Defaults, ranges with a weekly pattern

match ← All flat growth benchmarks

80% ranges from 1,000 simulated paths on every past and future date. A flat trend never changes, so the ranges come from noise alone.

Input

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

Output

Largest difference: 1.1e-10 (< 0.0001% of the input's range).

Every forecast row from both libraries.

Date	Effect Prophet	Python Prophet	Difference
2020-01-01	35.0025	35.0025	1.0e-12
2020-01-02	36.0968	36.0968	8.4e-11
2020-01-03	36.3663	36.3663	1.1e-10
2020-01-04	35.6079	35.6079	4.8e-11
2020-01-05	34.3921	34.3921	4.7e-11
2020-01-06	33.6334	33.6334	1.0e-10
2020-01-07	33.9115	33.9115	8.2e-11
2020-01-08	35.0025	35.0025	1.0e-12
2020-01-09	36.0968	36.0968	8.4e-11
2020-01-10	36.3663	36.3663	1.1e-10
2020-01-11	35.6079	35.6079	4.8e-11
2020-01-12	34.3921	34.3921	4.7e-11
2020-01-13	33.6334	33.6334	1.0e-10
2020-01-14	33.9115	33.9115	8.2e-11
2020-01-15	35.0025	35.0025	1.0e-12
2020-01-16	36.0968	36.0968	8.4e-11
2020-01-17	36.3663	36.3663	1.1e-10
2020-01-18	35.6079	35.6079	4.8e-11
2020-01-19	34.3921	34.3921	4.7e-11
2020-01-20	33.6334	33.6334	1.0e-10
2020-01-21	33.9115	33.9115	8.2e-11
2020-01-22	35.0025	35.0025	1.0e-12
2020-01-23	36.0968	36.0968	8.4e-11
2020-01-24	36.3663	36.3663	1.1e-10
2020-01-25	35.6079	35.6079	4.8e-11
2020-01-26	34.3921	34.3921	4.7e-11
2020-01-27	33.6334	33.6334	1.0e-10
2020-01-28	33.9115	33.9115	8.2e-11
2020-01-29	35.0025	35.0025	1.0e-12
2020-01-30	36.0968	36.0968	8.4e-11
2020-01-31	36.3663	36.3663	1.1e-10
2020-02-01	35.6079	35.6079	4.8e-11
2020-02-02	34.3921	34.3921	4.7e-11
2020-02-03	33.6334	33.6334	1.0e-10
2020-02-04	33.9115	33.9115	8.2e-11
2020-02-05	35.0025	35.0025	1.0e-12
2020-02-06	36.0968	36.0968	8.4e-11
2020-02-07	36.3663	36.3663	1.1e-10
2020-02-08	35.6079	35.6079	4.8e-11
2020-02-09	34.3921	34.3921	4.7e-11
2020-02-10	33.6334	33.6334	1.0e-10
2020-02-11	33.9115	33.9115	8.2e-11
2020-02-12	35.0025	35.0025	1.0e-12
2020-02-13	36.0968	36.0968	8.4e-11
2020-02-14	36.3663	36.3663	1.1e-10
2020-02-15	35.6079	35.6079	4.8e-11
2020-02-16	34.3921	34.3921	4.7e-11
2020-02-17	33.6334	33.6334	1.0e-10
2020-02-18	33.9115	33.9115	8.2e-11
2020-02-19	35.0025	35.0025	1.0e-12
2020-02-20	36.0968	36.0968	8.4e-11
2020-02-21	36.3663	36.3663	1.1e-10
2020-02-22	35.6079	35.6079	4.8e-11
2020-02-23	34.3921	34.3921	4.7e-11
2020-02-24	33.6334	33.6334	1.0e-10
2020-02-25	33.9115	33.9115	8.2e-11
2020-02-26	35.0025	35.0025	1.0e-12
2020-02-27	36.0968	36.0968	8.4e-11
2020-02-28	36.3663	36.3663	1.1e-10
2020-02-29	35.6079	35.6079	4.8e-11
2020-03-01	34.3921	34.3921	4.7e-11
2020-03-02	33.6334	33.6334	1.0e-10
2020-03-03	33.9115	33.9115	8.2e-11
2020-03-04	35.0025	35.0025	1.0e-12
2020-03-05	36.0968	36.0968	8.4e-11
2020-03-06	36.3663	36.3663	1.1e-10
2020-03-07	35.6079	35.6079	4.8e-11
2020-03-08	34.3921	34.3921	4.7e-11
2020-03-09	33.6334	33.6334	1.0e-10
2020-03-10	33.9115	33.9115	8.2e-11
2020-03-11	35.0025	35.0025	1.0e-12
2020-03-12	36.0968	36.0968	8.4e-11
2020-03-13	36.3663	36.3663	1.1e-10
2020-03-14	35.6079	35.6079	4.8e-11
2020-03-15	34.3921	34.3921	4.7e-11
2020-03-16	33.6334	33.6334	1.0e-10
2020-03-17	33.9115	33.9115	8.2e-11
2020-03-18	35.0025	35.0025	1.0e-12
2020-03-19	36.0968	36.0968	8.4e-11
2020-03-20	36.3663	36.3663	1.1e-10
2020-03-21	35.6079	35.6079	4.8e-11
2020-03-22	34.3921	34.3921	4.7e-11
2020-03-23	33.6334	33.6334	1.0e-10
2020-03-24	33.9115	33.9115	8.2e-11
2020-03-25	35.0025	35.0025	1.0e-12
2020-03-26	36.0968	36.0968	8.4e-11
2020-03-27	36.3663	36.3663	1.1e-10
2020-03-28	35.6079	35.6079	4.8e-11
2020-03-29	34.3921	34.3921	4.7e-11
2020-03-30	33.6334	33.6334	1.0e-10
2020-03-31	33.9115	33.9115	8.2e-11
2020-04-01	35.0025	35.0025	1.0e-12
2020-04-02	36.0968	36.0968	8.4e-11
2020-04-03	36.3663	36.3663	1.1e-10
2020-04-04	35.6079	35.6079	4.8e-11
2020-04-05	34.3921	34.3921	4.7e-11
2020-04-06	33.6334	33.6334	1.0e-10
2020-04-07	33.9115	33.9115	8.2e-11
2020-04-08	35.0025	35.0025	1.0e-12
2020-04-09	36.0968	36.0968	8.4e-11
2020-04-10	36.3663	36.3663	1.1e-10
2020-04-11	35.6079	35.6079	4.8e-11
2020-04-12	34.3921	34.3921	4.7e-11
2020-04-13	33.6334	33.6334	1.0e-10
2020-04-14	33.9115	33.9115	8.2e-11
2020-04-15	35.0025	35.0025	1.0e-12
2020-04-16	36.0968	36.0968	8.4e-11
2020-04-17	36.3663	36.3663	1.1e-10
2020-04-18	35.6079	35.6079	4.8e-11
2020-04-19	34.3921	34.3921	4.7e-11
2020-04-20	33.6334	33.6334	1.0e-10
2020-04-21	33.9115	33.9115	8.2e-11
2020-04-22	35.0025	35.0025	1.0e-12
2020-04-23	36.0968	36.0968	8.4e-11
2020-04-24	36.3663	36.3663	1.1e-10
2020-04-25	35.6079	35.6079	4.8e-11
2020-04-26	34.3921	34.3921	4.7e-11
2020-04-27	33.6334	33.6334	1.0e-10
2020-04-28	33.9115	33.9115	8.2e-11
2020-04-29	35.0025	35.0025	1.0e-12

