/AWS1/CL_AMPRANDOMCUTFORESTC00¶
Configuration for the Random Cut Forest algorithm used for anomaly detection in time-series data.
CONSTRUCTOR¶
IMPORTING¶
Required arguments:¶
iv_query TYPE /AWS1/AMPRANDOMCUTFORESTQUERY /AWS1/AMPRANDOMCUTFORESTQUERY¶
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, andsum.
Optional arguments:¶
iv_shinglesize TYPE /AWS1/AMPINTEGER /AWS1/AMPINTEGER¶
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.
iv_samplesize TYPE /AWS1/AMPINTEGER /AWS1/AMPINTEGER¶
The number of data points sampled from the input stream for the Random Cut Forest algorithm. The default number is 256 consecutive data points.
io_ignorenearexpectedfrmab00 TYPE REF TO /AWS1/CL_AMPIGNORENEAREXPECTED /AWS1/CL_AMPIGNORENEAREXPECTED¶
Configuration for ignoring values that are near expected values from above during anomaly detection.
io_ignorenearexpectedfrmbe00 TYPE REF TO /AWS1/CL_AMPIGNORENEAREXPECTED /AWS1/CL_AMPIGNORENEAREXPECTED¶
Configuration for ignoring values that are near expected values from below during anomaly detection.
Queryable Attributes¶
query¶
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, andsum.
Accessible with the following methods¶
| Method | Description |
|---|---|
GET_QUERY() |
Getter for QUERY, with configurable default |
ASK_QUERY() |
Getter for QUERY w/ exceptions if field has no value |
HAS_QUERY() |
Determine if QUERY has a value |
shingleSize¶
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.
Accessible with the following methods¶
| Method | Description |
|---|---|
GET_SHINGLESIZE() |
Getter for SHINGLESIZE, with configurable default |
ASK_SHINGLESIZE() |
Getter for SHINGLESIZE w/ exceptions if field has no value |
HAS_SHINGLESIZE() |
Determine if SHINGLESIZE has a value |
sampleSize¶
The number of data points sampled from the input stream for the Random Cut Forest algorithm. The default number is 256 consecutive data points.
Accessible with the following methods¶
| Method | Description |
|---|---|
GET_SAMPLESIZE() |
Getter for SAMPLESIZE, with configurable default |
ASK_SAMPLESIZE() |
Getter for SAMPLESIZE w/ exceptions if field has no value |
HAS_SAMPLESIZE() |
Determine if SAMPLESIZE has a value |
ignoreNearExpectedFromAbove¶
Configuration for ignoring values that are near expected values from above during anomaly detection.
Accessible with the following methods¶
| Method | Description |
|---|---|
GET_IGNORENEAREXPECTEDFRMA00() |
Getter for IGNORENEAREXPECTEDFROMABOVE |
ignoreNearExpectedFromBelow¶
Configuration for ignoring values that are near expected values from below during anomaly detection.
Accessible with the following methods¶
| Method | Description |
|---|---|
GET_IGNORENEAREXPECTEDFRMB00() |
Getter for IGNORENEAREXPECTEDFROMBELOW |