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Use when implementing univariate\u002Fmultivariate anomaly detection, time-series analysis, or AI-powered monitoring.","ComeOnOliver\u002Fskillshub","ComeOnOliver","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F43215937?v=4","01M6E5JNFECJAAVFE0W7SMWWD2","Haoran Shu","01M6E5JNNDQS7AJZDYFQ8TPZCK",66,"MIT","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Faiskillstore\u002Fmarketplace\u002Fsickn33\u002Fazure-ai-anomalydetector-java\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36],"azure ai","azure","azure ai anomalydetector java","java","with","sdk","anomaly detection","series","time-series","time","anomaly detector","ai anomaly detector","build","use","analysis","applications","detection","monitoring","2026-11-10 10:12:30",false,"---\nname: azure-ai-anomalydetector-java\ndescription: Build anomaly detection applications with Azure AI Anomaly Detector SDK for Java. Use when implementing univariate\u002Fmultivariate anomaly detection, time-series analysis, or AI-powered monitoring.\npackage: com.azure:azure-ai-anomalydetector\n---\n\n# Azure AI Anomaly Detector SDK for Java\n\nBuild anomaly detection applications using the Azure AI Anomaly Detector SDK for Java.\n\n## Installation\n\n```xml\n\u003Cdependency>\n  \u003CgroupId>com.azure\u003C\u002FgroupId>\n  \u003CartifactId>azure-ai-anomalydetector\u003C\u002FartifactId>\n  \u003Cversion>3.0.0-beta.6\u003C\u002Fversion>\n\u003C\u002Fdependency>\n```\n\n## Client Creation\n\n### Sync and Async Clients\n\n```java\nimport com.azure.ai.anomalydetector.AnomalyDetectorClientBuilder;\nimport com.azure.ai.anomalydetector.MultivariateClient;\nimport com.azure.ai.anomalydetector.UnivariateClient;\nimport com.azure.core.credential.AzureKeyCredential;\n\nString endpoint = System.getenv(\"AZURE_ANOMALY_DETECTOR_ENDPOINT\");\nString key = System.getenv(\"AZURE_ANOMALY_DETECTOR_API_KEY\");\n\n\u002F\u002F Multivariate client for multiple correlated signals\nMultivariateClient multivariateClient = new AnomalyDetectorClientBuilder()\n    .credential(new AzureKeyCredential(key))\n    .endpoint(endpoint)\n    .buildMultivariateClient();\n\n\u002F\u002F Univariate client for single variable analysis\nUnivariateClient univariateClient = new AnomalyDetectorClientBuilder()\n    .credential(new AzureKeyCredential(key))\n    .endpoint(endpoint)\n    .buildUnivariateClient();\n```\n\n### With DefaultAzureCredential\n\n```java\nimport com.azure.identity.DefaultAzureCredentialBuilder;\n\nMultivariateClient client = new AnomalyDetectorClientBuilder()\n    .credential(new DefaultAzureCredentialBuilder().build())\n    .endpoint(endpoint)\n    .buildMultivariateClient();\n```\n\n## Key Concepts\n\n### Univariate Anomaly Detection\n- **Batch Detection**: Analyze entire time series at once\n- **Streaming Detection**: Real-time detection on latest data point\n- **Change Point Detection**: Detect trend changes in time series\n\n### Multivariate Anomaly Detection\n- Detect anomalies across 300+ correlated signals\n- Uses Graph Attention Network for inter-correlations\n- Three-step process: Train → Inference → Results\n\n## Core Patterns\n\n### Univariate Batch Detection\n\n```java\nimport com.azure.ai.anomalydetector.models.