Azure AI Anomaly Detector SDK for Java

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

Service status

Microsoft’s retirement guidance states that new resources were disabled in September 2023 and service retirement was scheduled for October 2026. Treat these SDK fragments as historical/migration reference, not a path for a new cloud deployment. Verify the actual resource lifecycle and evaluate the documented Fabric/open-source alternatives before implementation.

The snippets omit surrounding application/imports and are not a tested complete program. For asynchronous training/inference, poll terminal status with a deadline and handle failure before accessing results. Use approved data, short-lived least-privilege storage access and private reports; never expose SAS tokens.

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.identity.DefaultAzureCredentialBuilder;

MultivariateClient client = new AnomalyDetectorClientBuilder()
    .credential(new DefaultAzureCredentialBuilder().build())
    .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/
AZURE_ANOMALY_DETECTOR_API_KEY=<your-api-key>

Best Practices

1. Minimum Data Points: Univariate requires at least 12 points; more data improves accuracy
2. Granularity Alignment: Match TimeGranularity to your actual data frequency
3. Sensitivity Tuning: Higher values (0-99) detect more anomalies
4. Multivariate Training: Use 200-1000 sliding window based on pattern complexity
5. 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"

When to Use

This skill is applicable to execute the workflow or actions described in the overview.

Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.