- 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
IMPORTANT: No additional bug fixes or documentation updates
will be released for this version. For the latest information, see the
current release documentation.
Anomaly detection
editAnomaly detection
editUse anomaly detection to analyze time series data by creating accurate baselines of normal behavior and identifying anomalous patterns in your dataset. Data is pulled from Elasticsearch for analysis and anomaly results are displayed in Kibana dashboards. Consult Setup and security to learn more about the licence and the security privileges that are required to use anomaly detection.
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