Azure AI Anomaly Detector in Java

--- name: azure-ai-anomalydetector-java description: "Build anomaly detection applications using the Azure AI Anomaly Detector SDK for Java. Use this when implementing univariate or multivariate anomaly detection, time-series analysis, or AI-powered monitoring." risk: unknown source: community date_added: "2026-02-27" --- # Azure AI Anomaly Detector SDK for Java Build anomaly detection applications using the Azure AI Anomaly Detector SDK for Java. ## Installation ``xml <dependency> <groupId>com.azure</groupId> <artifactId>azure-ai-anomalydetector</artifactId> <version>3.0.0-beta.6</version> </dependency> ` ## Client Creation ### Sync and Async Clients `java 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 `java 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 `java 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) `java 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 `java 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 ``java import com.azure.ai.anomalydetector.models.*; import com.azure