---
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.
package: com.azure:azure-ai-anomalydetector
---
# 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"
