Every row on the same date, different values

match ← All flat growth benchmarks

Three rows sharing one timestamp. A flat trend needs no time range, so both libraries fit it.

Input

benchmark/inputs/v1/training-zero-span-varied.json: 3 history rows and 2 forecast rows.

Output

Largest difference: 0.00029 (0.0059% of the input's range).

Every forecast row from both libraries.

Date	Effect Prophet	Python Prophet	Difference
2020-01-01	4.3286	4.3289	0.00029
2020-01-02	4.3286	4.3289	0.00029

Timing

Median of all measured repetitions.

Step	Effect Prophet	Python Prophet	Effect Prophet is
Fit	888 µs	5.74 ms	6.5× faster
Predict	167 µs	2.68 ms	16× faster
Save model to JSON	22 µs	323 µs	14× faster
Load model from JSON	160 µs	1.89 ms	12× faster
Load and predict in a new process	156 ms	1.40 s	9.0× faster

Settings

The case declaration both libraries ran.

{
  "kind": "map",
  "comparison": {
    "kind": "equivalent-objective",
    "evidenceId": "training-row-map-v1"
  },
  "configuration": {
    "growth": "flat",
    "scaling": "absmax",
    "seasonalityMode": "additive",
    "holidaysMode": "additive",
    "changepoints": {
      "mode": "explicit",
      "timestamps": []
    },
    "changepointPriorScale": 0.05,
    "seasonalities": [],
    "events": [],
    "regressors": []
  },
  "pythonOptimizer": {
    "algorithm": "Auto",
    "maxIterations": 10000,
    "newtonFallback": true,
    "sigFigs": 12
  }
}