*;\nimport java.time.OffsetDateTime;\nimport java.util.List;\n\nList\u003CTimeSeriesPoint> series = List.of(\n    new TimeSeriesPoint(OffsetDateTime.parse(\"2023-01-01T00:00:00Z\"), 1.0),\n    new TimeSeriesPoint(OffsetDateTime.parse(\"2023-01-02T00:00:00Z\"), 2.5),\n    \u002F\u002F ... more data points (minimum 12 points required)\n);\n\nUnivariateDetectionOptions options = new UnivariateDetectionOptions(series)\n    .setGranularity(TimeGranularity.DAILY)\n    .setSensitivity(95);\n\nUnivariateEntireDetectionResult result = univariateClient.detectUnivariateEntireSeries(options);\n\n\u002F\u002F Check for anomalies\nfor (int i = 0; i \u003C result.getIsAnomaly().size(); i++) {\n    if (result.getIsAnomaly().get(i)) {\n        System.out.printf(\"Anomaly detected at index %d with value %.2f%n\",\n            i, series.get(i).getValue());\n    }\n}\n```\n\n### Univariate Last Point Detection (Streaming)\n\n```java\nUnivariateLastDetectionResult lastResult = univariateClient.detectUnivariateLastPoint(options);\n\nif (lastResult.isAnomaly()) {\n    System.out.println(\"Latest point is an anomaly!\");\n    System.out.printf(\"Expected: %.2f, Upper: %.2f, Lower: %.2f%n\",\n        lastResult.getExpectedValue(),\n        lastResult.getUpperMargin(),\n        lastResult.getLowerMargin());\n}\n```\n\n### Change Point Detection\n\n```java\nUnivariateChangePointDetectionOptions changeOptions = \n    new UnivariateChangePointDetectionOptions(series, TimeGranularity.DAILY);\n\nUnivariateChangePointDetectionResult changeResult = \n    univariateClient.detectUnivariateChangePoint(changeOptions);\n\nfor (int i = 0; i \u003C changeResult.getIsChangePoint().size(); i++) {\n    if (changeResult.getIsChangePoint().get(i)) {\n        System.out.printf(\"Change point at index %d with confidence %.2f%n\",\n            i, changeResult.getConfidenceScores().get(i));\n    }\n}\n```\n\n### Multivariate Model Training\n\n```java\nimport com.azure.ai.anomalydetector.models.*;\nimport com.azure.core.util.polling.SyncPoller;\n\n\u002F\u002F Prepare training request with blob storage data\nModelInfo modelInfo = new ModelInfo()\n    .setDataSource(\"https:\u002F\u002Fstorage.blob.core.windows.net\u002Fcontainer\u002Fdata.zip?sasToken\")\n    .setStartTime(OffsetDateTime.parse(\"2023-01-01T00:00:00Z\"))\n    .setEndTime(OffsetDateTime.parse(\"2023-06-01T00:00:00Z\"))\n    .setSlidingWindow(200)\n    .setDisplayName(\"MyMultivariateModel\");\n\n\u002F\u002F Train model (long-running operation)\nAnomalyDetectionModel trainedModel = multivariateClient.trainMultivariateModel(modelInfo);\n\nString modelId = trainedModel.getModelId();\nSystem.out.println(\"Model ID: \" + modelId);\n\n\u002F\u002F Check training status\nAnomalyDetectionModel model = multivariateClient.getMultivariateModel(modelId);\nSystem.out.println(\"Status: \" + model.getModelInfo().getStatus());\n```\n\n### Multivariate Batch Inference\n\n```java\nMultivariateBatchDetectionOptions detectionOptions = new MultivariateBatchDetectionOptions()\n    .setDataSource(\"https:\u002F\u002Fstorage.blob.core.windows.net\u002Fcontainer\u002Finference-data.zip?sasToken\")\n    .setStartTime(OffsetDateTime.parse(\"2023-07-01T00:00:00Z\"))\n    .setEndTime(OffsetDateTime.parse(\"2023-07-31T00:00:00Z\"))\n    .setTopContributorCount(10);\n\nMultivariateDetectionResult detectionResult = \n    multivariateClient.detectMultivariateBatchAnomaly(modelId, detectionOptions);\n\nString resultId = detectionResult.getResultId();\n\n\u002F\u002F Poll for results\nMultivariateDetectionResult result = multivariateClient.getBatchDetectionResult(resultId);\nfor (AnomalyState state : result.getResults()) {\n    if (state.getValue().isAnomaly()) {\n        System.out.printf(\"Anomaly at %s, severity: %.2f%n\",\n            state.getTimestamp(),\n            