elasticsearch-js/docs/reference/api/MlAnomaly.md

MlAnomaly [interface-MlAnomaly]

Name	Type	Description
actual	double[]	The actual value for the bucket.
anomaly_score_explanation	MlAnomalyExplanation	Information about the factors impacting the initial anomaly score.
bucket_span	DurationValue<UnitSeconds>	The length of the bucket in seconds. This value matches the bucket_span that is specified in the job.
by_field_name	string	The field used to split the data. In particular, this property is used for analyzing the splits with respect to their own history. It is used for finding unusual values in the context of the split.
by_field_value	string	The value of by_field_name.
causes	MlAnomalyCause[]	For population analysis, an over field must be specified in the detector. This property contains an array of anomaly records that are the causes for the anomaly that has been identified for the over field. This sub-resource contains the most anomalous records for the over_field_name. For scalability reasons, a maximum of the 10 most significant causes of the anomaly are returned. As part of the core analytical modeling, these low-level anomaly records are aggregated for their parent over field record. The causes resource contains similar elements to the record resource, namely actual, typical, geo_results.actual_point, geo_results.typical_point, *_field_name and *_field_value. Probability and scores are not applicable to causes.
detector_index	integer	A unique identifier for the detector.
field_name	string	Certain functions require a field to operate on, for example, sum(). For those functions, this value is the name of the field to be analyzed.
function_description	string	The description of the function in which the anomaly occurs, as specified in the detector configuration.
function	string	The function in which the anomaly occurs, as specified in the detector configuration. For example, max.
geo_results	MlGeoResults	If the detector function is lat_long, this object contains comma delimited strings for the latitude and longitude of the actual and typical values.
influencers	MlInfluence[]	If influencers were specified in the detector configuration, this array contains influencers that contributed to or were to blame for an anomaly.
initial_record_score	double	A normalized score between 0-100, which is based on the probability of the anomalousness of this record. This is the initial value that was calculated at the time the bucket was processed.
is_interim	boolean	If true, this is an interim result. In other words, the results are calculated based on partial input data.
job_id	string	Identifier for the anomaly detection job.
over_field_name	string	The field used to split the data. In particular, this property is used for analyzing the splits with respect to the history of all splits. It is used for finding unusual values in the population of all splits.
over_field_value	string	The value of over_field_name.
partition_field_name	string	The field used to segment the analysis. When you use this property, you have completely independent baselines for each value of this field.
partition_field_value	string	The value of partition_field_name.
probability	double	The probability of the individual anomaly occurring, in the range 0 to 1. For example, 0.0000772031. This value can be held to a high precision of over 300 decimal places, so the record_score is provided as a human-readable and friendly interpretation of this.
record_score	double	A normalized score between 0-100, which is based on the probability of the anomalousness of this record. Unlike initial_record_score, this value will be updated by a re-normalization process as new data is analyzed.
result_type	string	Internal. This is always set to record.
timestamp	EpochTime<UnitMillis>	The start time of the bucket for which these results were calculated.
typical	double[]	The typical value for the bucket, according to analytical modeling.