Anomaly Detector Client (preview:1.1)

2025/10/21 • 9 deleted methods

DetectEntireSeries (removed)
Description :  This operation generates a model with an entire series, each point is detected with the same model. With this method, points before and after a certain point are used to determine whether it is an anomaly. The entire detection can give user an overall status of the time series.
Reference :  Link ¶

⚼ Request

POST:  /timeseries/entire/detect
{
body:
{
Description: Time series points and period if needed. Advanced model parameters can also be set in the request. ,
Required: True ,
}
{
series:
{
Description: Time series data points. Points should be sorted by timestamp in ascending order to match the anomaly detection result. If the data is not sorted correctly or there is duplicated timestamp, the API will not work. In such case, an error message will be returned. ,
Required: True ,
}
[
{
timestamp:
{
Description: Optional argument, timestamp of a data point (ISO8601 format). ,
Format: date-time ,
Required: False ,
}
string ,
value:
{
Description: The measurement of that point, should be float. ,
Format: float ,
Required: True ,
}
number ,
}
,
]
,
granularity:
{
Description: Optional argument, can be one of yearly, monthly, weekly, daily, hourly, minutely, secondly, microsecond or none. If granularity is not present, it will be none by default. If granularity is none, the timestamp property in time series point can be absent. ,
Enum:
{
yearly: No description available. ,
monthly: No description available. ,
weekly: No description available. ,
daily: No description available. ,
hourly: No description available. ,
minutely: No description available. ,
secondly: No description available. ,
microsecond: No description available. ,
none: No description available. ,
}
,
Required: False ,
}
enum ,
customInterval:
{
Description: Custom Interval is used to set non-standard time interval, for example, if the series is 5 minutes, request can be set as {"granularity":"minutely", "customInterval":5}. ,
Format: int32 ,
Required: False ,
}
integer ,
period:
{
Description: Optional argument, periodic value of a time series. If the value is null or does not present, the API will determine the period automatically. ,
Format: int32 ,
Required: False ,
}
integer ,
maxAnomalyRatio:
{
Description: Optional argument, advanced model parameter, max anomaly ratio in a time series. ,
Format: float ,
Required: False ,
}
number ,
sensitivity:
{
Description: Optional argument, advanced model parameter, between 0-99, the lower the value is, the larger the margin value will be which means less anomalies will be accepted. ,
Format: int32 ,
Required: False ,
}
integer ,
}
,
}

⚐ Response (200)

{
period:
{
Description: Frequency extracted from the series, zero means no recurrent pattern has been found. ,
Format: int32 ,
Required: True ,
}
integer ,
expectedValues:
{
Description: ExpectedValues contain expected value for each input point. The index of the array is consistent with the input series. ,
Required: True ,
}
[
number ,
]
,
upperMargins:
{
Description: UpperMargins contain upper margin of each input point. UpperMargin is used to calculate upperBoundary, which equals to expectedValue + (100 - marginScale)*upperMargin. Anomalies in response can be filtered by upperBoundary and lowerBoundary. By adjusting marginScale value, less significant anomalies can be filtered in client side. The index of the array is consistent with the input series. ,
Required: True ,
}
[
number ,
]
,
lowerMargins:
{
Description: LowerMargins contain lower margin of each input point. LowerMargin is used to calculate lowerBoundary, which equals to expectedValue - (100 - marginScale)*lowerMargin. Points between the boundary can be marked as normal ones in client side. The index of the array is consistent with the input series. ,
Required: True ,
}
[
number ,
]
,
isAnomaly:
{
Description: IsAnomaly contains anomaly properties for each input point. True means an anomaly either negative or positive has been detected. The index of the array is consistent with the input series. ,
Required: True ,
}
[
boolean ,
]
,
isNegativeAnomaly:
{
Description: IsNegativeAnomaly contains anomaly status in negative direction for each input point. True means a negative anomaly has been detected. A negative anomaly means the point is detected as an anomaly and its real value is smaller than the expected one. The index of the array is consistent with the input series. ,
Required: True ,
}
[
boolean ,
]
,
isPositiveAnomaly:
{
Description: IsPositiveAnomaly contain anomaly status in positive direction for each input point. True means a positive anomaly has been detected. A positive anomaly means the point is detected as an anomaly and its real value is larger than the expected one. The index of the array is consistent with the input series. ,
Required: True ,
}
[
boolean ,
]
,
}

