---
url: >-
  https://jer-k.github.io/effect-prophet/benchmarks/logistic/logistic-reconcile-weekly.md
---

# Weekly pattern

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

A custom weekly seasonality on the basic curve.

## Input

[`benchmark/inputs/v2/logistic-weekly-96.json`](https://github.com/jer-k/effect-prophet/blob/main/benchmark/inputs/v2/logistic-weekly-96.json): 96 history rows and 120 forecast rows.

## Output

Largest difference: **8.7e-8** (< 0.0001% of the input's range).

Every forecast row from both libraries.

| Date | Effect Prophet | Python Prophet | Difference |
| --- | ---: | ---: | ---: |
| 2020-01-01 | 14.1889 | 14.1889 | 5.2e-8 |
| 2020-01-02 | 15.3462 | 15.3462 | 7.8e-8 |
| 2020-01-03 | 16.0484 | 16.0484 | 8.5e-8 |
| 2020-01-04 | 16.1800 | 16.1800 | 6.6e-8 |
| 2020-01-05 | 16.0674 | 16.0674 | 3.6e-8 |
| 2020-01-06 | 16.2332 | 16.2332 | 2.0e-8 |
| 2020-01-07 | 17.0127 | 17.0127 | 2.6e-8 |
| 2020-01-08 | 18.2694 | 18.2694 | 4.7e-8 |
| 2020-01-09 | 19.5475 | 19.5475 | 7.3e-8 |
| 2020-01-10 | 20.3719 | 20.3719 | 7.8e-8 |
| 2020-01-11 | 20.6271 | 20.6271 | 5.9e-8 |
| 2020-01-12 | 20.6392 | 20.6392 | 2.8e-8 |
| 2020-01-13 | 20.9308 | 20.9308 | 1.1e-8 |
| 2020-01-14 | 21.8369 | 21.8369 | 1.6e-8 |
| 2020-01-15 | 23.2209 | 23.2209 | 3.6e-8 |
| 2020-01-16 | 24.6267 | 24.6267 | 6.1e-8 |
| 2020-01-17 | 25.5792 | 25.5792 | 6.6e-8 |
| 2020-01-18 | 25.9624 | 25.9624 | 4.5e-8 |
| 2020-01-19 | 26.1023 | 26.1023 | 1.3e-8 |
| 2020-01-20 | 26.5213 | 26.5213 | 4.6e-9 |
| 2020-01-21 | 27.5541 | 27.5541 | 4.3e-10 |
| 2020-01-22 | 29.0639 | 29.0639 | 1.8e-8 |
| 2020-01-23 | 30.5943 | 30.5943 | 4.2e-8 |
| 2020-01-24 | 31.6698 | 31.6698 | 4.6e-8 |
| 2020-01-25 | 32.1744 | 32.1744 | 2.5e-8 |
| 2020-01-26 | 32.4336 | 32.4336 | 8.4e-9 |
| 2020-01-27 | 32.9696 | 32.9696 | 2.7e-8 |
| 2020-01-28 | 34.1168 | 34.1168 | 2.4e-8 |
| 2020-01-29 | 35.7380 | 35.7380 | 6.3e-9 |
| 2020-01-30 | 37.3766 | 37.3766 | 1.7e-8 |
| 2020-01-31 | 38.5569 | 38.5569 | 2.0e-8 |
| 2020-02-01 | 39.1625 | 39.1625 | 2.1e-9 |
| 2020-02-02 | 39.5186 | 39.5186 | 3.6e-8 |
| 2020-02-03 | 40.1471 | 40.1471 | 5.5e-8 |
| 2020-02-04 | 41.3823 | 41.3823 | 5.2e-8 |
| 2020-02-05 | 43.0867 | 43.0867 | 3.5e-8 |
| 2020-02-06 | 44.8033 | 44.8033 | 1.3e-8 |
| 2020-02-07 | 46.0562 | 46.0562 | 1.0e-8 |
| 2020-02-08 | 46.7288 | 46.7288 | 3.2e-8 |
