Azure Ai Anomalydetector Java

# 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 Mod