First 225 days, minmax scaling

match ← All flat growth benchmarks

The 256-day mixed-feature history cut to its first 225 days: the training window of one cross-validation fold.

Input

benchmark/inputs/v1/flat-mixed-components-large-prefix-225.json: 225 history rows and 7 forecast rows.

Output

Largest difference: 2.2e-7 (< 0.0001% of the input's range).

Every forecast row from both libraries.

Date	Effect Prophet	Python Prophet	Difference
2020-08-13	35.0005	35.0005	7.8e-8
2020-08-14	69.7311	69.7311	9.2e-8
2020-08-15	50.4317	50.4317	2.8e-8
2020-08-16	19.5445	19.5445	6.7e-8
2020-08-17	0.2953	0.2953	2.9e-9
2020-08-18	36.1993	36.1993	9.2e-8
2020-08-19	34.9891	34.9891	2.2e-7

Timing

Median of all measured repetitions.

Step	Effect Prophet	Python Prophet	Effect Prophet is
Fit	3.17 ms	8.79 ms	2.8× faster
Predict	136 µs	4.61 ms	34× faster
Save model to JSON	19 µs	780 µs	42× faster
Load model from JSON	178 µs	3.78 ms	21× faster
Load and predict in a new process	156 ms	1.42 s	9.0× 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
  }
}