Azure AI Anomalydetector Java

MicrosoftOfficialCreated by Microsoft

Skill
Tool integration
Data and AI
MIT
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From the author

Build anomaly detection applications with Azure AI Anomaly Detector SDK for Java. Use when implementing univariate/multivariate anomaly detection, time-series analysis, or AI-powered monitoring.

— the description by Microsoft

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The Azure AI Anomaly Detector SDK for Java provides a comprehensive interface for building intelligent monitoring systems. It supports both univariate analysis for single-variable time series and multivariate analysis for detecting anomalies across hundreds of correlated signals using Graph Attention Networks. The SDK includes capabilities for batch processing, real-time streaming detection, and change-point identification, with robust support for authentication via Azure Identity.

What it does

Performs univariate batch and streaming anomaly detection on time-series data.
Identifies trend changes using change-point detection algorithms.
Executes multivariate anomaly detection across up to 300 correlated signals.
Manages model training, inference, and lifecycle operations.
Provides flexible authentication options including API keys and managed identities.
Offers interpretation of multivariate anomalies through contributor scoring.

When to use it

When building AI-powered monitoring solutions for time-series data.
When you need to detect anomalies in single-variable datasets.
When analyzing complex systems with multiple correlated data streams.
When implementing real-time anomaly detection for streaming data points.
When you need to identify significant trend shifts in historical data.

Advantages

Native Java support for Azure AI services.
Supports both synchronous and asynchronous client patterns.
Advanced multivariate detection using Graph Attention Networks.
Seamless integration with Azure Identity for secure production deployments.
Detailed contributor analysis for multivariate anomaly interpretation.

Usage examples

“How do I detect anomalies in a single time series using Java?”: The skill provides a UnivariateClient that accepts a list of TimeSeriesPoint objects and returns detection results, including anomaly flags and margins.
“Can I detect anomalies across multiple correlated variables?”: Yes, the MultivariateClient allows you to train models on multiple variables and perform batch or streaming inference to identify anomalies and contributing factors.

Install

Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/microsoft/azure-ai-anomalydetector-java/download

Unzip it into the cross-client skills folder of your project:

mkdir -p .agents/skills && unzip azure-ai-anomalydetector-java.zip -d .agents/skills

Documentation by Microsoft

Azure AI Anomaly Detector SDK for Java

Build anomaly detection applications using the Azure AI Anomaly Detector SDK for Java.

Installation

<dependency>
  <groupId>com.azure</groupId>
  <artifactId>azure-ai-anomalydetector</artifactId>
  <version>3.0.0-beta.6</version>
</dependency>

Client Creation

Sync and Async Clients

import com.azure.ai.anomalydetector.AnomalyDetectorClientBuilder;
import com.azure.ai.anomalydetector.MultivariateClient;
import com.azure.ai.anomalydetector.UnivariateClient;
import com.azure.core.credential.AzureKeyCredential;

String endpoint = System.getenv("AZURE_ANOMALY_DETECTOR_ENDPOINT");
String key = System.getenv("AZURE_ANOMALY_DETECTOR_API_KEY");

// Multivariate client for multiple correlated signals
MultivariateClient multivariateClient = new AnomalyDetectorClientBuilder()
    .credential(new AzureKeyCredential(key))
    .endpoint(endpoint)
    .buildMultivariateClient();

// Univariate client for single variable analysis
UnivariateClient univariateClient = new AnomalyDetectorClientBuilder()
    .credential(new AzureKeyCredential(key))
    .endpoint(endpoint)
    .buildUnivariateClient();

With DefaultAzureCredential

import com.azure.core.credential.TokenCredential;
import com.azure.identity.AzureIdentityEnvVars;
import com.azure.identity.DefaultAzureCredentialBuilder;
import com.azure.identity.ManagedIdentityCredentialBuilder;

