Samples with repeated timestamps

match ← All linear growth benchmarks

128 simulated futures from a model fitted on rows with repeated dates.

Input

benchmark/inputs/v1/training-duplicates.json: 96 history rows and 8 forecast rows.

Output

Largest difference: 5.4e-5 (0.0013% of the input's range).

Every forecast row from both libraries.

Date	Effect Prophet	Python Prophet	Difference
2020-01-25	16.2605	16.2604	5.4e-5
2020-01-26	16.4952	16.4952	5.2e-5
2020-01-27	16.7300	16.7300	4.9e-5
2020-01-28	16.9648	16.9648	4.7e-5
2020-01-29	17.1996	17.1995	4.4e-5
2020-01-30	17.4344	17.4343	4.2e-5
2020-01-31	17.6691	17.6691	4.0e-5
2020-02-01	17.9039	17.9039	3.7e-5

Timing

Median of all measured repetitions.

Step	Effect Prophet	Python Prophet	Effect Prophet is
Simulate uncertainty	471 µs	16.8 ms	Not compared: different work
Load and simulate in a new process	165 ms	1.45 s	Not compared: different work

Some steps aren't compared, because the two libraries' public calls produce different outputs. Their times show what each call costs, not which library is faster.

• Simulate uncertainty, Load and simulate in a new process: Python's predictive_samples(vectorized=False) and Effect Prophet's predictUncertainty are different public calls: Python's timing includes resetting NumPy's random generator and returns NumPy matrices, and the two don't draw the same random numbers.

Settings

The case declaration both libraries ran.

{
  "kind": "map",
  "comparison": {
    "kind": "equivalent-objective",
    "evidenceId": "linear-output-first-v4"
  },
  "configuration": {
    "growth": "linear",
    "scaling": "absmax",
    "seasonalityMode": "additive",
    "holidaysMode": "additive",
    "changepoints": {
      "mode": "auto",
      "count": 25,
      "range": 0.8
    },
    "changepointPriorScale": 0.05,
    "seasonalities": [],
    "events": [],
    "regressors": []
  },
  "effectOptimizer": {
    "algorithm": "auto",
    "maxIterations": 10000,
    "fallback": "newton",
    "lbfgs": {
      "historySize": 5,
      "initAlpha": 0.001,
      "tolObj": 1e-12,
      "tolRelObj": 10000,
      "tolGrad": 1e-8,
      "tolRelGrad": 10000000,
      "tolParam": 1e-8
    }
  },
  "pythonOptimizer": {
    "algorithm": "Auto",
    "maxIterations": 10000,
    "newtonFallback": true,
    "sigFigs": 12
  },
  "uncertainty": {
    "seed": 42,
    "samples": 128,
    "intervalWidth": 0.8,
    "output": "samples",
    "algorithm": "scalar-continuous-time",
    "evidence": "docs/validation/uncertainty.md",
    "samplerMemoryLimitBytes": 33554432
  }
}