---
url: >-
  https://jer-k.github.io/effect-prophet/benchmarks/logistic/logistic-implicit-floor-absmax.md
---

# Weekly pattern, absmax scaling

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

96 daily rows rising toward a ceiling of about 60, with a custom weekly pattern and one changepoint. Values are divided by their largest distance from the floor.

## Input

[`benchmark/inputs/v1/logistic-implicit-floor.json`](https://github.com/jer-k/effect-prophet/blob/main/benchmark/inputs/v1/logistic-implicit-floor.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 | 64.6830 | 64.6830 | 1.2e-11 |
| 2020-04-07 | 65.1412 | 65.1412 | 1.2e-11 |
| 2020-04-08 | 65.8511 | 65.8511 | 1.1e-11 |
| 2020-04-09 | 66.5504 | 66.5504 | 1.1e-11 |
| 2020-04-10 | 66.9796 | 66.9796 | 1.1e-11 |
| 2020-04-11 | 67.0695 | 67.0695 | 1.0e-11 |
| 2020-04-12 | 67.0029 | 67.0029 | 1.0e-11 |
| 2020-04-13 | 67.0800 | 67.0800 | 1.0e-11 |
| 2020-04-14 | 67.4829 | 67.4829 | 1.0e-11 |
| 2020-04-15 | 68.1389 | 68.1389 | 9.5e-12 |
| 2020-04-16 | 68.7859 | 68.7859 | 9.1e-12 |
| 2020-04-17 | 69.1642 | 69.1642 | 8.8e-12 |
| 2020-04-18 | 69.2049 | 69.2049 | 8.8e-12 |
| 2020-04-19 | 69.0904 | 69.0904 | 8.8e-12 |
| 2020-04-20 | 69.1213 | 69.1213 | 8.7e-12 |
| 2020-04-21 | 69.4793 | 69.4793 | 8.3e-12 |
| 2020-04-22 | 70.0919 | 70.0919 | 7.9e-12 |
| 2020-04-23 | 70.6969 | 70.6969 | 7.5e-12 |
| 2020-04-24 | 71.0346 | 71.0346 | 7.3e-12 |
| 2020-04-25 | 71.0359 | 71.0359 | 7.2e-12 |
| 2020-04-26 | 70.8835 | 70.8835 | 7.3e-12 |
| 2020-04-27 | 70.8777 | 70.8777 | 7.2e-12 |
| 2020-04-28 | 71.2002 | 71.2002 | 6.9e-12 |
| 2020-04-29 | 71.7786 | 71.7786 | 6.5e-12 |

## Timing

Median of all measured repetitions.

| Step | Effect Prophet | Python Prophet | Effect Prophet is |
| --- | ---: | ---: | --- |
| Convert input rows | 19 µs | 485 µs | 25× faster |
| Fit | 19.3 ms | 28.8 ms | 1.5× faster |
| Predict | 313 µs | 3.33 ms | 11× faster |
| Fit and predict | 18.9 ms | 32.0 ms | 1.7× faster |
| Convert, fit and predict | 18.7 ms | 32.6 ms | 1.7× faster |
| First forecast in a new process | 190 ms | 1.48 s | 7.8× faster |
| Save model to JSON | 34 µs | 448 µs | 13× faster |
| Load model from JSON | 134 µs | 2.40 ms | 18× faster |
| Load and predict in a new process | 159 ms | 1.47 s | 9.2× faster |

## Settings

The case declaration both libraries ran.

```json
{
  "kind": "map",
  "comparison": {
    "kind": "equivalent-objective",
    "evidenceId": "logistic-map-v1"
  },
  "configuration": {
    "growth": "logistic",
    "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": []
  },
  "pythonOptimizer": {
    "algorithm": "Auto",
    "maxIterations": 10000,
    "newtonFallback": true,
    "sigFigs": 12
  }
}
```