TokenCredential credential = new DefaultAzureCredentialBuilder()
    .requireEnvVars(AzureIdentityEnvVars.AZURE_TOKEN_CREDENTIALS)
    .build();
// Or use a specific credential directly in production:
// See https://learn.microsoft.com/java/api/overview/azure/identity-readme?view=azure-java-stable#credential-classes
// TokenCredential credential = new ManagedIdentityCredentialBuilder().build();

MultivariateClient client = new AnomalyDetectorClientBuilder()
    .credential(credential)
    .endpoint(endpoint)
    .buildMultivariateClient();

Key Concepts

Univariate Anomaly Detection

Batch Detection: Analyze entire time series at once
Streaming Detection: Real-time detection on latest data point
Change Point Detection: Detect trend changes in time series

Multivariate Anomaly Detection

Detect anomalies across 300+ correlated signals
Uses Graph Attention Network for inter-correlations
Three-step process: Train → Inference → Results

Core Patterns

Univariate Batch Detection

import com.azure.ai.anomalydetector.models.*;
import java.time.OffsetDateTime;
import java.util.List;

List<TimeSeriesPoint> series = List.of(
    new TimeSeriesPoint(OffsetDateTime.parse("2023-01-01T00:00:00Z"), 1.0),
    new TimeSeriesPoint(OffsetDateTime.parse("2023-01-02T00:00:00Z"), 2.5),
    // ... more data points (minimum 12 points required)
);

UnivariateDetectionOptions options = new UnivariateDetectionOptions(series)
    .setGranularity(TimeGranularity.DAILY)
    .setSensitivity(95);

UnivariateEntireDetectionResult result = univariateClient.detectUnivariateEntireSeries(options);

// Check for anomalies
for (int i = 0; i < result.getIsAnomaly().size(); i++) {
    if (result.getIsAnomaly().get(i)) {
        System.out.printf("Anomaly detected at index %d with value %.2f%n",
            i, series.get(i).getValue());
    }
}

Univariate Last Point Detection (Streaming)

UnivariateLastDetectionResult lastResult = univariateClient.detectUnivariateLastPoint(options);

if (lastResult.isAnomaly()) {
    System.out.println("Latest point is an anomaly!");
    System.out.printf("Expected: %.2f, Upper: %.2f, Lower: %.2f%n",
        lastResult.getExpectedValue(),
        lastResult.getUpperMargin(),
        lastResult.getLowerMargin());
}

Change Point Detection

UnivariateChangePointDetectionOptions changeOptions = 
    new UnivariateChangePointDetectionOptions(series, TimeGranularity.DAILY);

UnivariateChangePointDetectionResult changeResult = 
    univariateClient.detectUnivariateChangePoint(changeOptions);

for (int i = 0; i < changeResult.getIsChangePoint().size(); i++) {
    if (changeResult.getIsChangePoint().get(i)) {
        System.out.printf("Change point at index %d with confidence %.2f%n",
            i, changeResult.getConfidenceScores().get(i));
    }
}

Multivariate Model Training

import com.azure.ai.anomalydetector.models.*;
import com.azure.core.util.polling.SyncPoller;

// Prepare training request with blob storage data
ModelInfo modelInfo = new ModelInfo()
    .setDataSource("https://storage.blob.core.windows.net/container/data.zip?sasToken")
    .setStartTime(OffsetDateTime.parse("2023-01-01T00:00:00Z"))
    .setEndTime(OffsetDateTime.parse("2023-06-01T00:00:00Z"))
    .setSlidingWindow(200)
    .setDisplayName("MyMultivariateModel");

// Train model (long-running operation)
AnomalyDetectionModel trainedModel = multivariateClient.trainMultivariateModel(modelInfo);

String modelId = trainedModel.getModelId();
System.out.println("Model ID: " + modelId);

// Check training status
AnomalyDetectionModel model = multivariateClient.getMultivariateModel(modelId);
System.out.println("Status: " + model.getModelInfo().getStatus());

Multivariate Batch Inference

MultivariateBatchDetectionOptions detectionOptions = new MultivariateBatchDetectionOptions()
    .setDataSource("https://storage.blob.core.windows.net/container/inference-data.zip?sasToken")
    .setStartTime(OffsetDateTime.parse("2023-07-01T00:00:00Z"))
    .setEndTime(OffsetDateTime.parse("2023-07-31T00:00:00Z"))
    .setTopContributorCount(10);

