Microsoft.Azure.CognitiveServices.AnomalyDetector 0.8.0-preview

The ID prefix of this package has been reserved for one of the owners of this package by 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There is a newer version of this package available.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.
dotnet add package Microsoft.Azure.CognitiveServices.AnomalyDetector --version 0.8.0-preview
NuGet\Install-Package Microsoft.Azure.CognitiveServices.AnomalyDetector -Version 0.8.0-preview
This command is intended to be used within the Package Manager Console in Visual Studio, as it uses the NuGet module's version of Install-Package.
<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
#r "nuget: Microsoft.Azure.CognitiveServices.AnomalyDetector, 0.8.0-preview"
#r directive can be used in F# Interactive and Polyglot Notebooks. 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

Package Description

Product Compatible and additional computed target framework versions.
.NET net5.0 was computed.  net5.0-windows was computed.  net6.0 was computed.  net6.0-android was computed.  net6.0-ios was computed.  net6.0-maccatalyst was computed.  net6.0-macos was computed.  net6.0-tvos was computed.  net6.0-windows was computed.  net7.0 was computed.  net7.0-android was computed.  net7.0-ios was computed.  net7.0-maccatalyst was computed.  net7.0-macos was computed.  net7.0-tvos was computed.  net7.0-windows was computed.  net8.0 was computed.  net8.0-android was computed.  net8.0-browser was computed.  net8.0-ios was computed.  net8.0-maccatalyst was computed.  net8.0-macos was computed.  net8.0-tvos was computed.  net8.0-windows was computed. 
.NET Core netcoreapp1.0 was computed.  netcoreapp1.1 was computed.  netcoreapp2.0 was computed.  netcoreapp2.1 was computed.  netcoreapp2.2 was computed.  netcoreapp3.0 was computed.  netcoreapp3.1 was computed. 
.NET Standard netstandard1.4 is compatible.  netstandard1.5 was computed.  netstandard1.6 was computed.  netstandard2.0 is compatible.  netstandard2.1 was computed. 
.NET Framework net452 is compatible.  net46 was computed.  net461 is compatible.  net462 was computed.  net463 was computed.  net47 was computed.  net471 was computed.  net472 was computed.  net48 was computed.  net481 was computed. 
MonoAndroid monoandroid was computed. 
MonoMac monomac was computed. 
MonoTouch monotouch was computed. 
Tizen tizen30 was computed.  tizen40 was computed.  tizen60 was computed. 
Universal Windows Platform uap was computed.  uap10.0 was computed. 
Xamarin.iOS xamarinios was computed. 
Xamarin.Mac xamarinmac was computed. 
Xamarin.TVOS xamarintvos was computed. 
Xamarin.WatchOS xamarinwatchos was computed. 
Compatible target framework(s)
Included target framework(s) (in package)
Learn more about Target Frameworks and .NET Standard.

NuGet packages

This package is not used by any NuGet packages.

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Learn how to use the Cognitive Services SDKs with these samples
Version Downloads Last updated
1.0.0 116,378 8/13/2020
1.0.0-preview.1 322 7/7/2020
0.8.0-preview 11,021 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.