Perfectly constant, minmax scaling

match ← All flat growth benchmarks

The same identical values, where minmax scaling has no range to divide by.

Input

benchmark/inputs/v1/flat-constant.json: 96 history rows and 24 forecast rows.

Output

Largest difference: 0.

Every forecast row from both libraries.

Date	Effect Prophet	Python Prophet	Difference
2020-04-06	35.0000	35.0000	0
2020-04-07	35.0000	35.0000	0
2020-04-08	35.0000	35.0000	0
2020-04-09	35.0000	35.0000	0
2020-04-10	35.0000	35.0000	0
2020-04-11	35.0000	35.0000	0
2020-04-12	35.0000	35.0000	0
2020-04-13	35.0000	35.0000	0
2020-04-14	35.0000	35.0000	0
2020-04-15	35.0000	35.0000	0
2020-04-16	35.0000	35.0000	0
2020-04-17	35.0000	35.0000	0
2020-04-18	35.0000	35.0000	0
2020-04-19	35.0000	35.0000	0
2020-04-20	35.0000	35.0000	0
2020-04-21	35.0000	35.0000	0
2020-04-22	35.0000	35.0000	0
2020-04-23	35.0000	35.0000	0
2020-04-24	35.0000	35.0000	0
2020-04-25	35.0000	35.0000	0
2020-04-26	35.0000	35.0000	0
2020-04-27	35.0000	35.0000	0
2020-04-28	35.0000	35.0000	0
2020-04-29	35.0000	35.0000	0

Timing

Median of all measured repetitions.

Step	Effect Prophet	Python Prophet	Effect Prophet is
Fit	1.16 ms	3.03 ms	2.6× faster
Predict	212 µs	2.59 ms	12× faster
Save model to JSON	18 µs	397 µs	23× faster
Load model from JSON	105 µs	2.06 ms	20× faster
Load and predict in a new process	151 ms	1.45 s	9.6× faster

Settings

The case declaration both libraries ran.

{
  "kind": "map",
  "comparison": {
    "kind": "equivalent-objective",
    "evidenceId": "flat-growth-reduced-v1"
  },
  "configuration": {
    "growth": "flat",
    "scaling": "minmax",
    "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
  }
}