---
url: >-
  https://jer-k.github.io/effect-prophet/benchmarks/logistic/mcmc-logistic-explicit-floor-minmax.md
---

# Capacity, floor and a multiplicative regressor

 [← All logistic growth benchmarks](/benchmarks/logistic)

96 daily rows with changing capacities, an explicit floor, min-max scaling, weekly seasonality, a multiplicative regressor and two automatic changepoints. `mcmc_samples=300`.

## Input

[`benchmark/inputs/v1/logistic-explicit-floor.json`](https://github.com/jer-k/effect-prophet/blob/main/benchmark/inputs/v1/logistic-explicit-floor.json): 96 history rows and 24 forecast rows.

## Output

Largest difference: **0.0044** (0.0091% of the input's range).

Every forecast row from both libraries.

| Date | Effect Prophet | Python Prophet | Difference |
| --- | ---: | ---: | ---: |
| 2020-04-06 | 65.8320 | 65.8337 | 0.0017 |
| 2020-04-07 | 66.2473 | 66.2481 | 0.00078 |
| 2020-04-08 | 67.2872 | 67.2830 | 0.0042 |
| 2020-04-09 | 67.5700 | 67.5714 | 0.0014 |
| 2020-04-10 | 67.9574 | 67.9587 | 0.0013 |
| 2020-04-11 | 68.0044 | 68.0037 | 0.00072 |
| 2020-04-12 | 67.8983 | 67.8983 | 5.8e-5 |
| 2020-04-13 | 67.9422 | 67.9440 | 0.0018 |
| 2020-04-14 | 68.6967 | 68.6934 | 0.0033 |
| 2020-04-15 | 68.9348 | 68.9347 | 8.2e-5 |
| 2020-04-16 | 69.5493 | 69.5508 | 0.0015 |
| 2020-04-17 | 69.8956 | 69.8969 | 0.0014 |
| 2020-04-18 | 69.9027 | 69.9020 | 0.00069 |
| 2020-04-19 | 69.7578 | 69.7578 | 4.4e-5 |
| 2020-04-20 | 70.1568 | 70.1544 | 0.0024 |
| 2020-04-21 | 70.0982 | 70.0991 | 0.00084 |
| 2020-04-22 | 70.6851 | 70.6850 | 9.2e-5 |
| 2020-04-23 | 71.2655 | 71.2669 | 0.0014 |
| 2020-04-24 | 71.5787 | 71.5801 | 0.0013 |
| 2020-04-25 | 71.5539 | 71.5531 | 0.00072 |
| 2020-04-26 | 71.7785 | 71.7741 | 0.0044 |
| 2020-04-27 | 71.3547 | 71.3564 | 0.0018 |
| 2020-04-28 | 71.6599 | 71.6607 | 0.00080 |
| 2020-04-29 | 72.2189 | 72.2188 | 0.00014 |

## Timing

Median of all measured repetitions.

| Step | Effect Prophet | Python Prophet | Effect Prophet is |
| --- | ---: | ---: | --- |
| Fit | 1.83 s | 452 ms | 4.0× slower |
| Predict | 396 µs | 3.79 ms | Not compared: different work |
| Save model to JSON | 269 µs | 12.5 ms | 46× faster |
| Load model from JSON | 2.87 ms | 8.01 ms | 2.8× faster |
| Simulate uncertainty | 2.91 ms | 210 ms | Not compared: different work |
| Load and simulate in a new process | 186 ms | 1.65 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.

* **Predict:** Python's `predict` on a posterior also computes every component's range over the draws, while Effect Prophet's `predict` returns point forecasts and leaves ranges to `predictWithIntervals`.
* **Simulate uncertainty, Load and simulate in a new process:** Python's `predict(vectorized=False)` also builds the point forecast and every component, while Effect Prophet's `predictUncertainty` returns only the ranges.

## Settings

The case declaration both libraries ran.

```json
{
  "kind": "map",
  "comparison": {
    "kind": "equivalent-posterior",
    "evidenceId": "posterior-nuts-v1"
  },
  "configuration": {
    "growth": "logistic",
    "scaling": "minmax",
    "seasonalityMode": "additive",
    "holidaysMode": "additive",
    "changepoints": {
      "mode": "auto",
      "count": 2,
      "range": 0.8
    },
    "changepointPriorScale": 0.2,
    "seasonalities": [
      {
        "name": "weekly-custom",
        "periodDays": 7,
        "fourierOrder": 2,
        "priorScale": 10
      }
    ],
    "events": [],
    "regressors": [
      {
        "name": "promotion",
        "priorScale": 10,
        "standardization": "never",
        "mode": "multiplicative"
      }
    ]
  },
  "pythonOptimizer": {
    "algorithm": "Auto",
    "maxIterations": 10000,
    "newtonFallback": true,
    "sigFigs": 12
  },
  "uncertainty": {
    "seed": 19,
    "samples": 1000,
    "intervalWidth": 0.8,
    "output": "intervals",
    "algorithm": "scalar-continuous-time",
    "evidence": "docs/validation/uncertainty.md#evidence-classes",
    "samplerMemoryLimitBytes": 67108864
  },
  "mcmc": {
    "samples": 300,
    "seed": 19,
    "pointWidthFraction": 0.15
  }
}
```