Does This Look Weird to You? An Introduction to Anomaly Detection
ABSTRACT
Discovering anomalies is a critical part of systems monitoring, financial auditing, and more. In this session, we will gain an understanding of what constitutes an anomaly, starting with a general concept of anomalies and then moving into technical definitions. We will review techniques for detecting anomalies for both time series and non-series data and implement a simple anomaly detector in .NET. Finally, we will compare these results against Microsoft's anomaly detector in Azure Cognitive Services.ADDITIONAL MEDIA
No recordings or additional media are available for this talk.SLIDES
Click here to access the slides for this presentation.
The slides are licensed under Creative Commons Attribution-ShareAlike.
DEMO CODE
Click here to access demo code for this presentation.
The source code is licensed under the terms offered by the GPL.
LINKS & FURTHER INFO
Demos
- Microsoft has a demo of the Azure Cognitive Services Anomaly Detector API. Note that you do need an endpoint for this.
Links
- This post from Codesuji provided the basis for my Basic Anomaly Detector project. I made a few changes, particularly in the visualization (such as using Plotly instead of Google Charts), but this post provides the foundation.
- Microsoft has a quickstart on using their Anomaly Detection API in Azure Cognitive Services. This example is in C# and I converted it to F# for the demo.
- Microsoft has also provided best practices when using the Anomaly Detector API.
Packages
Here are links to the packages I reference during the talk.
- Anomalize (R)
- Time-series Anomaly Detection with Prophet by Facebook (R)
- AnomalyDetection by Twitter (R)
- anomaly (R)
- Anomaly Detection Toolkit (Python)
- PyOD (Python)
- ML.NET (.NET)
- Azure Cognitive Services Anomaly Detector (Multi)