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.
82
73%
Does it follow best practices?
Impact
98%
2.33xAverage score across 3 eval scenarios
Advisory
Suggest reviewing before use
Optimize this skill with Tessl
npx tessl skill review --optimize ./skills/azure-ai-anomalydetector-java/SKILL.mdUnivariate batch anomaly detection
Correct Maven artifact
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Endpoint env var
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API key env var
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AnomalyDetectorClientBuilder usage
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UnivariateClient built
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AzureKeyCredential used
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TimeSeriesPoint construction
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Minimum 12 data points
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Granularity set
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Sensitivity set
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Batch detection method
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HttpResponseException handling
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Streaming detection and change point detection
Correct Maven artifact
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Env var credentials
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UnivariateClient construction
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Last-point detection method
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isAnomaly check
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Expected/margin values read
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Change point method
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Change point options construction
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Confidence score accessed
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Granularity alignment
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Sensitivity in range
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HttpResponseException handling
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Multivariate model lifecycle management
Correct Maven artifact
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MultivariateClient built
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Env var credentials
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ModelInfo data source
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ModelInfo time range
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Sliding window in range
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trainMultivariateModel called
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Training status check
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TopContributorCount set
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Batch result polling
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Severity read
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Contributor interpretation
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List models
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HttpResponseException handling
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