azure-ai-anomalydetector-java

Topics in the source

• Azure AI Anomaly Detector SDK for Java
• Installation
• Client Creation
• Sync and Async Clients
• With DefaultAzureCredential
• Key Concepts
• Univariate Anomaly Detection
• Multivariate Anomaly Detection

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 Detect anomalies across 300+ correlated signals Uses Graph Attention Network for inter-correlations Three-step process: Train → Inference → Results 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 "anomaly detection Java" "detect anomalies time series" "multivariate anomaly Java" "univariate anomaly detection" "streaming anomaly detection" "change point detection" "Azure AI Anomaly Detector"