⚐ Response (default)

{
code:
{
Description: The error code. ,
Enum:
{
InvalidCustomInterval: No description available. ,
BadArgument: No description available. ,
InvalidGranularity: No description available. ,
InvalidPeriod: No description available. ,
InvalidModelArgument: No description available. ,
InvalidSeries: No description available. ,
InvalidJsonFormat: No description available. ,
RequiredGranularity: No description available. ,
RequiredSeries: No description available. ,
}
,
Required: False ,
}
enum ,
message:
{
Description: A message explaining the error reported by the service. ,
Required: False ,
}
string ,
}
DetectLastPoint (removed)
Description :  This operation generates a model using points before the latest one. With this method, only historical points are used to determine whether the target point is an anomaly. The latest point detecting operation matches the scenario of real-time monitoring of business metrics.
Reference :  Link ¶

⚼ Request

POST:  /timeseries/last/detect
{
body:
{
Description: Time series points and period if needed. Advanced model parameters can also be set in the request. ,
Required: True ,
}
{
series:
{
Description: Time series data points. Points should be sorted by timestamp in ascending order to match the anomaly detection result. If the data is not sorted correctly or there is duplicated timestamp, the API will not work. In such case, an error message will be returned. ,
Required: True ,
}
[
{
timestamp:
{
Description: Optional argument, timestamp of a data point (ISO8601 format). ,
Format: date-time ,
Required: False ,
}
string ,
value:
{
Description: The measurement of that point, should be float. ,
Format: float ,
Required: True ,
}
number ,
}
,
]
,
granularity:
{
Description: Optional argument, can be one of yearly, monthly, weekly, daily, hourly, minutely, secondly, microsecond or none. If granularity is not present, it will be none by default. If granularity is none, the timestamp property in time series point can be absent. ,
Enum:
{
yearly: No description available. ,
monthly: No description available. ,
weekly: No description available. ,
daily: No description available. ,
hourly: No description available. ,
minutely: No description available. ,
secondly: No description available. ,
microsecond: No description available. ,
none: No description available. ,
}
,
Required: False ,
}
enum ,
customInterval:
{
Description: Custom Interval is used to set non-standard time interval, for example, if the series is 5 minutes, request can be set as {"granularity":"minutely", "customInterval":5}. ,
Format: int32 ,
Required: False ,
}
integer ,
period:
{
Description: Optional argument, periodic value of a time series. If the value is null or does not present, the API will determine the period automatically. ,
Format: int32 ,
Required: False ,
}
integer ,
maxAnomalyRatio:
{
Description: Optional argument, advanced model parameter, max anomaly ratio in a time series. ,
Format: float ,
Required: False ,
}
number ,
sensitivity:
{
Description: Optional argument, advanced model parameter, between 0-99, the lower the value is, the larger the margin value will be which means less anomalies will be accepted. ,
Format: int32 ,
Required: False ,
}
integer ,
}
,
}

⚐ Response (200)

