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

# Deliberately empty country

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

Bouvet Island's selected provider calendar has no columns; the saved model still retains country selection, coverage policy and the embedded snapshot.

## 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: **1.1e-11** (< 0.0001% of the input's range).

Every forecast row from both libraries.

| Date | Effect Prophet | Python Prophet | Difference |
| --- | ---: | ---: | ---: |
| 2024-12-25 | 20.0473 | 20.0473 | 1.1e-11 |
| 2025-01-01 | 20.0473 | 20.0473 | 1.1e-11 |
| 2025-01-02 | 20.0473 | 20.0473 | 1.1e-11 |
| 2025-01-05 | 20.0473 | 20.0473 | 1.1e-11 |

## Timing

Median of all measured repetitions.

| Step | Effect Prophet | Python Prophet | Effect Prophet is |
| --- | ---: | ---: | --- |
| Convert input rows | 13 µs | 460 µs | 36× faster |
| Fit | 2.53 ms | 9.29 ms | 3.7× faster |
| Predict | 186 µs | 3.05 ms | 16× faster |
| Fit and predict | 1.46 ms | 12.7 ms | 8.7× faster |
| Convert, fit and predict | 1.35 ms | 13.5 ms | 10× faster |
| First forecast in a new process | 177 ms | 1.60 s | 9.0× faster |
| Save model to JSON | 42 µs | 448 µs | 11× faster |
| Load model from JSON | 224 µs | 2.04 ms | 9.1× faster |
| Load and predict in a new process | 165 ms | 1.57 s | 9.5× faster |

## Settings

The case declaration both libraries ran.

```json
{
  "kind": "map",
  "comparison": {
    "kind": "equivalent-objective",
    "evidenceId": "country-calendar-public-v1"
  },
  "configuration": {
    "growth": "flat",
    "countryHolidays": {
      "country": "BV",
      "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": [],
    "regressors": []
  },
  "pythonOptimizer": {
    "algorithm": "Auto",
    "maxIterations": 10000,
    "newtonFallback": true,
    "sigFigs": 12
  }
}
```