Create an Abnormal Profile
post
/api/metric/AbnormalProfiles
Creates an abnormal profile that can be used by abnormal thresholds and the Metric Abnormal Threshold Engine.
Request
There are no request parameters for this operation.
Supported Media Types
- application/json
Root Schema : schema
Type:
Show Source
object
-
AbnormalProfileName: string
Name of abnormal profileExample:
Default Abnormal Profile
-
Alpha: number
The degree to which the confidence bands smooth a weighted average (more weight being given to recent observations, less to older observations). Values are from >0 to 1. The closer to 1 the Alpha value becomes, the closer the predicted value will be to the weighted average of the last n observations.Example:
0.999497
-
Beta: number
The degree to which the smoothing function considers the slope (amount of increase or decrease) of the weighted average of 2 adjacent data points. Values are from >0 to 1. The closer to 1 the Beta value gets, the more the Algorithm will consider the slope of the previous (n-1) data point to the current data point (n) when predicting the next data point (n+1).Example:
0.00224
-
Delta: number
Scaling factor, used to influence the width of the confidence bands generated by the Algorithm. Values are between 2 and 3.Example:
3
-
Epsilon: number
Epsilon scaling. Values are >0 to 1.Example:
0.492391
-
FailureThreshold: integer
Used with Window Length by the Abnormal Thresholding Engine to determine whether to generate a threshold violation event. If Violation Threshold data points fall outside the confidence band within a Window Length number of points, then the data points are considered "abnormal" and an event will be generated.Example:
3
-
Gamma: number
The degree to which the smoothing function considers seasonal data when forecasting a data point. Values are >0 to 1. The closer to 1 Gamma gets, the more the Algorithm will weight seasonal patterns when forecasting expected values.Example:
0.487092
-
WindowLength: integer
Used with Violation Threshold by the Abnormal Thresholding Engine to determine whether to generate a threshold violation event. If Violation Threshold data points fall outside the confidence band within a Window Length number of points, then the data points are considered "abnormal" and an event will be generated. Values are between 0 and 28.Example:
5
Response
Supported Media Types
- application/json
200 Response
Successful operation
Nested Schema : SuccessfulAddOperation
Type:
Show Source
object
-
message: string
The response message.Example:
Added record
-
success: boolean
Whether the operation was a success (true) or a failure (false).Example:
true
Nested Schema : type
Type:
Show Source
object
-
data: array
data
-
total: integer
The total number of results regardless of paging.Example:
1
Nested Schema : metricAbnormalProfilesRead
Type:
Show Source
object
-
AbnormalProfileID: integer
Abnormal Profile IDExample:
1
-
AbnormalProfileName: string
Name of abnormal profileExample:
Default Abnormal Profile
-
Alpha: number
The degree to which the confidence bands smooth a weighted average (more weight being given to recent observations, less to older observations). Values are from >0 to 1. The closer to 1 the Alpha value becomes, the closer the predicted value will be to the weighted average of the last n observations.Example:
0.999497
-
Beta: number
The degree to which the smoothing function considers the slope (amount of increase or decrease) of the weighted average of 2 adjacent data points. Values are from >0 to 1. The closer to 1 the Beta value gets, the more the Algorithm will consider the slope of the previous (n-1) data point to the current data point (n) when predicting the next data point (n+1).Example:
0.00224
-
Delta: number
Scaling factor, used to influence the width of the confidence bands generated by the Algorithm. Values are between 2 and 3.Example:
3
-
Epsilon: number
Epsilon scaling. Values are >0 to 1.Example:
0.492391
-
FailureThreshold: integer
Used with Window Length by the Abnormal Thresholding Engine to determine whether to generate a threshold violation event. If Violation Threshold data points fall outside the confidence band within a Window Length number of points, then the data points are considered "abnormal" and an event will be generated.Example:
3
-
Gamma: number
The degree to which the smoothing function considers seasonal data when forecasting a data point. Values are >0 to 1. The closer to 1 Gamma gets, the more the Algorithm will weight seasonal patterns when forecasting expected values.Example:
0.487092
-
WindowLength: integer
Used with Violation Threshold by the Abnormal Thresholding Engine to determine whether to generate a threshold violation event. If Violation Threshold data points fall outside the confidence band within a Window Length number of points, then the data points are considered "abnormal" and an event will be generated. Values are between 0 and 28.Example:
5
Default Response
Failed operation
Root Schema : schema
Type:
Show Source
object
-
errors: array
errors
The list of errors reported. Validation errors will be keyed by record field.
-
message: string
The response message.Example:
Exception thrown
-
success: boolean
Whether the operation was a success (true) or a failure (false).Example:
false
Nested Schema : errors
Type:
array
The list of errors reported. Validation errors will be keyed by record field.
Show Source
Nested Schema : items
Type:
object