- Machine Learning: other versions:
- Setup and security
- Getting started with machine learning
- Anomaly detection
- Overview
- Concepts
- Configure anomaly detection
- API quick reference
- Supplied configurations
- Function reference
- Examples
- Generating alerts for anomaly detection jobs
- Aggregating data for faster performance
- Customizing detectors with custom rules
- Detecting anomalous categories of data
- Detecting anomalous locations in geographic data
- Performing population analysis
- Altering data in your datafeed with runtime fields
- Adding custom URLs to machine learning results
- Handling delayed data
- Mapping anomalies by location
- Exporting and importing machine learning jobs
- Limitations
- Troubleshooting
- Data frame analytics
A newer version is available. For the latest information, see the
current release documentation.
Uptime anomaly detection configurations
editUptime anomaly detection configurations
editIf you have appropriate Heartbeat data in Elasticsearch, you can enable this anomaly detection job in the Uptime app in Kibana. For more details, see the datafeed and job definitions in GitHub.
These configurations are only available if data exists that matches the recognizer query specified in the manifest file.
- high_latency_by_geo
-
-
Detects unusually high average latency values (using the
high_mean
function on themonitor.duration.us
field). -
Models the occurrences across geographical locations (
partition_field_name
isobserver.geo.name
).
-
Detects unusually high average latency values (using the
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