anomaly_detector_client_class] class. You will need an **endpoint** and an **API key** to instantiate a client object.
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#### Get API Key
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You can find the endpoint for your Anomaly Detector service resource using the
or [Azure CLI](https://learn.microsoft.com/cli/azure/):
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```bash
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# Get the endpoint for the Anomaly Detector service resource
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az cognitiveservices account show --name "resource-name" --resource-group "resource-group-name" --query "properties.endpoint"
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```
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You can obtain the endpoint and API key from the resource information in the [Azure Portal][azure_portal].
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#### Get the API Key
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Alternatively, you can use the [Azure CLI][azure_cli] snippet below to get the API key from the Anomaly Detector resource.
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You can get the **API Key** from the Anomaly Detector service resource in the Azure Portal.
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Alternatively, you can use **Azure CLI** snippet below to get the API key of your resource.
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```PowerShell
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az cognitiveservices account keys list --resource-group <your-resource-group-name> --name <your-resource-name>
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```
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#### Create AnomalyDetectorClient with AzureKeyCredential
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Once you have the value for the API key, create an `AzureKeyCredential`. With the endpoint and key credential, you can create the [`AnomalyDetectorClient`][anomaly_detector_client_class]:
#### Create AnomalyDetectorClient with Azure Active Directory Credential
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##Key concepts
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`AzureKeyCredential` authentication is used in the examples in this getting started guide, but you can also authenticate with Azure Active Directory using the [Azure Identity library][azure_identity]. Note that regional endpoints do not support AAD authentication. Create a [custom subdomain][custom_subdomain] for your resource in order to use this type of authentication.
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With the Anomaly Detector, you can either detect anomalies in one variable using **Univariate Anomaly Detection**, or detect anomalies in multiple variables with **Multivariate Anomaly Detection**.
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To use the [DefaultAzureCredential][DefaultAzureCredential] provider shown below, or other credential providers provided with the Azure SDK, please install the `Azure.Identity` package:
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|Feature |Description |
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|---------|---------|
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|Univariate Anomaly Detection | Detect anomalies in one variable, like revenue, cost, etc. The model was selected automatically based on your data pattern. |
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|Multivariate Anomaly Detection| Detect anomalies in multiple variables with correlations, which are usually gathered from equipment or other complex system. The underlying model used is Graph attention network.|
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```dotnetcli
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dotnet add package Azure.Identity
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```
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### Univariate Anomaly Detection
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You will also need to [register a new AAD application][register_aad_app] and [grant access][aad_grant_access]to Anomaly Detector by assigning the `"Cognitive Services User"` role to your service principal.
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The Univariate Anomaly Detection API enables you to monitor and detect abnormalities in your time series data without having to know machine learning. The algorithms adapt by automatically identifying and applying the best-fitting models to your data, regardless of industry, scenario, or data volume. Using your time series data, the API determines boundaries for anomaly detection, expected values, and which data points are anomalies.
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Set the values of the client ID, tenant ID, and client secret of the AAD application as environment variables: AZURE_CLIENT_ID, AZURE_TENANT_ID, AZURE_CLIENT_SECRET.
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Using the Anomaly Detector doesn't require any prior experience in machine learning, and the REST API enables you to easily integrate the service into your applications and processes.
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```
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string endpoint = "<endpoint>";
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var client = new AnomalyDetectorClient(new Uri(endpoint), new DefaultAzureCredential());
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```
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With the Univariate Anomaly Detection, you can automatically detect anomalies throughout your time series data, or as they occur in real-time.
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## Key concepts
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|Feature |Description |
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|---------|---------|
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| Streaming detection| Detect anomalies in your streaming data by using previously seen data points to determine if your latest one is an anomaly. This operation generates a model using the data points you send, and determines if the target point is an anomaly. By calling the API with each new data point you generate, you can monitor your data as it's created. |
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| Batch detection | Use your time series to detect any anomalies that might exist throughout your data. This operation generates a model using your entire time series data, with each point analyzed with the same model. |
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| Change points detection | Use your time series to detect any trend change points that exist in your data. This operation generates a model using your entire time series data, with each point analyzed with the same model. |
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### Multivariate Anomaly Detection
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The **Multivariate Anomaly Detection** APIs further enable developers by easily integrating advanced AI for detecting anomalies from groups of metrics, without the need for machine learning knowledge or labeled data. Dependencies and inter-correlations between up to 300 different signals are now automatically counted as key factors. This new capability helps you to proactively protect your complex systems such as software applications, servers, factory machines, spacecraft, or even your business, from failures.
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With the Multivariate Anomaly Detection, you can automatically detect anomalies throughout your time series data, or as they occur in real-time. There are three processes to use Multivariate Anomaly Detection.
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-**Training**: Use Train Model API to create and train a model, then use Get Model Status API to get the status and model metadata.
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-**Inference**:
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- Use Async Inference API to trigger an asynchronous inference process and use Get Inference results API to get detection results on a batch of data.
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- You could also use Sync Inference API to trigger a detection on one timestamp every time.
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-**Other operations**: List Model API and Delete Model API are supported in Multivariate Anomaly Detection model for model management.
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### Thread safety
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We guarantee that all client instance methods are thread-safe and independent of each other ([guideline](https://azure.github.io/azure-sdk/dotnet_introduction.html#dotnet-service-methods-thread-safety)). This ensures that the recommendation of reusing client instances is always safe, even across threads.
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### Additional concepts
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Console.WriteLine("A change point was detected at index:");
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for (inti=0; i<request.Series.Count; ++i)
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{
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if (result.IsChangePoint[i])
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{
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Console.Write(i);
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Console.Write("");
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}
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}
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Console.WriteLine();
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}
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else
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{
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Console.WriteLine("No change point detected in the series.");
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}
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```
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### Multivariate Anomaly Detection Sample
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To see how to use Anomaly Detector library to conduct Multivariate Anomaly Detection, see this [sample](https://github.com/Azure/azure-sdk-for-net/blob/main/sdk/anomalydetector/Azure.AI.AnomalyDetector/tests/samples/Sample4_MultivariateDetect.cs).
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## Troubleshooting
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### Setting up console logging
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The simplest way to see the logs is to enable the console logging.
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To create an Azure SDK log listener that outputs messages to console use the AzureEventSourceListener.CreateConsoleLogger method.
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To learn more about other logging mechanisms see [Diagnostics Samples][logging].
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## Next steps
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These code samples show common scenario operations with the Azure Anomaly Detector library. More samples can be found under the [samples](https://github.com/Azure/azure-sdk-for-net/blob/main/sdk/anomalydetector/Azure.AI.AnomalyDetector/tests/samples/) directory.
For more extensive documentation on Azure Anomaly Detector, see the [Anomaly Detector documentation](https://learn.microsoft.com/azure/cognitive-services/anomaly-detector/overview) on docs.microsoft.com.
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## Contributing
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This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit [cla.microsoft.com][cla].
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This project has adopted the [Microsoft Open Source Code of Conduct][code_of_conduct]. For more information see the [Code of Conduct FAQ][coc_faq] or contact [opencode@microsoft.com][coc_contact] with any additional questions or comments.
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