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.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

• 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"