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

# US holidays, additive

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

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

## Output

Largest difference: **1.5e-11** (< 0.0001% of the input's range).

Every forecast row from both libraries.

| Date | Effect Prophet | Python Prophet | Difference |
| --- | ---: | ---: | ---: |
| 2024-12-25 | 78.6444 | 78.6444 | 1.5e-11 |
| 2025-01-01 | 82.3589 | 82.3589 | 1.4e-11 |
| 2025-01-02 | 78.7702 | 78.7702 | 1.1e-11 |
| 2025-01-05 | 80.5727 | 80.5727 | 1.1e-11 |

## Timing

Median of all measured repetitions.

| Step | Effect Prophet | Python Prophet | Effect Prophet is |
| --- | ---: | ---: | --- |
| Convert input rows | 16 µs | 505 µs | 32× faster |
| Fit | 32.1 ms | 53.4 ms | 1.7× faster |
| Predict | 256 µs | 5.59 ms | 22× faster |
| Fit and predict | 31.4 ms | 60.2 ms | 1.9× faster |
| Convert, fit and predict | 31.4 ms | 59.8 ms | 1.9× faster |
| First forecast in a new process | 215 ms | 1.68 s | 7.8× faster |
| Save model to JSON | 157 µs | 723 µs | 4.6× faster |
| Load model from JSON | 1.13 ms | 4.01 ms | 3.6× faster |
| Load and predict in a new process | 170 ms | 1.62 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": "logistic",
    "countryHolidays": {
      "country": "US",
      "coverage": "reject-known-gaps",
      "priorScale": 10
    },
    "scaling": "absmax",
    "seasonalityMode": "additive",
    "holidaysMode": "additive",
    "changepoints": {
      "mode": "auto",
      "count": 2,
      "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": []
  },
  "effectOptimizer": {
    "algorithm": "auto",
    "maxIterations": 10000,
    "fallback": "newton",
    "lbfgs": {
      "historySize": 5,
      "initAlpha": 0.001,
      "tolObj": 1e-12,
      "tolRelObj": 10000,
      "tolGrad": 1e-8,
      "tolRelGrad": 10000000,
      "tolParam": 1e-8
    }
  },
  "pythonOptimizer": {
    "algorithm": "Auto",
    "maxIterations": 10000,
    "newtonFallback": true,
    "sigFigs": 12
  }
}
```