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[[ml-analyzing]]
=== Analyzing the Past and Present

The {xpackml} features automate the analysis of time-series data by creating
accurate baselines of normal behavior in the data and identifying anomalous
patterns in that data. You can submit your data for analysis in batches or
continuously in real-time {dfeeds}.

Using proprietary {ml} 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 {ml}.

You can view the {ml} results in {kib} where, for example, charts illustrate the
actual data values, the bounds for the expected values, and the anomalies that
occur outside these bounds.

[role="screenshot"]
image::images/ml-gs-job-analysis.jpg["Example screenshot from the Machine Learning Single Metric Viewer in Kibana"]

For a more detailed walk-through of {xpackml} features, see
<<ml-getting-started>>.