MultivariateDetectionResult detectionResult = 
    multivariateClient.detectMultivariateBatchAnomaly(modelId, detectionOptions);

String resultId = detectionResult.getResultId();

// Poll for results
MultivariateDetectionResult result = multivariateClient.getBatchDetectionResult(resultId);
for (AnomalyState state : result.getResults()) {
    if (state.getValue().isAnomaly()) {
        System.out.printf("Anomaly at %s, severity: %.2f%n",
            state.getTimestamp(),
            state.getValue().getSeverity());
    }
}

Multivariate Last Point Detection

MultivariateLastDetectionOptions lastOptions = new MultivariateLastDetectionOptions()
    .setVariables(List.of(
        new VariableValues("variable1", List.of("timestamp1"), List.of(1.0f)),
        new VariableValues("variable2", List.of("timestamp1"), List.of(2.5f))
    ))
    .setTopContributorCount(5);

MultivariateLastDetectionResult lastResult = 
    multivariateClient.detectMultivariateLastAnomaly(modelId, lastOptions);

if (lastResult.getValue().isAnomaly()) {
    System.out.println("Anomaly detected!");
    // Check contributing variables
    for (AnomalyContributor contributor : lastResult.getValue().getInterpretation()) {
        System.out.printf("Variable: %s, Contribution: %.2f%n",
            contributor.getVariable(),
            contributor.getContributionScore());
    }
}

Model Management

// List all models
PagedIterable<AnomalyDetectionModel> models = multivariateClient.listMultivariateModels();
for (AnomalyDetectionModel m : models) {
    System.out.printf("Model: %s, Status: %s%n",
        m.getModelId(),
        m.getModelInfo().getStatus());
}

// Delete a model
multivariateClient.deleteMultivariateModel(modelId);

Error Handling

import com.azure.core.exception.HttpResponseException;

try {
    univariateClient.detectUnivariateEntireSeries(options);
} catch (HttpResponseException e) {
    System.out.println("Status code: " + e.getResponse().getStatusCode());
    System.out.println("Error: " + e.getMessage());
}

Environment Variables

AZURE_ANOMALY_DETECTOR_ENDPOINT=https://<resource>.cognitiveservices.azure.com/ # Required for all auth methods
AZURE_ANOMALY_DETECTOR_API_KEY=<your-api-key> # Only required for AzureKeyCredential auth
AZURE_TOKEN_CREDENTIALS=prod  # Required only if DefaultAzureCredential is used in production

Best Practices

Minimum Data Points: Univariate requires at least 12 points; more data improves accuracy
Granularity Alignment: Match TimeGranularity to your actual data frequency
Sensitivity Tuning: Higher values (0-99) detect more anomalies
Multivariate Training: Use 200-1000 sliding window based on pattern complexity
Error Handling: Always handle HttpResponseException for API errors

Trigger Phrases

"anomaly detection Java"
"detect anomalies time series"
"multivariate anomaly Java"
"univariate anomaly detection"
"streaming anomaly detection"
"change point detection"
"Azure AI Anomaly Detector"

Files

ATTRIBUTION.mdAdded by AutoGineer994 B
LICENSELicence file added by AutoGineer1.1 KiB
SKILL.md8.8 KiB
references/examples.md23.8 KiB

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs an API key

Security

No issues found by our static scan· No issues found

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by

Microsoft

Publisher

Microsoft (Official)

Source

microsoft/skills · .github/plugins/azure-sdk-java/skills/azure-ai-anomalydetector-java · commit 0696600

Licence

MIT License (redistribution allowed)

LICENSE (added by AutoGineer from LICENSE)
ATTRIBUTION.md (written by AutoGineer)

Synced

2026-11-08

AutoGineer is not affiliated with or endorsed by Microsoft.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download azure-ai-anomalydetector-java.zip