What anomaly detection algorithms does Microsoft offer in the Anomaly Detection API

Question

I am exploring the Microsoft Azure anomaly detection API for potential use in my organization. I want to know what algorithms (such as isolation forest, one-class SVM, autoencoder based anomaly detection) are available in the library. What choice of statistical models do I have through this API?

Answer 1

Do you mean the Anomaly detector in Azure cognitive service. So far as I observed, the API is not only take single model or algorithm. You can check its response, if you don't give period information, it will return one, it means the algorithm behind also calculate the period of the time series, maybe it is one feature they used to select model or algo. I tried to send seasonal time series, non-seasonal time series, uptrend and down trend, it all return correct good result. Not sure they can expose the detail about the algorithms. But I can see same behavior if I used DFT, STL for seasonal time series and ESD or simple Zscore for non seasonal.

Sometimes the "entire" detection cannot return expected value as the "last" detection in a stream mode, but according to the API reference, https://aka.ms/anomaly-detector-rest-api-ref , it seems the "entire" API build a single model or select one algo to do the detection and points after the target point are taken into account, so it should be anomaly in the whole time series. But the "last" api only use the points before the target point, so it mean the anomaly according to the history. It is a little confused though, but in streaming monitoring scenario, I think the "last" it the right choice, it is faster and more accurate.

Answer 2

According to Microsoft Tech Community

The following algorithms are used:

• Fourier Transformation
• Extreme Studentized Deviate (ESD)
• STL Decomposition
• Dynamic Threshold
• Z-score detector
• Some advanced algorithms to be disclosed pending paper publishing.

---