{
period:
{
Description: Frequency extracted from the series, zero means no recurrent pattern has been found. ,
Format: int32 ,
Required: True ,
}
integer ,
suggestedWindow:
{
Description: Suggested input series points needed for detecting the latest point. ,
Format: int32 ,
Required: True ,
}
integer ,
expectedValue:
{
Description: Expected value of the latest point. ,
Format: float ,
Required: True ,
}
number ,
upperMargin:
{
Description: Upper margin of the latest point. UpperMargin is used to calculate upperBoundary, which equals to expectedValue + (100 - marginScale)*upperMargin. If the value of latest point is between upperBoundary and lowerBoundary, it should be treated as normal value. By adjusting marginScale value, anomaly status of latest point can be changed. ,
Format: float ,
Required: True ,
}
number ,
lowerMargin:
{
Description: Lower margin of the latest point. LowerMargin is used to calculate lowerBoundary, which equals to expectedValue - (100 - marginScale)*lowerMargin. ,
Format: float ,
Required: True ,
}
number ,
isAnomaly:
{
Description: Anomaly status of the latest point, true means the latest point is an anomaly either in negative direction or positive direction. ,
Required: True ,
}
boolean ,
isNegativeAnomaly:
{
Description: Anomaly status in negative direction of the latest point. True means the latest point is an anomaly and its real value is smaller than the expected one. ,
Required: True ,
}
boolean ,
isPositiveAnomaly:
{
Description: Anomaly status in positive direction of the latest point. True means the latest point is an anomaly and its real value is larger than the expected one. ,
Required: True ,
}
boolean ,
}

⚐ Response (default)

{
code:
{
Description: The error code. ,
Enum:
{
InvalidCustomInterval: No description available. ,
BadArgument: No description available. ,
InvalidGranularity: No description available. ,
InvalidPeriod: No description available. ,
InvalidModelArgument: No description available. ,
InvalidSeries: No description available. ,
InvalidJsonFormat: No description available. ,
RequiredGranularity: No description available. ,
RequiredSeries: No description available. ,
}
,
Required: False ,
}
enum ,
message:
{
Description: A message explaining the error reported by the service. ,
Required: False ,
}
string ,
}
DetectChangePoint (removed)
Description :  Evaluate change point score of every series point
Reference :  Link ¶

⚼ Request

POST:  /timeseries/changepoint/detect
{
body:
{
Description: Time series points and granularity is needed. Advanced model parameters can also be set in the request if needed. ,
Required: True ,
}
{
series:
{
Description: Time series data points. Points should be sorted by timestamp in ascending order to match the change point detection result. ,
Required: True ,
}
[
{
timestamp:
{
Description: Optional argument, timestamp of a data point (ISO8601 format). ,
Format: date-time ,
Required: False ,
}
string ,
value:
{
Description: The measurement of that point, should be float. ,
Format: float ,
Required: True ,
}
number ,
}
,
]
,
granularity:
{
Description: Can only be one of yearly, monthly, weekly, daily, hourly, minutely or secondly. Granularity is used for verify whether input series is valid. ,
Enum:
{
yearly: No description available. ,
monthly: No description available. ,
weekly: No description available. ,
daily: No description available. ,
hourly: No description available. ,
minutely: No description available. ,
secondly: No description available. ,
microsecond: No description available. ,
none: No description available. ,
}
,
Required: True ,
}
enum ,
customInterval:
{
Description: Custom Interval is used to set non-standard time interval, for example, if the series is 5 minutes, request can be set as {"granularity":"minutely", "customInterval":5}. ,
Format: int32 ,
Required: False ,
}
integer ,
period:
{
Description: Optional argument, periodic value of a time series. If the value is null or does not present, the API will determine the period automatically. ,
Format: int32 ,
Required: False ,
}
integer ,
stableTrendWindow:
{
Description: Optional argument, advanced model parameter, a default stableTrendWindow will be used in detection. ,
Format: int32 ,
Required: False ,
}
integer ,
threshold:
{
Description: Optional argument, advanced model parameter, between 0.0-1.0, the lower the value is, the larger the trend error will be which means less change point will be accepted. ,
Format: float ,
Required: False ,
}
number ,
}
,
}

⚐ Response (200)

{
period:
{
Description: Frequency extracted from the series, zero means no recurrent pattern has been found. ,
Format: int32 ,
Required: False ,
}
integer ,
isChangePoint:
{
Description: isChangePoint contains change point properties for each input point. True means an anomaly either negative or positive has been detected. The index of the array is consistent with the input series. ,
Required: False ,
}
[
boolean ,
]
,
confidenceScores:
{
Description: the change point confidence of each point ,
Required: False ,
}
[
number ,
]
,
}

⚐ Response (default)

