Samples for one future date
match ← All linear growth benchmarks128 raw simulated values for a single date after the history.
Input
benchmark/inputs/v1/linear-offset-scaling.json: 96 history rows and 24 forecast rows.
Output
Largest difference: 1.2e-11 (< 0.0001% of the input's range).
Every forecast row from both libraries.
| Date | Effect Prophet | Python Prophet | Difference |
|---|---|---|---|
| 2020-04-06 | 110.0819 | 110.0819 | 1.2e-11 |
Timing
Median of all measured repetitions.
| Step | Effect Prophet | Python Prophet | Effect Prophet is |
|---|---|---|---|
| Convert input rows | 14 µs | 462 µs | 32× faster |
| Save model to JSON | 20 µs | 428 µs | 21× faster |
| Load model from JSON | 149 µs | 2.24 ms | 15× faster |
| Simulate uncertainty | 208 µs | 17.2 ms | 83× faster |
| First simulation in a new process | 171 ms | 1.46 s | 8.6× faster |
| Load and simulate in a new process | 156 ms | 1.45 s | 9.3× faster |
Settings
The case declaration both libraries ran.
json
{
"kind": "map",
"comparison": {
"kind": "equivalent-objective",
"evidenceId": "linear-output-first-v4"
},
"configuration": {
"growth": "linear",
"scaling": "absmax",
"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": []
},
"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": 19,
"samples": 128,
"intervalWidth": 0.8,
"output": "samples",
"algorithm": "scalar-continuous-time",
"evidence": "docs/validation/uncertainty.md#evidence-classes",
"samplerMemoryLimitBytes": 67108864
}
}