Weekly pattern, minmax scaling

match ← All logistic growth benchmarks

96 daily rows rising toward a ceiling of about 60, with a custom weekly pattern and one changepoint. Values are scaled between the lowest floor and the highest ceiling.

Input

benchmark/inputs/v1/logistic-implicit-floor.json: 96 history rows and 24 forecast rows.

Output

Largest difference: 1.9e-11 (< 0.0001% of the input's range).

Every forecast row from both libraries.

Date	Effect Prophet	Python Prophet	Difference
2020-04-06	64.8977	64.8977	1.9e-11
2020-04-07	65.3452	65.3452	1.9e-11
2020-04-08	65.9695	65.9695	1.9e-11
2020-04-09	66.6587	66.6587	1.9e-11
2020-04-10	67.1452	67.1452	1.8e-11
2020-04-11	67.2451	67.2451	1.8e-11
2020-04-12	67.1872	67.1872	1.7e-11
2020-04-13	67.3187	67.3187	1.7e-11
2020-04-14	67.7078	67.7078	1.6e-11
2020-04-15	68.2754	68.2754	1.6e-11
2020-04-16	68.9095	68.9095	1.6e-11
2020-04-17	69.3426	69.3426	1.6e-11
2020-04-18	69.3907	69.3907	1.5e-11
2020-04-19	69.2826	69.2826	1.5e-11
2020-04-20	69.3655	69.3655	1.4e-11
2020-04-21	69.7075	69.7075	1.4e-11
2020-04-22	70.2296	70.2296	1.3e-11
2020-04-23	70.8197	70.8197	1.3e-11
2020-04-24	71.2103	71.2103	1.3e-11
2020-04-25	71.2173	71.2173	1.3e-11
2020-04-26	71.0696	71.0696	1.2e-11
2020-04-27	71.1143	71.1143	1.1e-11
2020-04-28	71.4195	71.4195	1.1e-11
2020-04-29	71.9060	71.9060	1.1e-11

Timing

Median of all measured repetitions.

Step	Effect Prophet	Python Prophet	Effect Prophet is
Convert input rows	18 µs	486 µs	27× faster
Fit	21.4 ms	32.0 ms	1.5× faster
Predict	235 µs	3.17 ms	13× faster
Fit and predict	20.5 ms	34.5 ms	1.7× faster
Convert, fit and predict	20.5 ms	37.8 ms	1.8× faster
First forecast in a new process	185 ms	1.45 s	7.8× faster
Save model to JSON	33 µs	435 µs	13× faster
Load model from JSON	82 µs	2.23 ms	27× faster
Load and predict in a new process	154 ms	1.41 s	9.2× faster

Settings

The case declaration both libraries ran.

{
  "kind": "map",
  "comparison": {
    "kind": "equivalent-objective",
    "evidenceId": "logistic-map-v1"
  },
  "configuration": {
    "growth": "logistic",
    "scaling": "minmax",
    "seasonalityMode": "additive",
    "holidaysMode": "additive",
    "changepoints": {
      "mode": "explicit",
      "timestamps": [
        "2020-02-10T00:00:00.000Z"
      ]
    },
    "changepointPriorScale": 0.2,
    "seasonalities": [
      {
        "name": "weekly-custom",
        "periodDays": 7,
        "fourierOrder": 2,
        "priorScale": 10
      }
    ],
    "events": [],
    "regressors": []
  },
  "pythonOptimizer": {
    "algorithm": "Auto",
    "maxIterations": 10000,
    "newtonFallback": true,
    "sigFigs": 12
  }
}