azure-ai-anomalydetector-java — Обнаружение аномалий во временных рядах данных

--- name: azure-ai-anomalydetector-java description: "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." 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.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 ```java 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 ```java 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 ```java // 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 ```java 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 ```bash 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.