Online (Real-Time) Anomaly Detection

Online anomaly detection dynamically identifies anomalies as data streams in, allowing users to specify the number of timestamps to monitor. This method is well-suited for immediate applications, such as fraud detection, live sensor monitoring, or tracking real-time demand changes. By focusing on recent data and continuously generating forecasts, it enables timely responses to anomalies in critical scenarios.

This section provides various recipes for performing real-time anomaly detection using TimeGPT, offering users the ability to detect outliers and unusual patterns as they emerge, ensuring prompt intervention in time-sensitive situations.

This section covers:

• Online anomaly detection

• How to adjust the detection process

• Univariate vs. multiseries anomaly detection