30 lines
1.2 KiB
Plaintext
30 lines
1.2 KiB
Plaintext
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[[ml-analyzing]]
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=== Analyzing the Past and Present
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The {xpackml} features automate the analysis of time-series data by creating
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accurate baselines of normal behavior in the data and identifying anomalous
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patterns in that data. You can submit your data for analysis in batches or
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continuously in real-time {dfeeds}.
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Using proprietary {ml} algorithms, the following circumstances are detected,
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scored, and linked with statistically significant influencers in the data:
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* Anomalies related to temporal deviations in values, counts, or frequencies
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* Statistical rarity
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* Unusual behaviors for a member of a population
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Automated periodicity detection and quick adaptation to changing data ensure
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that you don’t need to specify algorithms, models, or other data science-related
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configurations in order to get the benefits of {ml}.
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You can view the {ml} results in {kib} where, for example, charts illustrate the
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actual data values, the bounds for the expected values, and the anomalies that
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occur outside these bounds.
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[role="screenshot"]
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image::images/ml-gs-job-analysis.jpg["Example screenshot from the Machine Learning Single Metric Viewer in Kibana"]
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For a more detailed walk-through of {xpackml} features, see
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<<ml-getting-started>>.
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