Anomaly Detection · Schema

DetectionJob

Configuration for an anomaly detection job that analyzes one or more time series using a specified algorithm and detection settings.

Anomaly DetectionArtificial IntelligenceData ScienceFraud DetectionMachine-LearningMonitoringObservabilityOutlier DetectionPattern RecognitionSecurityTime Series

Properties

Name Type Description
id string Unique identifier for the detection job.
name string Human-readable name for the detection job.
description string Description of what this detection job monitors and why.
status string Current operational status of the detection job.
algorithm string The anomaly detection algorithm used by this job.
mode string Detection mode — batch retrospective, streaming real-time, or multivariate correlation-based.
sensitivity number Sensitivity level controlling the anomaly detection threshold. Higher values detect more subtle anomalies.
seasonality string Seasonality pattern used for baseline modeling.
series_ids array List of time series identifiers analyzed by this job.
created_at string Timestamp when the job was created.
modified_at string Timestamp when the job was last modified.
View JSON Schema on GitHub

JSON Schema

anomaly-detection-detection-job-schema.json Raw ↑
{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "$id": "https://raw.githubusercontent.com/api-evangelist/anomaly-detection/refs/heads/main/json-schema/anomaly-detection-detection-job-schema.json",
  "title": "DetectionJob",
  "description": "Configuration for an anomaly detection job that analyzes one or more time series using a specified algorithm and detection settings.",
  "type": "object",
  "properties": {
    "id": {
      "type": "string",
      "description": "Unique identifier for the detection job.",
      "example": "job-500456"
    },
    "name": {
      "type": "string",
      "description": "Human-readable name for the detection job.",
      "example": "Production API Latency Anomaly Detector"
    },
    "description": {
      "type": "string",
      "description": "Description of what this detection job monitors and why.",
      "example": "Monitors API p99 latency for anomalies using daily seasonality."
    },
    "status": {
      "type": "string",
      "enum": ["pending", "running", "paused", "closed", "failed"],
      "description": "Current operational status of the detection job.",
      "example": "running"
    },
    "algorithm": {
      "type": "string",
      "enum": ["basic", "agile", "robust", "iforest", "lof", "ocsvm", "autoencoder", "sr-cnn", "sarima", "graph-attention-network"],
      "description": "The anomaly detection algorithm used by this job.",
      "example": "agile"
    },
    "mode": {
      "type": "string",
      "enum": ["batch", "streaming", "multivariate"],
      "description": "Detection mode — batch retrospective, streaming real-time, or multivariate correlation-based.",
      "example": "streaming"
    },
    "sensitivity": {
      "type": "number",
      "minimum": 0,
      "maximum": 10,
      "description": "Sensitivity level controlling the anomaly detection threshold. Higher values detect more subtle anomalies.",
      "example": 3
    },
    "seasonality": {
      "type": "string",
      "enum": ["hourly", "daily", "weekly", "none", "auto"],
      "description": "Seasonality pattern used for baseline modeling.",
      "example": "daily"
    },
    "series_ids": {
      "type": "array",
      "description": "List of time series identifiers analyzed by this job.",
      "items": {
        "type": "string"
      },
      "example": ["ts-api-latency-p99", "ts-api-error-rate"]
    },
    "created_at": {
      "type": "string",
      "format": "date-time",
      "description": "Timestamp when the job was created.",
      "example": "2026-04-01T00:00:00Z"
    },
    "modified_at": {
      "type": "string",
      "format": "date-time",
      "description": "Timestamp when the job was last modified.",
      "example": "2026-04-19T00:00:00Z"
    }
  },
  "required": ["id", "name", "status", "algorithm", "mode"]
}

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