- Machine Learning: other versions:
- What is Elastic Machine Learning?
- Setup and security
- Anomaly detection
- Finding anomalies
- Tutorial: Getting started with anomaly detection
- Advanced concepts
- API quick reference
- How-tos
- Generating alerts for anomaly detection jobs
- Aggregating data for faster performance
- Altering data in your datafeed with runtime fields
- Customizing detectors with custom rules
- Detecting anomalous categories of data
- Reverting to a model snapshot
- Detecting anomalous locations in geographic data
- Mapping anomalies by location
- Adding custom URLs to machine learning results
- Anomaly detection jobs from visualizations
- Exporting and importing machine learning jobs
- Resources
- Data frame analytics
- Natural language processing
Finding anomalies in time series data
editFinding anomalies in time series data
editThe machine learning anomaly detection features automate the analysis of time series data by creating accurate baselines of normal behavior in your data. These baselines then enable you to identify anomalous events or patterns. Data is pulled from Elasticsearch for analysis and anomaly results are displayed in Kibana dashboards. For example, the Machine Learning app provides charts that illustrate the actual data values, the bounds for the expected values, and the anomalies that occur outside these bounds.
The typical workflow for performing anomaly detection is as follows:
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