# Using anomaly detection in CloudWatch Logs Insights
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In addition to creating log anomaly detectors for continuous monitoring, you can also use the `anomaly` command in CloudWatch Logs Insights queries to identify unusual patterns in your log data on-demand. This command extends the existing `pattern` functionality and uses machine learning to detect five types of anomalies including pattern frequency changes, new patterns, and token variations.

The `anomaly` command is particularly useful for:
+ Ad-hoc analysis of historical log data to identify unusual patterns
+ Investigating specific time periods for anomalous behavior
+ Monitoring applications like Lambda functions for execution issues

For more information about using the `anomaly` command in your queries, see [anomaly](CWL_QuerySyntax-Anomaly.md).

This query-based anomaly detection complements the continuous anomaly detectors described in the following sections, giving you both real-time monitoring and on-demand analysis capabilities.