state.getValue().getSeverity());\n    }\n}\n```\n\n### Multivariate Last Point Detection\n\n```java\nMultivariateLastDetectionOptions lastOptions = new MultivariateLastDetectionOptions()\n    .setVariables(List.of(\n        new VariableValues(\"variable1\", List.of(\"timestamp1\"), List.of(1.0f)),\n        new VariableValues(\"variable2\", List.of(\"timestamp1\"), List.of(2.5f))\n    ))\n    .setTopContributorCount(5);\n\nMultivariateLastDetectionResult lastResult = \n    multivariateClient.detectMultivariateLastAnomaly(modelId, lastOptions);\n\nif (lastResult.getValue().isAnomaly()) {\n    System.out.println(\"Anomaly detected!\");\n    \u002F\u002F Check contributing variables\n    for (AnomalyContributor contributor : lastResult.getValue().getInterpretation()) {\n        System.out.printf(\"Variable: %s, Contribution: %.2f%n\",\n            contributor.getVariable(),\n            contributor.getContributionScore());\n    }\n}\n```\n\n### Model Management\n\n```java\n\u002F\u002F List all models\nPagedIterable\u003CAnomalyDetectionModel> models = multivariateClient.listMultivariateModels();\nfor (AnomalyDetectionModel m : models) {\n    System.out.printf(\"Model: %s, Status: %s%n\",\n        m.getModelId(),\n        m.getModelInfo().getStatus());\n}\n\n\u002F\u002F Delete a model\nmultivariateClient.deleteMultivariateModel(modelId);\n```\n\n## Error Handling\n\n```java\nimport com.azure.core.exception.HttpResponseException;\n\ntry {\n    univariateClient.detectUnivariateEntireSeries(options);\n} catch (HttpResponseException e) {\n    System.out.println(\"Status code: \" + e.getResponse().getStatusCode());\n    System.out.println(\"Error: \" + e.getMessage());\n}\n```\n\n## Environment Variables\n\n```bash\nAZURE_ANOMALY_DETECTOR_ENDPOINT=https:\u002F\u002F\u003Cresource>.cognitiveservices.azure.com\u002F\nAZURE_ANOMALY_DETECTOR_API_KEY=\u003Cyour-api-key>\n```\n\n## Best Practices\n\n1. **Minimum Data Points**: Univariate requires at least 12 points; more data improves accuracy\n2. **Granularity Alignment**: Match `TimeGranularity` to your actual data frequency\n3. **Sensitivity Tuning**: Higher values (0-99) detect more anomalies\n4. **Multivariate Training**: Use 200-1000 sliding window based on pattern complexity\n5. **Error Handling**: Always handle `HttpResponseException` for API errors\n\n## Trigger Phrases\n\n- \"anomaly detection Java\"\n- \"detect anomalies time series\"\n- \"multivariate anomaly Java\"\n- \"univariate anomaly detection\"\n- \"streaming anomaly detection\"\n- \"change point detection\"\n- \"Azure AI Anomaly Detector\"\n","# Azure AI Anomaly Detector SDK for Java\n\nBuild anomaly detection applications using the Azure AI Anomaly Detector SDK for Java.\n\n## Installation\n\n```xml\n\u003Cdependency>\n  \u003CgroupId>com.azure\u003C\u002FgroupId>\n  \u003CartifactId>azure-ai-anomalydetector\u003C\u002FartifactId>\n  \u003Cversion>3.0.0-beta.6\u003C\u002Fversion>\n\u003C\u002Fdependency>\n```\n\n## Client Creation\n\n### Sync and Async Clients\n\n```java\nimport com.azure.ai.anomalydetector.AnomalyDetectorClientBuilder;\nimport com.azure.ai.anomalydetector.MultivariateClient;\nimport com.azure.ai.anomalydetector.UnivariateClient;\nimport com.azure.core.credential.AzureKeyCredential;\n\nString endpoint = System.getenv(\"AZURE_ANOMALY_DETECTOR_ENDPOINT\");\nString key = System.getenv(\"AZURE_ANOMALY_DETECTOR_API_KEY\");\n\n\u002F\u002F Multivariate client for multiple correlated signals\nMultivariateClient multivariateClient = new AnomalyDetectorClientBuilder()\n    .credential(new