Function referenceedit

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, count, low_count, and 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 metric. When you use summary_count_field_name, the machine learning features expect the input data to be pre-aggregated. The value of the summary_count_field_name field 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 sum and count functions are strongly affected by empty buckets. For this reason, there are non_null_sum and non_zero_count functions, which are tolerant to sparse data. These functions effectively ignore empty buckets.