Microsoft.Azure.CognitiveServices.AnomalyDetector 0.8.0-preview Prefix Reserved

This is a prerelease version of Microsoft.Azure.CognitiveServices.AnomalyDetector.
There is a newer version of this package available.
See the version list below for details.
Install-Package Microsoft.Azure.CognitiveServices.AnomalyDetector -Version 0.8.0-preview
dotnet add package Microsoft.Azure.CognitiveServices.AnomalyDetector --version 0.8.0-preview
<PackageReference Include="Microsoft.Azure.CognitiveServices.AnomalyDetector" Version="0.8.0-preview" />
For projects that support PackageReference, copy this XML node into the project file to reference the package.
paket add Microsoft.Azure.CognitiveServices.AnomalyDetector --version 0.8.0-preview
The NuGet Team does not provide support for this client. Please contact its maintainers for support.
#r "nuget: Microsoft.Azure.CognitiveServices.AnomalyDetector, 0.8.0-preview"
#r directive can be used in F# Interactive, C# scripting and .NET Interactive. Copy this into the interactive tool or source code of the script to reference the package.
// Install Microsoft.Azure.CognitiveServices.AnomalyDetector as a Cake Addin
#addin nuget:?package=Microsoft.Azure.CognitiveServices.AnomalyDetector&version=0.8.0-preview&prerelease

// Install Microsoft.Azure.CognitiveServices.AnomalyDetector as a Cake Tool
#tool nuget:?package=Microsoft.Azure.CognitiveServices.AnomalyDetector&version=0.8.0-preview&prerelease
The NuGet Team does not provide support for this client. Please contact its maintainers for support.

Package Description

NuGet packages

This package is not used by any NuGet packages.

GitHub repositories

This package is not used by any popular GitHub repositories.

Version Downloads Last updated
1.0.0 37,601 8/13/2020
1.0.0-preview.1 202 7/7/2020
0.8.0-preview 9,553 3/16/2019

The Cognitive Service Anomaly Detector Client SDK helps users detect anomalies automatically in time series data. It supports two functionalities:
   1) 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 the user an overall status of the time series.
   2) 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.