Historical anomaly detection

This section provides various recipes for performing historical anomaly detection using TimeGPT.

Historical anomaly detection identifies data points that deviate from the expected behavior over a given historical time series, helping to spot fraudulent activity, security breaches, or significant outliers.

The process involves generating predictions and constructing a 99% confidence interval. Data points falling outside this interval are considered anomalies.

This section covers:

• Historical anomaly detection

• Historical anomaly detection with exogenous features

• Historical anomaly detection with date features

• Modifying the confidence intervals