Explore how to identify events or patterns in data that differ from expected behavior using MATLAB.
This guide presents an overview of anomaly detection algorithm design approaches for engineering applications. It covers how to characterize normal behavior, analyze and preprocess data suitable for anomaly detection, select appropriate AI and statistical detection techniques, and deploy algorithms in operation.
You will learn about:
- Point, contextual, and collective anomalies in engineering data
- Statistical, machine learning, and deep learning based anomaly detection methods
- Working with datasets representing normal operating behavior and few labeled anomalies
- Design considerations for deployment
