Values.RandomCutForestConfigurationSourceConfiguration for the Random Cut Forest algorithm used for anomaly detection in time-series data.
type nonrec t = {query : RandomCutForestQuery.t;The Prometheus query used to retrieve the time-series data for anomaly detection. Random Cut Forest queries must be wrapped by a supported PromQL aggregation operator. For more information, see Aggregation operators on the Prometheus docs website. Supported PromQL aggregation operators: avg, count, group, max, min, quantile, stddev, stdvar, and sum.
*)shingleSize : RandomCutForestConfigurationShingleSizeInteger.t option;The number of consecutive data points used to create a shingle for the Random Cut Forest algorithm. The default number is 8 consecutive data points.
*)sampleSize : RandomCutForestConfigurationSampleSizeInteger.t option;The number of data points sampled from the input stream for the Random Cut Forest algorithm. The default number is 256 consecutive data points.
*)ignoreNearExpectedFromAbove : IgnoreNearExpected.t option;Configuration for ignoring values that are near expected values from above during anomaly detection.
*)ignoreNearExpectedFromBelow : IgnoreNearExpected.t option;Configuration for ignoring values that are near expected values from below during anomaly detection.
*)}val make :
?shingleSize:??? ->
?sampleSize:??? ->
?ignoreNearExpectedFromAbove:??? ->
?ignoreNearExpectedFromBelow:??? ->
query:RandomCutForestQuery.t ->
unit ->
tval to_value :
t ->
[> `Structure of
(string
* [> `Integer of RandomCutForestConfigurationShingleSizeInteger.t
| `String of RandomCutForestQuery.t
| `Structure of
(string * [> `Double of IgnoreNearExpectedAmountDouble.t ]) list ])
list ]