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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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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unknown
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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.
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(
newTimeSeriesPoint(OffsetDateTime.parse("2023-01-01T00:00:00Z"), 1.0),
newTimeSeriesPoint(OffsetDateTime.parse("2023-01-02T00:00:00Z"), 2.5),
// ... more data points (minimum 12 points required)
);
UnivariateDetectionOptionsoptions=newUnivariateDetectionOptions(series)
.setGranularity(TimeGranularity.DAILY)
.setSensitivity(95);
UnivariateEntireDetectionResultresult= univariateClient.detectUnivariateEntireSeries(options);
// Check for anomaliesfor (inti=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)
UnivariateLastDetectionResultlastResult= 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
UnivariateChangePointDetectionOptionschangeOptions=newUnivariateChangePointDetectionOptions(series, TimeGranularity.DAILY);
UnivariateChangePointDetectionResultchangeResult=
univariateClient.detectUnivariateChangePoint(changeOptions);
for (inti=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 dataModelInfomodelInfo=newModelInfo()
.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)AnomalyDetectionModeltrainedModel= multivariateClient.trainMultivariateModel(modelInfo);
StringmodelId= trainedModel.getModelId();
System.out.println("Model ID: " + modelId);
// Check training statusAnomalyDetectionModelmodel= multivariateClient.getMultivariateModel(modelId);
System.out.println("Status: " + model.getModelInfo().getStatus());
Multivariate Batch Inference
MultivariateBatchDetectionOptionsdetectionOptions=newMultivariateBatchDetectionOptions()
.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);
MultivariateDetectionResultdetectionResult=
multivariateClient.detectMultivariateBatchAnomaly(modelId, detectionOptions);
StringresultId= detectionResult.getResultId();
// Poll for resultsMultivariateDetectionResultresult= 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());
}
}
// 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);