AzureKeyCredential(key))\n    .endpoint(endpoint)\n    .buildMultivariateClient();\n\n\u002F\u002F Univariate client for single variable analysis\nUnivariateClient univariateClient = new AnomalyDetectorClientBuilder()\n    .credential(new AzureKeyCredential(key))\n    .endpoint(endpoint)\n    .buildUnivariateClient();\n```\n\n### With DefaultAzureCredential\n\n```java\nimport com.azure.identity.DefaultAzureCredentialBuilder;\n\nMultivariateClient client = new AnomalyDetectorClientBuilder()\n    .credential(new DefaultAzureCredentialBuilder().build())\n    .endpoint(endpoint)\n    .buildMultivariateClient();\n```\n\n## Key Concepts\n\n### Univariate Anomaly Detection\n- **Batch Detection**: Analyze entire time series at once\n- **Streaming Detection**: Real-time detection on latest data point\n- **Change Point Detection**: Detect trend changes in time series\n\n### Multivariate Anomaly Detection\n- Detect anomalies across 300+ correlated signals\n- Uses Graph Attention Network for inter-correlations\n- Three-step process: Train → Inference → Results\n\n## Core Patterns\n\n### Univariate Batch Detection\n\n```java\nimport com.azure.ai.anomalydetector.models.*;\nimport java.time.OffsetDateTime;\nimport java.util.List;\n\nList\u003CTimeSeriesPoint> series = List.of(\n    new TimeSeriesPoint(OffsetDateTime.parse(\"2023-01-01T00:00:00Z\"), 1.0),\n    new TimeSeriesPoint(OffsetDateTime.parse(\"2023-01-02T00:00:00Z\"), 2.5),\n    \u002F\u002F ... more data points (minimum 12 points required)\n);\n\nUnivariateDetectionOptions options = new UnivariateDetectionOptions(series)\n    .setGranularity(TimeGranularity.DAILY)\n    .setSensitivity(95);\n\nUnivariateEntireDetectionResult result = univariateClient.detectUnivariateEntireSeries(options);\n\n\u002F\u002F Check for anomalies\nfor (int i = 0; i \u003C result.getIsAnomaly().size(); i++) {\n    if (result.getIsAnomaly().get(i)) {\n        System.out.printf(\"Anomaly detected at index %d with value %.2f%n\",\n            i, series.get(i).getValue());\n    }\n}\n```\n\n### Univariate Last Point Detection (Streaming)\n\n```java\nUnivariateLastDetectionResult lastResult = univariateClient.detectUnivariateLastPoint(options);\n\nif (lastResult.isAnomaly()) {\n    System.out.println(\"Latest point is an anomaly!\");\n    System.out.printf(\"Expected: %.2f, Upper: %.2f, Lower: %.2f%n\",\n        lastResult.getExpectedValue(),\n        lastResult.getUpperMargin(),\n        lastResult.getLowerMargin());\n}\n```\n\n### Change Point Detection\n\n```java\nUnivariateChangePointDetectionOptions changeOptions = \n    new UnivariateChangePointDetectionOptions(series, TimeGranularity.DAILY);\n\nUnivariateChangePointDetectionResult changeResult = \n    univariateClient.detectUnivariateChangePoint(changeOptions);\n\nfor (int i = 0; i \u003C changeResult.getIsChangePoint().size(); i++) {\n    if (changeResult.getIsChangePoint().get(i)) {\n        System.out.printf(\"Change point at index %d with confidence %.2f%n\",\n            i, changeResult.getConfidenceScores().get(i));\n    }\n}\n```\n\n### Multivariate Model Training\n\n```java\nimport com.azure.ai.anomalydetector.models.