Uses of Class
com.azure.ai.anomalydetector.models.UnivariateChangePointDetectionOptions

Packages that use UnivariateChangePointDetectionOptions

Package	Description
com.azure.ai.anomalydetector	Package containing the classes for AnomalyDetector.
com.azure.ai.anomalydetector.models	Package containing the data models for AnomalyDetector.

Uses of UnivariateChangePointDetectionOptions in com.azure.ai.anomalydetector

Methods in com.azure.ai.anomalydetector with parameters of type UnivariateChangePointDetectionOptions

Modifier and Type	Method	Description
Mono<UnivariateChangePointDetectionResult>	AnomalyDetectorAsyncClient.detectUnivariateChangePoint(UnivariateChangePointDetectionOptions options)	Detect change point for the entire series
UnivariateChangePointDetectionResult	AnomalyDetectorClient.detectUnivariateChangePoint(UnivariateChangePointDetectionOptions options)	Detect change point for the entire series

Uses of UnivariateChangePointDetectionOptions in com.azure.ai.anomalydetector.models

Methods in com.azure.ai.anomalydetector.models that return UnivariateChangePointDetectionOptions

Modifier and Type	Method	Description
UnivariateChangePointDetectionOptions	UnivariateChangePointDetectionOptions.setCustomInterval(Integer customInterval)	Set the customInterval property: 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}.
UnivariateChangePointDetectionOptions	UnivariateChangePointDetectionOptions.setPeriod(Integer period)	Set the period property: Optional argument, periodic value of a time series.
UnivariateChangePointDetectionOptions	UnivariateChangePointDetectionOptions.setStableTrendWindow(Integer stableTrendWindow)	Set the stableTrendWindow property: Optional argument, advanced model parameter, a default stableTrendWindow will be used in detection.
UnivariateChangePointDetectionOptions	UnivariateChangePointDetectionOptions.setThreshold(Double threshold)	Set the threshold property: 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.