Fabric RTI 101: AI Anomaly Detection - Available Models

The AI Anomaly Detector in Microsoft Fabric Real-Time Intelligence uses a combination of time-series analysis and machine learning models. You don’t need to choose the model manually-Fabric evaluates the data and automatically selects the most appropriate approach.

Behind the scenes, it can use classical models like seasonal decomposition or ARIMA for steady, predictable series, or neural network–based models for more complex and irregular data. It also supports hybrid methods that adapt to both short-term trends and cope with long-term seasonality.

An important aspect is that these models aren’t static. The system continues to retrain as new data flows in, so it adapts automatically to changing baselines and patterns over time. This helps reduce false positives and improves detection reliability in dynamic environments like IoT telemetry or financial transactions.

Full list of models here: https://learn.microsoft.com/en-us/fabric/real-time-intelligence/anomaly-detection-models

Learn more about Fabric RTI

If you really want to learn about RTI right now, we have an online on-demand course that you can enrol in, right now. You’ll find it at Mastering Microsoft Fabric Real-Time Intelligence

2026-09-28