Anomaly Detection · Schema

TimeSeries

A named time series used as input for anomaly detection, containing an ordered sequence of timestamped metric values.

Anomaly DetectionArtificial IntelligenceData ScienceFraud DetectionMachine LearningMonitoringObservabilityOutlier DetectionPattern RecognitionSecurityTime Series

Properties

Name Type Description
id string Unique identifier for the time series.
name string Human-readable name for the time series.
description string Description of what this time series measures.
metric string The metric being tracked in this series.
unit string Unit of measurement for the metric values.
granularity string Time granularity between data points (ISO 8601 duration).
seasonality string The dominant seasonality pattern observed in this series.
data_points array Ordered list of timestamped data points.
created_at string Timestamp when this time series was created.
dimensions object Dimensional metadata tags for this series.
View JSON Schema on GitHub

JSON Schema

anomaly-detection-time-series-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-time-series-schema.json",
  "title": "TimeSeries",
  "description": "A named time series used as input for anomaly detection, containing an ordered sequence of timestamped metric values.",
  "type": "object",
  "properties": {
    "id": {
      "type": "string",
      "description": "Unique identifier for the time series.",
      "example": "ts-prod-cluster-01"
    },
    "name": {
      "type": "string",
      "description": "Human-readable name for the time series.",
      "example": "Production Cluster CPU Utilization"
    },
    "description": {
      "type": "string",
      "description": "Description of what this time series measures.",
      "example": "Average CPU utilization across production web cluster nodes."
    },
    "metric": {
      "type": "string",
      "description": "The metric being tracked in this series.",
      "example": "cpu_utilization"
    },
    "unit": {
      "type": "string",
      "description": "Unit of measurement for the metric values.",
      "example": "percent"
    },
    "granularity": {
      "type": "string",
      "description": "Time granularity between data points (ISO 8601 duration).",
      "example": "PT1M"
    },
    "seasonality": {
      "type": "string",
      "enum": ["hourly", "daily", "weekly", "none"],
      "description": "The dominant seasonality pattern observed in this series.",
      "example": "daily"
    },
    "data_points": {
      "type": "array",
      "description": "Ordered list of timestamped data points.",
      "items": {
        "$ref": "#/$defs/DataPoint"
      }
    },
    "created_at": {
      "type": "string",
      "format": "date-time",
      "description": "Timestamp when this time series was created.",
      "example": "2026-01-01T00:00:00Z"
    },
    "dimensions": {
      "type": "object",
      "description": "Dimensional metadata tags for this series.",
      "additionalProperties": {
        "type": "string"
      }
    }
  },
  "required": ["id", "name", "metric"],
  "$defs": {
    "DataPoint": {
      "type": "object",
      "properties": {
        "timestamp": {
          "type": "string",
          "format": "date-time",
          "description": "Timestamp of the data point.",
          "example": "2026-04-19T14:00:00Z"
        },
        "value": {
          "type": "number",
          "description": "Metric value at this timestamp.",
          "example": 72.5
        }
      },
      "required": ["timestamp", "value"]
    }
  }
}