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

# US holidays, additive

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

96 daily rows from 2024, country holidays plus custom January events, including simultaneous activations. Prediction spans late 2024 and early 2025; fitting includes lookup, and saved models embed the country 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: **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 | 15 µs | 449 µs | 30× faster |
| Fit | 14.3 ms | 44.7 ms | 3.1× faster |
| Predict | 257 µs | 5.46 ms | 21× faster |
| Fit and predict | 14.0 ms | 50.3 ms | 3.6× faster |
| Convert, fit and predict | 13.7 ms | 51.6 ms | 3.8× faster |
| First forecast in a new process | 197 ms | 1.62 s | 8.2× faster |
| Save model to JSON | 126 µs | 699 µs | 5.5× faster |
| Load model from JSON | 1.15 ms | 3.90 ms | 3.4× faster |
| Load and predict in a new process | 163 ms | 1.58 s | 9.7× 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": "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
  }
}
```