AnomalyDetectorClientOperationsMixin Class

Inheritance

builtins.object

AnomalyDetectorClientOperationsMixin

Constructor

AnomalyDetectorClientOperationsMixin()

Methods

change_point_detect	Detect change point for the entire series. Evaluate change point score of every series point.
entire_detect	Detect anomalies for the entire series in batch. This operation generates a model using 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.
last_detect	Detect anomaly status of the latest point in time series. 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.

change_point_detect

Detect change point for the entire series.

Evaluate change point score of every series point.

change_point_detect(body, custom_headers=None, raw=False, **operation_config)

Parameters

body

ChangePointDetectRequest

Required

Time series points and granularity is needed. Advanced model parameters can also be set in the request if needed.

custom_headers

dict

default value: None

headers that will be added to the request

raw

bool

default value: False

returns the direct response alongside the deserialized response

operation_config

Required

Operation configuration overrides.

Returns

ChangePointDetectResponse or ClientRawResponse if raw=true

Return type

ChangePointDetectResponse,

ClientRawResponse

Exceptions

APIErrorException

entire_detect

Detect anomalies for the entire series in batch.

This operation generates a model using 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.

entire_detect(body, custom_headers=None, raw=False, **operation_config)

Parameters

body

Request

Required

Time series points and period if needed. Advanced model parameters can also be set in the request.

custom_headers

dict

default value: None

headers that will be added to the request

raw

bool

default value: False

returns the direct response alongside the deserialized response

operation_config

Required

Operation configuration overrides.

Returns

EntireDetectResponse or ClientRawResponse if raw=true

Return type

EntireDetectResponse,

ClientRawResponse

Exceptions

APIErrorException

last_detect

Detect anomaly status of the latest point in time series.

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.

last_detect(body, custom_headers=None, raw=False, **operation_config)

Parameters

body

Request

Required

Time series points and period if needed. Advanced model parameters can also be set in the request.

custom_headers

dict

default value: None

headers that will be added to the request

raw

bool

default value: False

returns the direct response alongside the deserialized response

operation_config

Required

Operation configuration overrides.

Returns

LastDetectResponse or ClientRawResponse if raw=true

Return type

LastDetectResponse,

ClientRawResponse

Exceptions

APIErrorException