| 2020-02-09 | 47.1467 | 47.1467 | 5.9e-8 |
| 2020-02-10 | 47.8309 | 47.8309 | 7.1e-8 |
| 2020-02-11 | 49.1157 | 49.1157 | 6.1e-8 |
| 2020-02-12 | 50.8634 | 50.8634 | 3.6e-8 |
| 2020-02-13 | 52.6169 | 52.6169 | 5.7e-9 |
| 2020-02-14 | 53.9002 | 53.9002 | 4.6e-9 |
| 2020-02-15 | 54.5967 | 54.5967 | 1.0e-8 |
| 2020-02-16 | 55.0313 | 55.0313 | 3.6e-8 |
| 2020-02-17 | 55.7256 | 55.7256 | 4.8e-8 |
| 2020-02-18 | 57.0137 | 57.0137 | 3.8e-8 |
| 2020-02-19 | 58.7581 | 58.7581 | 1.3e-8 |
| 2020-02-20 | 60.5016 | 60.5016 | 1.7e-8 |
| 2020-02-21 | 61.7683 | 61.7683 | 2.8e-8 |
| 2020-02-22 | 62.4415 | 62.4415 | 1.3e-8 |
| 2020-02-23 | 62.8466 | 62.8466 | 1.3e-8 |
| 2020-02-24 | 63.5049 | 63.5049 | 2.5e-8 |
| 2020-02-25 | 64.7510 | 64.7510 | 1.5e-8 |
| 2020-02-26 | 66.4472 | 66.4472 | 9.0e-9 |
| 2020-02-27 | 68.1367 | 68.1367 | 3.9e-8 |
| 2020-02-28 | 69.3438 | 69.3438 | 4.9e-8 |
| 2020-02-29 | 69.9520 | 69.9520 | 3.4e-8 |
| 2020-03-01 | 70.2868 | 70.2868 | 6.9e-9 |
| 2020-03-02 | 70.8700 | 70.8700 | 5.6e-9 |
| 2020-03-03 | 72.0361 | 72.0361 | 3.8e-9 |
| 2020-03-04 | 73.6479 | 73.6479 | 2.8e-8 |
| 2020-03-05 | 75.2489 | 75.2489 | 5.7e-8 |
| 2020-03-06 | 76.3636 | 76.3636 | 6.6e-8 |
| 2020-03-07 | 76.8757 | 76.8757 | 5.0e-8 |
| 2020-03-08 | 77.1111 | 77.1111 | 2.3e-8 |
| 2020-03-09 | 77.5918 | 77.5918 | 9.4e-9 |
| 2020-03-10 | 78.6526 | 78.6526 | 1.8e-8 |
| 2020-03-11 | 80.1566 | 80.1566 | 4.1e-8 |
| 2020-03-12 | 81.6476 | 81.6476 | 7.0e-8 |
| 2020-03-13 | 82.6503 | 82.6503 | 7.8e-8 |
| 2020-03-14 | 83.0488 | 83.0488 | 6.1e-8 |
| 2020-03-15 | 83.1691 | 83.1691 | 3.3e-8 |
| 2020-03-16 | 83.5336 | 83.5336 | 1.9e-8 |
| 2020-03-17 | 84.4773 | 84.4773 | 2.7e-8 |
| 2020-03-18 | 85.8636 | 85.8636 | 4.9e-8 |
| 2020-03-19 | 87.2364 | 87.2364 | 7.7e-8 |
| 2020-03-20 | 88.1207 | 88.1207 | 8.5e-8 |
| 2020-03-21 | 88.4008 | 88.4008 | 6.7e-8 |
| 2020-03-22 | 88.4030 | 88.4030 | 3.8e-8 |
| 2020-03-23 | 88.6497 | 88.6497 | 2.4e-8 |
| 2020-03-24 | 89.4762 | 89.4762 | 3.1e-8 |
| 2020-03-25 | 90.7461 | 90.7461 | 5.2e-8 |
| 2020-03-26 | 92.0034 | 92.0034 | 8.0e-8 |
| 2020-03-27 | 92.7733 | 92.7733 | 8.7e-8 |
| 2020-03-28 | 92.9402 | 92.9402 | 6.9e-8 |
| 2020-03-29 | 92.8304 | 92.8304 | 3.9e-8 |
| 2020-03-30 | 92.9667 | 92.9667 | 2.4e-8 |
| 2020-03-31 | 93.6843 | 93.6843 | 3.1e-8 |
| 2020-04-01 | 94.8470 | 94.8470 | 5.2e-8 |
