Anomaly Detection and Forecasting

Anomaly detection is the process of identifying data points, events, or observations that deviate significantly from normal behavior in a dataset. These anomalies often signal critical situations, such as system failures, fraudulent activity, or unexpected trends in time-series data.

Forecasting is the process of predicting the future value for time-series metrics based on the historical data.

Anomaly detection is used across various domains to identify unexpected behavior and prevent potential issues. Common applications include:

• Identifying unusual patterns in financial transactions that may indicate fraud or abnormal user behavior.
• Monitoring system performance metrics to detect early signs of failures, slowdowns, or configuration issues.

Before You Begin

To enable the Anomaly Detection capability in Pulse, ensure to deploy the Deploy Anomaly Detection Add-on Service.

Explore In Detail

• Anomaly Detection in Charts
• Configure Anomaly Detection Model Training
• Create Forecasting Alert