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This repository contains a sample for Anomaly Detector API which data comes from a CSV. The Anomaly Detector API enables you to monitor and find abnormalities in your time series data by automatically identifying and applying the correct statistical models, regardless of industry, scenario, or data volume.


Anomaly Detector API Samples

This repository contains samples for Anomaly Detector API. The Anomaly Detector API enables you to monitor and find abnormalities in your time series data by automatically identifying and applying the correct statistical models, regardless of industry, scenario, or data volume. Using your time series data, the API can find anomalies as a batch throughout your data, or determine if your latest data point is an anomaly.

Contents

File/folder Description
example-data Example data to be sent to the API, along with example API responses.
ipython-notebook Jupyter notebooks.
quickstarts Sample code for the Anomaly Detector API quickstarts.

Prerequisites

You must have an Anomaly Detector API resource. Before continuing, you will need the API key and the endpoint from your Azure dashboard. Get Anomaly Detector access keys

Or you could create a 7-day free resource of Anomaly Detector from here.

If you want to run the notebook with an on-premise version of Anomaly Detector as container, there're four prerequisites that must be met:

  1. You have access to the Azure Container Registry which hosts the Anomaly Detector container images. Please complete and submit the Anomaly Detector Container Request form to request access to the container.
  2. You have created an Anomaly Detector resource on Azure.
  3. You have the proper container environment ready to host the Anomaly Detector container. Please read Prerequisites and The host computer for details.
  4. You have Jupyter Notebook installed on your computer. We recommend installing Python and Jupyter using the Anaconda Distribution.

After you pull the container image and spin it up, ensure there's an HTTP endpoint accessible to the APIs and this will be your endpoint for the demo.

Online demo

To quickly start using the Anomaly Detector API, try an online demo. This demo runs in a web-hosted Jupyter notebook and shows you how to send an API request, and visualize the result.

To run the demo, complete the following steps:

  1. Sign in, and click Clone, in the upper right corner.
  2. Uncheck the "public" option in the dialog box before completing the clone operation, otherwise your notebook, including any subscription keys, will be public.
  3. Click Run on free compute
  4. Open one of the notebooks, for example, Anomaly Finder API Example Private Preview (Batch Method).ipynb
  5. Fill in the API key and the endpoint from your Anomaly Detector resource on Azure
  6. In the Notebook main menu, click Cell->run all

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