First 63 days, minmax scaling

match ← All flat growth benchmarks

The 96-day mixed-feature history cut to its first 63 days: the training window of one cross-validation fold.

Input

benchmark/inputs/v1/flat-mixed-components-prefix-63.json: 63 history rows and 7 forecast rows.

Output

Largest difference: 2.0e-7 (< 0.0001% of the input's range).

Every forecast row from both libraries.

Date	Effect Prophet	Python Prophet	Difference
2020-03-04	34.9410	34.9410	2.2e-9
2020-03-05	62.8734	62.8734	7.3e-8
2020-03-06	35.0019	35.0019	9.2e-8
2020-03-07	50.4492	50.4492	2.0e-7
2020-03-08	19.4386	19.4386	1.6e-7
2020-03-09	1.4810	1.4810	5.7e-8
2020-03-10	7.1798	7.1798	5.1e-9

Timing

Median of all measured repetitions.

Step	Effect Prophet	Python Prophet	Effect Prophet is
Fit	2.21 ms	7.61 ms	3.4× faster
Predict	178 µs	4.89 ms	27× faster
Save model to JSON	19 µs	606 µs	32× faster
Load model from JSON	226 µs	3.59 ms	16× faster
Load and predict in a new process	161 ms	1.46 s	9.1× faster

Settings

The case declaration both libraries ran.

{
  "kind": "map",
  "comparison": {
    "kind": "equivalent-objective",
    "evidenceId": "flat-growth-mixed-scaling-v1"
  },
  "configuration": {
    "growth": "flat",
    "scaling": "minmax",
    "seasonalityMode": "additive",
    "holidaysMode": "additive",
    "changepoints": {
      "mode": "auto",
      "count": 25,
      "range": 0.8
    },
    "changepointPriorScale": 0.05,
    "seasonalities": [
      {
        "name": "weekly-custom",
        "periodDays": 7,
        "fourierOrder": 2,
        "priorScale": 10,
        "conditionName": "active",
        "mode": "multiplicative"
      }
    ],
    "events": [
      {
        "name": "campaign",
        "date": "2020-01-22",
        "lowerWindowDays": 0,
        "upperWindowDays": 0,
        "priorScale": 10
      }
    ],
    "regressors": [
      {
        "name": "promotion",
        "priorScale": 10,
        "standardization": "never"
      }
    ]
  },
  "effectOptimizer": {
    "algorithm": "lbfgs",
    "maxIterations": 10000,
    "fallback": "none",
    "lbfgs": {
      "historySize": 5,
      "initAlpha": 0.001,
      "tolObj": 1e-12,
      "tolRelObj": 10000,
      "tolGrad": 1e-8,
      "tolRelGrad": 10000000,
      "tolParam": 1e-8
    }
  },
  "pythonOptimizer": {
    "algorithm": "LBFGS",
    "maxIterations": 10000,
    "newtonFallback": false
  }
}