{
code:
{
Description: The error code. ,
Enum:
{
InvalidCustomInterval: No description available. ,
BadArgument: No description available. ,
InvalidGranularity: No description available. ,
InvalidPeriod: No description available. ,
InvalidModelArgument: No description available. ,
InvalidSeries: No description available. ,
InvalidJsonFormat: No description available. ,
RequiredGranularity: No description available. ,
RequiredSeries: No description available. ,
}
,
Required: False ,
}
enum ,
message:
{
Description: A message explaining the error reported by the service. ,
Required: False ,
}
string ,
}
TrainMultivariateModel (removed)
Description :  Create and train a multivariate anomaly detection model. The request must include a source parameter to indicate an externally accessible Azure storage Uri (preferably a Shared Access Signature Uri). All time-series used in generate the model must be zipped into one single file. Each time-series will be in a single CSV file in which the first column is timestamp and the second column is value.
Reference :  Link ¶

⚼ Request

POST:  /multivariate/models
{
modelRequest:
{
Description: Training request ,
Required: True ,
}
{
slidingWindow:
{
Description: An optional field, indicates how many history points will be used to determine the anomaly score of one subsequent point. ,
Required: False ,
}
integer ,
alignPolicy:
{
Description: An optional field, since those multivariate need to be aligned in the same timestamp before starting the detection. ,
Required: False ,
}
{
alignMode:
{
Description: An optional field, indicates how we align different variables into the same time-range which is required by the model.{Inner, Outer} ,
Enum:
{
Inner: No description available. ,
Outer: No description available. ,
}
,
Required: False ,
}
enum ,
fillNAMethod:
{
Description: An optional field, indicates how missed values will be filled with. Can not be set to NotFill, when alignMode is Outer.{Previous, Subsequent, Linear, Zero, Fix, NotFill} ,
Enum:
{
Previous: No description available. ,
Subsequent: No description available. ,
Linear: No description available. ,
Zero: No description available. ,
Pad: No description available. ,
NotFill: No description available. ,
}
,
Required: False ,
}
enum ,
paddingValue:
{
Description: optional field, only be useful if FillNAMethod is set to Pad. ,
Required: False ,
}
integer ,
}
,
source:
{
Description: source file link of the input variables, each variable will be a csv with two columns, the first column will be timestamp, the second column will be value.Besides these variable csv files, an extra meta.json can be included in th zip file if you would like to rename a variable.Be default, the file name of the variable will be used as the variable name. ,
Required: True ,
}
string ,
startTime:
{
Description: require field, start time of data be used for generating multivariate anomaly detection model, should be data-time ,
Format: date-time ,
Required: True ,
}
string ,
endTime:
{
Description: require field, end time of data be used for generating multivariate anomaly detection model, should be data-time ,
Format: date-time ,
Required: True ,
}
string ,
displayName:
{
Description: optional field, name of the model ,
Required: False ,
}
string ,
status:
{
Description: Model training status. ,
Enum:
{
CREATED: No description available. ,
RUNNING: No description available. ,
READY: No description available. ,
FAILED: No description available. ,
}
,
Required: False ,
}
enum ,
errors:
{
Description: Error message when fails to create a model. ,
Required: False ,
}
[
{
code:
{
Description: The error Code ,
Required: True ,
}
string ,
message:
{
Description: A message explaining the error reported by the service. ,
Required: True ,
}
string ,
}
,
]
,
diagnosticsInfo:
{
Description: Used for deep analysis model and variables ,
Required: False ,
}
{
modelState:
{
Required: False ,
}
{
epochIds:
{
Description: Epoch id ,
Required: False ,
}
[
integer ,
]
,
trainLosses:
{
Required: False ,
}
[
number ,
]
,
validationLosses:
{
Required: False ,
}
[
number ,
]
,
latenciesInSeconds:
{
Required: False ,
}
[
number ,
]
,
}
,
variableStates:
{
Required: False ,
}
[
{
variable:
{
Description: Variable name. ,
Required: False ,
}
string ,
filledNARatio:
{
Description: Merged NA ratio of a variable. ,
Required: False ,
}
number ,
effectiveCount:
{
Description: Effective time-series points count. ,
Required: False ,
}
integer ,
startTime:
{
Description: Start time of a variable ,
Format: date-time ,
Required: False ,
}
string ,
endTime:
{
Description: End time of a variable ,
Format: date-time ,
Required: False ,
}
string ,
errors:
{
Description: Error message when parse variable ,
Required: False ,
}
[
{
code:
{
Description: The error Code ,
Required: True ,
}
string ,
message:
{
Description: A message explaining the error reported by the service. ,
Required: True ,
}
string ,
}
,
]
,
}
,
]
,
}
,
}
,
}

