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.
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Where does azure-ai-anomalydetector-java live?
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Which agents can use azure-ai-anomalydetector-java?
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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.
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.
license: MIT
metadata:
author: Microsoft
version: "1.0.0"
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.
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.core.credential.TokenCredential;
import com.azure.identity.AzureIdentityEnvVars;
import com.azure.identity.DefaultAzureCredentialBuilder;
import com.azure.identity.ManagedIdentityCredentialBuilder;
TokenCredential credential = new DefaultAzureCredentialBuilder()
.requireEnvVars(AzureIdentityEnvVars.AZURE_TOKEN_CREDENTIALS)
.build();
// Or use a specific credential directly in production:
// See https://learn.microsoft.com/java/api/overview/azure/identity-readme?view=azure-java-stable#credential-classes
// TokenCredential credential = new ManagedIdentityCredentialBuilder().build();
MultivariateClient client = new AnomalyDetectorClientBuilder()
.credential(credential)
.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);
AZURE_ANOMALY_DETECTOR_ENDPOINT=https://<resource>.cognitiveservices.azure.com/ # Required for all auth methods
AZURE_ANOMALY_DETECTOR_API_KEY=<your-api-key> # Only required for AzureKeyCredential auth
AZURE_TOKEN_CREDENTIALS=prod # Required only if DefaultAzureCredential is used in production
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"
How do I install azure-ai-anomalydetector-java?
Download .github/plugins/azure-sdk-java/skills/azure-ai-anomalydetector-java/SKILL.md from the publisher, or copy the path and open the repository. PlugMCP lists the skill and does not host the file.
Where does azure-ai-anomalydetector-java live?
The publisher keeps it at https://github.com/microsoft/skills.
Which agents can use azure-ai-anomalydetector-java?
Claude, Cursor, and Codex can read a SKILL.md you add from the publisher's repository.