{
  "example_kind": "original synthetic manual planning case",
  "slug": "forecast-scaled-error-zero-baseline",
  "title": "Keep a zero baseline out of a scaled-error claim",
  "scenario": "Synthetic planning case: A toy training series has durations four,four,four days. A one-step last-value reference has zero mean absolute training change. A held-out forecast’s mean absolute error is two days.",
  "layout": "evaluation",
  "dataset": {
    "heading": "Inspect the invented case records",
    "headers": [
      "Evidence",
      "Values",
      "Derived quantity"
    ],
    "rows": [
      [
        "Training durations",
        "4,4,4 days",
        "No observed training change"
      ],
      [
        "Naive training errors",
        "0,0 days",
        "Mean absolute reference error 0"
      ],
      [
        "Held-out absolute error",
        "2 days",
        "Finite unscaled error"
      ]
    ]
  },
  "derivation": "Reference scale=(|4−4|+|4−4|)/2=0 days. The proposed scaled value 2/0 is undefined; it is not zero error or evidence of an infinitely reliable model.",
  "result": "The two-day error cannot be divided by the zero reference to produce a finite scaled score. Report the absolute error and the undefined relative score instead of inventing a favorable ratio.",
  "boundary": "The artifact checks a zero training-reference denominator, distinct from an observed zero outcome in percentage error.",
  "limitations": "A tiny constant training series cannot validate a model or a replacement scale. Scaled comparison requires a meaningful nonzero reference.",
  "sources": [
    "forecast"
  ],
  "tasks": [
    [
      "Recover the reference construction",
      "Evaluation owner",
      "The denominator comes from this constant training series."
    ],
    [
      "Keep the undefined division explicit",
      "Reviewer",
      "The two-day absolute error remains reported without a fabricated finite scaled score."
    ],
    [
      "Choose a justified comparison",
      "Planning lead",
      "Any alternative scale or metric is declared separately rather than patched with an arbitrary denominator."
    ]
  ],
  "faqs": [
    [
      "Can I replace zero with one silently?",
      "No. That changes the score’s meaning and comparison basis."
    ],
    [
      "Does constant training history prove future durations stay constant?",
      "No. The held-out error already illustrates a different outcome."
    ]
  ],
  "method_references": [
    {
      "id": "forecast",
      "title": "Forecasting: Principles and Practice — accuracy",
      "url": "https://otexts.com/fpp3/accuracy.html",
      "scope": "Genuine held-out forecast and point-error evaluation context. Binary scoring examples use their own explicit toy definitions; no real task model is validated."
    }
  ]
}