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The X-Pack machine learning features automate the analysis of time-series data by creating accurate baselines of normal behaviors in the data and identifying anomalous patterns in that data.
Using proprietary machine learning algorithms, the following circumstances are detected, scored, and linked with statistically significant influencers in the data:
- Anomalies related to temporal deviations in values, counts, or frequencies
- Statistical rarity
- Unusual behaviors for a member of a population
Automated periodicity detection and quick adaptation to changing data ensure that you don’t need to specify algorithms, models, or other data science-related configurations in order to get the benefits of machine learning.
Machine learning is tightly integrated with the Elastic Stack. Data is pulled from Elasticsearch for analysis and anomaly results are displayed in Kibana dashboards.