⚐ Response (201)

{
location:
{
Description: Location and ID of the model being saved. ,
}
string ,
}

⚐ Response (default)

{
code:
{
Description: The error Code ,
Required: True ,
}
string ,
message:
{
Description: A message explaining the error reported by the service. ,
Required: True ,
}
string ,
}
GetMultivariateModel (removed)
Description :  Get detailed information of multivariate model, including the training status and variables used in the model.
Reference :  Link ¶

⚼ Request

GET:  /multivariate/models/{modelId}
{}

⚐ Response (200)

{
modelId:
{
Description: Model identifier. ,
Format: uuid ,
Required: True ,
}
string ,
createdTime:
{
Description: Date and time (UTC) when the model was created. ,
Format: date-time ,
Required: True ,
}
string ,
lastUpdatedTime:
{
Description: Date and time (UTC) when the model was last updated. ,
Format: date-time ,
Required: True ,
}
string ,
modelInfo:
{
Description: Training Status of the model. ,
Required: False ,
}
{
slidingWindow:
{
Description: An optional field, indicates how many history points will be used to determine the anomaly score of one subsequent point. ,
Required: False ,
}
integer ,
alignPolicy:
{
Description: An optional field, since those multivariate need to be aligned in the same timestamp before starting the detection. ,
Required: False ,
}
{
alignMode:
{
Description: An optional field, indicates how we align different variables into the same time-range which is required by the model.{Inner, Outer} ,
Enum:
{
Inner: No description available. ,
Outer: No description available. ,
}
,
Required: False ,
}
enum ,
fillNAMethod:
{
Description: An optional field, indicates how missed values will be filled with. Can not be set to NotFill, when alignMode is Outer.{Previous, Subsequent, Linear, Zero, Fix, NotFill} ,
Enum:
{
Previous: No description available. ,
Subsequent: No description available. ,
Linear: No description available. ,
Zero: No description available. ,
Pad: No description available. ,
NotFill: No description available. ,
}
,
Required: False ,
}
enum ,
paddingValue:
{
Description: optional field, only be useful if FillNAMethod is set to Pad. ,
Required: False ,
}
integer ,
}
,
source:
{
Description: source file link of the input variables, each variable will be a csv with two columns, the first column will be timestamp, the second column will be value.Besides these variable csv files, an extra meta.json can be included in th zip file if you would like to rename a variable.Be default, the file name of the variable will be used as the variable name. ,
Required: True ,
}
string ,
startTime:
{
Description: require field, start time of data be used for generating multivariate anomaly detection model, should be data-time ,
Format: date-time ,
Required: True ,
}
string ,
endTime:
{
Description: require field, end time of data be used for generating multivariate anomaly detection model, should be data-time ,
Format: date-time ,
Required: True ,
}
string ,
displayName:
{
Description: optional field, name of the model ,
Required: False ,
}
string ,
status:
{
Description: Model training status. ,
Enum:
{
CREATED: No description available. ,
RUNNING: No description available. ,
READY: No description available. ,
FAILED: No description available. ,
}
,
Required: False ,
}
enum ,
errors:
{
Description: Error message when fails to create a model. ,
Required: False ,
}
[
{
code:
{
Description: The error Code ,
Required: True ,
}
string ,
message:
{
Description: A message explaining the error reported by the service. ,
Required: True ,
}
string ,
}
,
]
,
diagnosticsInfo:
{
Description: Used for deep analysis model and variables ,
Required: False ,
}
{
modelState:
{
Required: False ,
}
{
epochIds:
{
Description: Epoch id ,
Required: False ,
}
[
integer ,
]
,
trainLosses:
{
Required: False ,
}
[
number ,
]
,
validationLosses:
{
Required: False ,
}
[
number ,
]
,
latenciesInSeconds:
{
Required: False ,
}
[
number ,
]
,
}
,
variableStates:
{
Required: False ,
}
[
{
variable:
{
Description: Variable name. ,
Required: False ,
}
string ,
filledNARatio:
{
Description: Merged NA ratio of a variable. ,
Required: False ,
}
number ,
effectiveCount:
{
Description: Effective time-series points count. ,
Required: False ,
}
integer ,
startTime:
{
Description: Start time of a variable ,
Format: date-time ,
Required: False ,
}
string ,
endTime:
{
Description: End time of a variable ,
Format: date-time ,
Required: False ,
}
string ,
errors:
{
Description: Error message when parse variable ,
Required: False ,
}
[
{
code:
{
Description: The error Code ,
Required: True ,
}
string ,
message:
{
Description: A message explaining the error reported by the service. ,
Required: True ,
}
string ,
}
,
]
,
}
,
]
,
}
,
}
,
}

