Every row on the same date, same value

match ← All flat growth benchmarks

Three identical rows on one timestamp.

Input

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

Output

Largest difference: 0.

Every forecast row from both libraries.

Date	Effect Prophet	Python Prophet	Difference
2020-01-01	2.0000	2.0000	0
2020-01-02	2.0000	2.0000	0

Timing

Median of all measured repetitions.

Step	Effect Prophet	Python Prophet	Effect Prophet is
Fit	530 µs	2.86 ms	5.4× faster
Predict	183 µs	2.61 ms	14× faster
Save model to JSON	24 µs	328 µs	14× faster
Load model from JSON	147 µs	1.89 ms	13× faster
Load and predict in a new process	153 ms	1.47 s	9.6× 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
  }
}