US holidays, additive

match ← All logistic growth benchmarks

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: 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	14 µs	498 µs	35× faster
Fit	33.0 ms	53.3 ms	1.6× faster
Predict	245 µs	5.79 ms	24× faster
Fit and predict	31.5 ms	60.1 ms	1.9× faster
Convert, fit and predict	31.6 ms	59.8 ms	1.9× faster
First forecast in a new process	210 ms	1.65 s	7.9× faster
Save model to JSON	104 µs	716 µs	6.9× faster
Load model from JSON	934 µs	4.11 ms	4.4× faster
Load and predict in a new process	158 ms	1.58 s	10× faster

Settings

The case declaration both libraries ran.

{
  "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
  }
}