Azure Ai Anomalydetector Java

Azure AI Anomaly Detector SDK for Java

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

Installation

  com.azure
  azure-ai-anomalydetector
  3.0.0-beta.6

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 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  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://.cognitiveservices.azure.com/ # Required for all auth methods
AZURE_ANOMALY_DETECTOR_API_KEY= # Only required for AzureKeyCredential auth
AZURE_TOKEN_CREDENTIALS=prod  # Required only if DefaultAzureCredential is used in production

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"

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: microsoft
• Source: microsoft/skills
• License: MIT
• Homepage: https://microsoft.github.io/skills/

Install and usage instructions live in the source repository linked above.