Timing

Median of all measured repetitions.

Step	Effect Prophet	Python Prophet	Effect Prophet is
Convert input rows	28 µs	513 µs	18× faster
Save model to JSON	22 µs	455 µs	21× faster
Load model from JSON	171 µs	2.13 ms	12× faster
Simulate uncertainty	4.48 ms	125 ms	28× faster
First simulation in a new process	297 ms	1.95 s	6.6× faster
Load and simulate in a new process	165 ms	1.57 s	9.5× faster

Settings

The case declaration both libraries ran.

{
  "kind": "map",
  "comparison": {
    "kind": "equivalent-objective",
    "evidenceId": "flat-growth-reduced-v1"
  },
  "fitRequest": "growth-only",
  "configuration": {
    "growth": "flat",
    "scaling": "absmax",
    "seasonalityMode": "additive",
    "holidaysMode": "additive",
    "changepoints": {
      "mode": "auto",
      "count": 25,
      "range": 0.8
    },
    "changepointPriorScale": 0.05,
    "seasonalities": [
      {
        "name": "weekly",
        "periodDays": 7,
        "fourierOrder": 3,
        "priorScale": 10
      }
    ],
    "events": [],
    "regressors": []
  },
  "pythonOptimizer": {
    "algorithm": "Auto",
    "maxIterations": 10000,
    "newtonFallback": true
  },
  "uncertainty": {
    "seed": 19,
    "samples": 1000,
    "intervalWidth": 0.8,
    "output": "intervals",
    "algorithm": "scalar-continuous-time",
    "evidence": "docs/validation/uncertainty.md#evidence-classes",
    "samplerMemoryLimitBytes": 67108864
  }
}