Time Series Anomaly Detection with ML.NET

Unusual spikes, sudden drops, and unexpected patterns in time series data can signal critical system failures, security breaches, or financial shifts. Identifying these anomalies manually is impossible at scale, but machine learning offers a powerful automated solution for .NET developers. In this text-based course, you will learn how to build robust anomaly detection systems using ML.NET. You will start with the core concepts of time series data and anomaly types, then transition to implementing practical detection pipelines. By reading through conceptual breakdowns and analyzing clear C# code snippets, you will gain the skills to integrate intelligent alerting and monitoring directly into your .NET applications. What you'll learn: Understand the fundamental types of anomalies, including spikes, change points, and seasonal outliers; Configure the ML.NET environment and prepare time series data for machine learning pipelines; Apply the Spectral Residual Convolutional Neural Network (SRCNN) algorithm to detect anomalies in streaming data; Implement Randomized PCA to identify structural changes and outliers in multivariate datasets; Evaluate model performance using key metrics to minimize false positives and ensure detection accuracy; Integrate anomaly detection models into real-world C# applications using modern dependency injection patterns. The course begins with foundational definitions of time series metrics and anomaly detection theory. You will then progress through step-by-step written implementations of key ML.NET trainers, learning how to tune hyperparameters and interpret model predictions. This course is designed for C# developers, software engineers, and data enthusiasts who want to add machine learning capabilities to their applications. No prior machine learning experience is required, though a basic familiarity with C# and .NET development is recommended. Start reading today to build smarter, more responsive .NET applications that catch anomalies before they become critical issues.