⚐ Response (default)

{
code:
{
Description: The error Code ,
Required: True ,
}
string ,
message:
{
Description: A message explaining the error reported by the service. ,
Required: True ,
}
string ,
}
DeleteMultivariateModel (removed)
Description :  Delete an existing multivariate model according to the modelId
Reference :  Link ¶

⚼ Request

DELETE:  /multivariate/models/{modelId}
{}

⚐ Response (204)

{}

⚐ Response (default)

{
code:
{
Description: The error Code ,
Required: True ,
}
string ,
message:
{
Description: A message explaining the error reported by the service. ,
Required: True ,
}
string ,
}
DetectAnomaly (removed)
Description :  Submit detection multivariate anomaly task with the trained model of modelId, the input schema should be the same with the training request. Thus request will be complete asynchronously and will return a resultId for querying the detection result.The request should be a source link to indicate an externally accessible Azure storage Uri (preferably a Shared Access Signature Uri). All time-series used in generate the model must be zipped into one single file. Each time-series will be as follows: the first column is timestamp and the second column is value.
Reference :  Link ¶

⚼ Request

POST:  /multivariate/models/{modelId}/detect
{
detectionRequest:
{
Description: Detect anomaly request ,
Required: True ,
}
{
source:
{
Description: source file link of the input variables, each variable will be a csv with two columns, the first column will be timestamp, the second column will be value.Besides these variable csv files, a extra meta.json can be included in th zip file if you would like to rename a variable.Be default, the file name of the variable will be used as the variable name. The variables used in detection should be consistent with variables in the model used for detection. ,
Required: True ,
}
string ,
startTime:
{
Description: A require field, start time of data be used for detection, should be date-time. ,
Format: date-time ,
Required: True ,
}
string ,
endTime:
{
Description: A require field, end time of data be used for detection, should be date-time. ,
Format: date-time ,
Required: True ,
}
string ,
}
,
}

⚐ Response (201)

{
location:
{
Description: Location and ID of the detection result being saved. ,
}
string ,
}

⚐ Response (default)

{
code:
{
Description: The error Code ,
Required: True ,
}
string ,
message:
{
Description: A message explaining the error reported by the service. ,
Required: True ,
}
string ,
}
GetDetectionResult (removed)
Description :  Get multivariate anomaly detection result based on resultId returned by the DetectAnomalyAsync api
Reference :  Link ¶

⚼ Request

GET:  /multivariate/results/{resultId}
{}

⚐ Response (200)

