The machine learning features include analysis functions that provide a wide variety of flexible ways to analyze data for anomalies.
When you create anomaly detection jobs, you specify one or more detectors, which define the type of analysis that needs to be done. If you are creating your job by using machine learning APIs, you specify the functions in Detector configuration objects. If you are creating your job in Kibana, you specify the functions differently depending on whether you are creating single metric, multi-metric, or advanced jobs.
Most functions detect anomalies in both low and high values. In statistical
terminology, they apply a two-sided test. Some functions offer low and high
variations (for example,
high_count). These variations
apply one-sided tests, detecting anomalies only when the values are low or
high, depending one which alternative is used.
You can specify a
summary_count_field_name with any function except
When you use
summary_count_field_name, the machine learning features expect the input
data to be pre-aggregated. The value of the
must contain the count of raw events that were summarized. In Kibana, use the
summary_count_field_name in advanced anomaly detection jobs. Analyzing aggregated
input data provides a significant boost in performance. For more information, see
Aggregating data for faster performance.
If your data is sparse, there may be gaps in the data which means you might have
empty buckets. You might want to treat these as anomalies or you might want these
gaps to be ignored. Your decision depends on your use case and what is important
to you. It also depends on which functions you use. The
functions are strongly affected by empty buckets. For this reason, there are
non_zero_count functions, which are tolerant to sparse data.
These functions effectively ignore empty buckets.