*;\nimport com.azure.core.util.polling.SyncPoller;\n\n\u002F\u002F Prepare training request with blob storage data\nModelInfo modelInfo = new ModelInfo()\n    .setDataSource(\"https:\u002F\u002Fstorage.blob.core.windows.net\u002Fcontainer\u002Fdata.zip?sasToken\")\n    .setStartTime(OffsetDateTime.parse(\"2023-01-01T00:00:00Z\"))\n    .setEndTime(OffsetDateTime.parse(\"2023-06-01T00:00:00Z\"))\n    .setSlidingWindow(200)\n    .setDisplayName(\"MyMultivariateModel\");\n\n\u002F\u002F Train model (long-running operation)\nAnomalyDetectionModel trainedModel = multivariateClient.trainMultivariateModel(modelInfo);\n\nString modelId = trainedModel.getModelId();\nSystem.out.println(\"Model ID: \" + modelId);\n\n\u002F\u002F Check training status\nAnomalyDetectionModel model = multivariateClient.getMultivariateModel(modelId);\nSystem.out.println(\"Status: \" + model.getModelInfo().getStatus());\n```\n\n### Multivariate Batch Inference\n\n```java\nMultivariateBatchDetectionOptions detectionOptions = new MultivariateBatchDetectionOptions()\n    .setDataSource(\"https:\u002F\u002Fstorage.blob.core.windows.net\u002Fcontainer\u002Finference-data.zip?sasToken\")\n    .setStartTime(OffsetDateTime.parse(\"2023-07-01T00:00:00Z\"))\n    .setEndTime(OffsetDateTime.parse(\"2023-07-31T00:00:00Z\"))\n    .setTopContributorCount(10);\n\nMultivariateDetectionResult detectionResult = \n    multivariateClient.detectMultivariateBatchAnomaly(modelId, detectionOptions);\n\nString resultId = detectionResult.getResultId();\n\n\u002F\u002F Poll for results\nMultivariateDetectionResult result = multivariateClient.getBatchDetectionResult(resultId);\nfor (AnomalyState state : result.getResults()) {\n    if (state.getValue().isAnomaly()) {\n        System.out.printf(\"Anomaly at %s, severity: %.2f%n\",\n            state.getTimestamp(),\n            state.getValue().getSeverity());\n    }\n}\n```\n\n### Multivariate Last Point Detection\n\n```java\nMultivariateLastDetectionOptions lastOptions = new MultivariateLastDetectionOptions()\n    .setVariables(List.of(\n        new VariableValues(\"variable1\", List.of(\"timestamp1\"), List.of(1.0f)),\n        new VariableValues(\"variable2\", List.of(\"timestamp1\"), List.of(2.5f))\n    ))\n    .setTopContributorCount(5);\n\nMultivariateLastDetectionResult lastResult = \n    multivariateClient.detectMultivariateLastAnomaly(modelId, lastOptions);\n\nif (lastResult.getValue().isAnomaly()) {\n    System.out.println(\"Anomaly detected!\");\n    \u002F\u002F Check contributing variables\n    for (AnomalyContributor contributor : lastResult.getValue().getInterpretation()) {\n        System.out.printf(\"Variable: %s, Contribution: %.2f%n\",\n            contributor.getVariable(),\n            contributor.getContributionScore());\n    }\n}\n```\n\n### Model Management\n\n```java\n\u002F\u002F List all models\nPagedIterable\u003CAnomalyDetectionModel> models = multivariateClient.listMultivariateModels();\nfor (AnomalyDetectionModel m : models) {\n    System.out.printf(\"Model: %s, Status: %s%n\",\n        m.getModelId(),\n        m.getModelInfo().getStatus());\n}\n\n\u002F\u002F Delete a model\nmultivariateClient.deleteMultivariateModel(modelId);\n```\n\n## Error Handling\n\n```java\nimport com.azure.core.exception.HttpResponseException;\n\ntry {\n    univariateClient.detectUnivariateEntireSeries(options);\n} catch (HttpResponseException e) {\n    System.out.println(\"Status code: \" + e.getResponse().getStatusCode());\n    System.out.println(\"Error: \" + e.getMessage());\n}\n```\n\n## Environment Variables\n\n```bash\nAZURE_ANOMALY_DETECTOR_ENDPOINT=https:\u002F\u002F\u003Cresource>.cognitiveservices.azure.com\u002F\nAZURE_ANOMALY_DETECTOR_API_KEY=\u003Cyour-api-key>\n```\n\n## Best Practices\n\n1. **Minimum Data Points**: Univariate requires at least 12 points; more data improves accuracy\n2. **Granularity Alignment**: Match 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