Learn how to improve anomaly detection by incorporating external factors.
detect_anomalies
method to identify anomalies. The method will automatically detect and utilize any exogenous features present in your DataFrame:
['month', 'year']
and enabling date_features_to_one_hot=True
, TimeGPT automatically encodes these as one-hot vectors. This allows the model to better detect seasonal patterns, calendar effects, and periodic anomalies.
plot
method to visualize the detected anomalies in the time series data.
Detected anomalies in time series with exogenous variables
weights_x
method to view the relative weights of the exogenous features to understand their impact:
Weights of exogenous date features