No-calendar empty control

match ← All flat growth benchmarks

The same history and forecast rows as the empty-country case, with no events or embedded country data.

Input

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

Output

Largest difference: 1.1e-11 (< 0.0001% of the input's range).

Every forecast row from both libraries.

Date	Effect Prophet	Python Prophet	Difference
2024-12-25	20.0473	20.0473	1.1e-11
2025-01-01	20.0473	20.0473	1.1e-11
2025-01-02	20.0473	20.0473	1.1e-11
2025-01-05	20.0473	20.0473	1.1e-11

Timing

Median of all measured repetitions.

Step	Effect Prophet	Python Prophet	Effect Prophet is
Convert input rows	14 µs	474 µs	33× faster
Fit	1.77 ms	8.37 ms	4.7× faster
Predict	118 µs	2.60 ms	22× faster
Fit and predict	1.43 ms	10.9 ms	7.6× faster
Convert, fit and predict	1.47 ms	11.6 ms	7.9× faster
First forecast in a new process	183 ms	1.45 s	7.9× faster
Save model to JSON	20 µs	462 µs	24× faster
Load model from JSON	131 µs	2.08 ms	16× faster
Load and predict in a new process	158 ms	1.49 s	9.4× faster

Settings

The case declaration both libraries ran.

{
  "kind": "map",
  "comparison": {
    "kind": "equivalent-objective",
    "evidenceId": "country-calendar-public-v1"
  },
  "configuration": {
    "growth": "flat",
    "scaling": "absmax",
    "seasonalityMode": "additive",
    "holidaysMode": "additive",
    "changepoints": {
      "mode": "auto",
      "count": 25,
      "range": 0.8
    },
    "changepointPriorScale": 0.05,
    "seasonalities": [],
    "events": [],
    "regressors": []
  },
  "pythonOptimizer": {
    "algorithm": "Auto",
    "maxIterations": 10000,
    "newtonFallback": true,
    "sigFigs": 12
  }
}