What is Univariate Anomaly Detector?
The Anomaly Detector API enables you to monitor and detect abnormalities in your time series data without having to know machine learning. The Anomaly Detector API's 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.
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.
This documentation contains the following types of articles:
- The quickstarts are step-by-step instructions that let you make calls to the service and get results in a short period of time.
- The how-to guides contain instructions for using the service in more specific or customized ways.
- The conceptual articles provide in-depth explanations of the service's functionality and features.
- The tutorials are longer guides that show you how to use this service as a component in broader business solutions.
With the Anomaly Detector, you can automatically detect anomalies throughout your time series data, or as they occur in real-time.
|Anomaly detection in real-time.||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.|
|Detect anomalies throughout your data set as a batch.||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.|
|Detect change points throughout your data set as a batch.||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.|
|Get additional information about your data.||Get useful details about your data and any observed anomalies, including expected values, anomaly boundaries, and positions.|
|Adjust anomaly detection boundaries.||The Anomaly Detector API automatically creates boundaries for anomaly detection. Adjust these boundaries to increase or decrease the API's sensitivity to data anomalies, and better fit your data.|
Check out this interactive demo to understand how Anomaly Detector works. To run the demo, you need to create an Anomaly Detector resource and get the API key and endpoint.
To learn how to call the Anomaly Detector API, try this Notebook. This Jupyter Notebook shows you how to send an API request and visualize the result.
To run the Notebook, you should get a valid Anomaly Detector API subscription key and an API endpoint. In the notebook, add your valid Anomaly Detector API subscription key to the
subscription_key variable, and change the
endpoint variable to your endpoint.
The Anomaly Detector API is a RESTful web service, making it easy to call from any programming language that can make HTTP requests and parse JSON.
For best results when using the Anomaly Detector API, your JSON-formatted time series data should include:
- data points separated by the same interval, with no more than 10% of the expected number of points missing.
- at least 12 data points if your data doesn't have a clear seasonal pattern.
- at least 4 pattern occurrences if your data does have a clear seasonal pattern.
After signing up:
- Take your time series data and convert it into a valid JSON format. Use best practices when preparing your data to get the best results.
- Send a request to the Anomaly Detector API with your data.
- Process the API response by parsing the returned JSON message.
- See the following technical blogs for information about the algorithms used:
You can read the paper Time-Series Anomaly Detection Service at Microsoft (accepted by KDD 2019) to learn more about the SR-CNN algorithms developed by Microsoft.
Service availability and redundancy
Is the Anomaly Detector service zone resilient?
Yes. The Anomaly Detector service is zone-resilient by default.
How do I configure the Anomaly Detector service to be zone-resilient?
No customer configuration is necessary to enable zone-resiliency. Zone-resiliency for Anomaly Detector resources is available by default and managed by the service itself.
Deploy on premises using Docker containers
Use Anomaly Detector containers to deploy API features on-premises. Docker containers enable you to bring the service closer to your data for compliance, security, or other operational reasons.
Join the Anomaly Detector community
- Quickstart: Detect anomalies in your time series data using the Anomaly Detector
- The Anomaly Detector API online demo
- The Anomaly Detector REST API reference