---
url: >-
  https://jer-k.github.io/effect-prophet/benchmarks/flat/country-uncertainty-us-flat.md
---

# Country calendar uncertainty replay

 [← All flat growth benchmarks](/benchmarks/flat)

32 seeded scalar draws on the US/custom-event model, with interval reduction, portable JSON and fresh-process replay checks. Individual Python and Effect draws need not match.

## Input

[`benchmark/inputs/v5/country-basic-96.json`](https://github.com/jer-k/effect-prophet/blob/main/benchmark/inputs/v5/country-basic-96.json): 96 history rows and 4 forecast rows.

## Output

Largest difference: **0.00015** (0.0048% of the input's range).

Every forecast row from both libraries.

| Date | Effect Prophet | Python Prophet | Difference |
| --- | ---: | ---: | ---: |
| 2024-12-25 | 20.0010 | 20.0010 | 2.2e-6 |
| 2025-01-01 | 23.0356 | 23.0354 | 0.00015 |
| 2025-01-02 | 20.0010 | 20.0010 | 2.2e-6 |
| 2025-01-05 | 21.4447 | 21.4446 | 7.0e-5 |

## Timing

Median of all measured repetitions.

| Step | Effect Prophet | Python Prophet | Effect Prophet is |
| --- | ---: | ---: | --- |
| Convert input rows | 13 µs | 457 µs | 35× faster |
| Save model to JSON | 87 µs | 713 µs | 8.2× faster |
| Load model from JSON | 1.12 ms | 4.00 ms | 3.6× faster |
| Simulate uncertainty | 341 µs | 14.7 ms | Not compared: different work |
| First simulation in a new process | 191 ms | 1.65 s | Not compared: different work |
| Load and simulate in a new process | 165 ms | 1.61 s | Not compared: different work |

Some steps aren't compared, because the two libraries' public calls produce different outputs. Their times show what each call costs, not which library is faster.

* **Simulate uncertainty, First simulation in a new process, Load and simulate in a new process:** Python's `predict(vectorized=False)` also builds the point forecast and every component, while Effect Prophet's `predictUncertainty` returns only the ranges.

## Settings

The case declaration both libraries ran.

```json
{
  "kind": "map",
  "comparison": {
    "kind": "equivalent-objective",
    "evidenceId": "country-calendar-public-v1"
  },
  "configuration": {
    "growth": "flat",
    "countryHolidays": {
      "country": "US",
      "coverage": "reject-known-gaps",
      "priorScale": 10
    },
    "scaling": "absmax",
    "seasonalityMode": "additive",
    "holidaysMode": "additive",
    "changepoints": {
      "mode": "auto",
      "count": 25,
      "range": 0.8
    },
    "changepointPriorScale": 0.05,
    "seasonalities": [],
    "events": [
      {
        "name": "launch",
        "date": "2024-01-01",
        "lowerWindowDays": 0,
        "upperWindowDays": 0,
        "priorScale": 10
      },
      {
        "name": "launch",
        "date": "2024-01-05",
        "lowerWindowDays": 0,
        "upperWindowDays": 0,
        "priorScale": 10
      },
      {
        "name": "launch",
        "date": "2025-01-01",
        "lowerWindowDays": 0,
        "upperWindowDays": 0,
        "priorScale": 10
      },
      {
        "name": "launch",
        "date": "2025-01-05",
        "lowerWindowDays": 0,
        "upperWindowDays": 0,
        "priorScale": 10
      }
    ],
    "regressors": []
  },
  "pythonOptimizer": {
    "algorithm": "Auto",
    "maxIterations": 10000,
    "newtonFallback": true,
    "sigFigs": 12
  },
  "uncertainty": {
    "seed": 2024,
    "samples": 32,
    "intervalWidth": 0.8,
    "output": "intervals",
    "algorithm": "scalar-continuous-time",
    "evidence": "docs/validation/uncertainty.md#evidence-classes",
    "samplerMemoryLimitBytes": 67108864
  }
}
```