{
resultId:
{
Format: uuid ,
Required: True ,
}
string ,
summary:
{
Description: Multivariate anomaly detection status ,
Required: True ,
}
{
status:
{
Description: Multivariate anomaly detection status ,
Enum:
{
CREATED: No description available. ,
RUNNING: No description available. ,
READY: No description available. ,
FAILED: No description available. ,
}
,
Required: True ,
}
enum ,
errors:
{
Description: Error message when creating or training model fails. ,
Required: False ,
}
[
{
code:
{
Description: The error Code ,
Required: True ,
}
string ,
message:
{
Description: A message explaining the error reported by the service. ,
Required: True ,
}
string ,
}
,
]
,
variableStates:
{
Required: False ,
}
[
{
variable:
{
Description: Variable name. ,
Required: False ,
}
string ,
filledNARatio:
{
Description: Merged NA ratio of a variable. ,
Required: False ,
}
number ,
effectiveCount:
{
Description: Effective time-series points count. ,
Required: False ,
}
integer ,
startTime:
{
Description: Start time of a variable ,
Format: date-time ,
Required: False ,
}
string ,
endTime:
{
Description: End time of a variable ,
Format: date-time ,
Required: False ,
}
string ,
errors:
{
Description: Error message when parse variable ,
Required: False ,
}
[
{
code:
{
Description: The error Code ,
Required: True ,
}
string ,
message:
{
Description: A message explaining the error reported by the service. ,
Required: True ,
}
string ,
}
,
]
,
}
,
]
,
setupInfo:
{
Description: Request when creating the model. ,
Required: True ,
}
{
source:
{
Description: source file link of the input variables, each variable will be a csv with two columns, the first column will be timestamp, the second column will be value.Besides these variable csv files, a extra meta.json can be included in th zip file if you would like to rename a variable.Be default, the file name of the variable will be used as the variable name. The variables used in detection should be consistent with variables in the model used for detection. ,
Required: True ,
}
string ,
startTime:
{
Description: A require field, start time of data be used for detection, should be date-time. ,
Format: date-time ,
Required: True ,
}
string ,
endTime:
{
Description: A require field, end time of data be used for detection, should be date-time. ,
Format: date-time ,
Required: True ,
}
string ,
}
,
}
,
results:
{
Description: anomaly status of each timestamp ,
Required: True ,
}
[
{
timestamp:
{
Description: timestamp ,
Format: date-time ,
Required: True ,
}
string ,
value:
{
Required: False ,
}
{
contributors:
{
Description: If current timestamp is an anomaly, contributors will show potential root cause for thus anomaly. Contributors can help us understand why current timestamp has been detected as an anomaly. ,
Required: False ,
}
[
{
contributionScore:
{
Description: The higher the contribution score is, the more likely the variable to be the root cause of a anomaly. ,
Required: False ,
}
number ,
variable:
{
Description: Variable name of a contributor ,
Required: False ,
}
string ,
}
,
]
,
isAnomaly:
{
Description: To indicate whether current timestamp is anomaly or not ,
Required: True ,
}
boolean ,
severity:
{
Description: anomaly score of the current timestamp, the more significant an anomaly is, the higher the score will be ,
Required: True ,
}
number ,
score:
{
Description: anomaly score of the current timestamp, the more significant an anomaly is, the higher the score will be, score measures global significance ,
Required: False ,
}
number ,
}
,
errors:
{
Description: Error message when inference this timestamp ,
Required: False ,
}
[
{
code:
{
Description: The error Code ,
Required: True ,
}
string ,
message:
{
Description: A message explaining the error reported by the service. ,
Required: True ,
}
string ,
}
,
]
,
}
,
]
,
}

⚐ Response (default)

{
code:
{
Description: The error Code ,
Required: True ,
}
string ,
message:
{
Description: A message explaining the error reported by the service. ,
Required: True ,
}
string ,
}
ExportModel (removed)
Description :  Export multivariate anomaly detection model based on modelId
Reference :  Link ¶

⚼ Request

GET:  /multivariate/models/{modelId}/export
{}

⚐ Response (200)

{
$headers:
{
content-type:
{
Description: application/zip ,
}
string ,
}
,
$schema:
{
Format: file ,
}
object ,
}

⚐ Response (default)

{
code:
{
Description: The error Code ,
Required: True ,
}
string ,
message:
{
Description: A message explaining the error reported by the service. ,
Required: True ,
}
string ,
}