Global prior scales with explicit overrides

match ← All linear growth benchmarks

The same global priors, but the seasonality, custom events and regressor each set a prior of 10, so only the US holidays take holidaysPriorScale.

Input

benchmark/inputs/v5/country-mixed-96.json: 96 history rows and 4 forecast rows.

Output

Largest difference: 9.3e-11 (< 0.0001% of the input's range).

Every forecast row from both libraries.

Date	Effect Prophet	Python Prophet	Difference
2024-12-25	21.8782	21.8782	9.2e-11
2025-01-01	23.1562	23.1562	7.6e-11
2025-01-02	20.7293	20.7293	9.3e-11
2025-01-05	20.8727	20.8727	8.2e-11

Timing

Median of all measured repetitions.

Step	Effect Prophet	Python Prophet	Effect Prophet is
Convert input rows	52 µs	534 µs	10× faster
Fit	5.79 ms	27.1 ms	4.7× faster
Predict	233 µs	6.34 ms	27× faster
Fit and predict	5.11 ms	33.9 ms	6.6× faster
Convert, fit and predict	5.02 ms	35.2 ms	7.0× faster
First forecast in a new process	182 ms	1.66 s	9.1× faster
Save model to JSON	138 µs	756 µs	5.5× faster
Load model from JSON	1.08 ms	4.40 ms	4.1× faster
Load and predict in a new process	170 ms	1.62 s	9.6× faster

Settings

The case declaration both libraries ran.

{
  "kind": "map",
  "comparison": {
    "kind": "equivalent-objective",
    "evidenceId": "country-calendar-public-v1"
  },
  "configuration": {
    "growth": "linear",
    "countryHolidays": {
      "country": "US",
      "coverage": "reject-known-gaps",
      "priorScale": 0.05
    },
    "scaling": "absmax",
    "seasonalityMode": "additive",
    "holidaysMode": "additive",
    "seasonalityPriorScale": 0.1,
    "holidaysPriorScale": 0.05,
    "changepoints": {
      "mode": "auto",
      "count": 2,
      "range": 0.8
    },
    "changepointPriorScale": 0.05,
    "seasonalities": [
      {
        "name": "weekly-active",
        "periodDays": 7,
        "fourierOrder": 1,
        "priorScale": 10,
        "conditionName": "active"
      }
    ],
    "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": [
      {
        "name": "promotion",
        "priorScale": 10,
        "standardization": "never"
      }
    ]
  },
  "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
  }
}