| 2020-04-02 | 95.9987 | 95.9987 | 7.8e-8 |
| 2020-04-03 | 96.6649 | 96.6649 | 8.5e-8 |
| 2020-04-04 | 96.7300 | 96.7300 | 6.7e-8 |
| 2020-04-05 | 96.5203 | 96.5203 | 3.7e-8 |
| 2020-04-06 | 96.5587 | 96.5587 | 2.1e-8 |
| 2020-04-07 | 97.1805 | 97.1805 | 2.8e-8 |
| 2020-04-08 | 98.2493 | 98.2493 | 4.8e-8 |
| 2020-04-09 | 99.3093 | 99.3093 | 7.5e-8 |
| 2020-04-10 | 99.8859 | 99.8859 | 8.1e-8 |
| 2020-04-11 | 99.8635 | 99.8635 | 6.3e-8 |
| 2020-04-12 | 99.5684 | 99.5684 | 3.3e-8 |
| 2020-04-13 | 99.5236 | 99.5236 | 1.7e-8 |
| 2020-04-14 | 100.0643 | 100.0643 | 2.3e-8 |
| 2020-04-15 | 101.0542 | 101.0542 | 4.4e-8 |
| 2020-04-16 | 102.0374 | 102.0374 | 7.0e-8 |
| 2020-04-17 | 102.5392 | 102.5392 | 7.6e-8 |
| 2020-04-18 | 102.4442 | 102.4442 | 5.8e-8 |
| 2020-04-19 | 102.0786 | 102.0786 | 2.7e-8 |
| 2020-04-20 | 101.9652 | 101.9652 | 1.1e-8 |
| 2020-04-21 | 102.4394 | 102.4394 | 1.7e-8 |
| 2020-04-22 | 103.3647 | 103.3647 | 3.8e-8 |
| 2020-04-23 | 104.2854 | 104.2854 | 6.4e-8 |
| 2020-04-24 | 104.7266 | 104.7266 | 7.1e-8 |
| 2020-04-25 | 104.5728 | 104.5728 | 5.2e-8 |
| 2020-04-26 | 104.1503 | 104.1503 | 2.2e-8 |
| 2020-04-27 | 103.9818 | 103.9818 | 5.6e-9 |
| 2020-04-28 | 104.4027 | 104.4027 | 1.2e-8 |
| 2020-04-29 | 105.2764 | 105.2764 | 3.2e-8 |

## Timing

Median of all measured repetitions.

| Step | Effect Prophet | Python Prophet | Effect Prophet is |
| --- | ---: | ---: | --- |
| Fit | 15.0 ms | 25.0 ms | 1.7× faster |
| Predict | 918 µs | 3.31 ms | 3.6× faster |
| Save model to JSON | 18 µs | 433 µs | 24× faster |
| Load model from JSON | 139 µs | 2.32 ms | 17× faster |
| Load and predict in a new process | 173 ms | 1.41 s | 8.2× faster |

## Settings

The case declaration both libraries ran.

```json
{
  "kind": "map",
  "comparison": {
    "kind": "equivalent-objective",
    "evidenceId": "logistic-python-reconciliation-v1"
  },
  "configuration": {
    "growth": "logistic",
    "scaling": "absmax",
    "seasonalityMode": "additive",
    "holidaysMode": "additive",
    "changepoints": {
      "mode": "explicit",
      "timestamps": [
        "2020-02-08T00:00:00.000Z"
      ]
    },
    "changepointPriorScale": 0.05,
    "seasonalities": [
      {
        "name": "weekly-custom",
        "periodDays": 7,
        "fourierOrder": 3,
        "priorScale": 10
      }
    ],
    "events": [],
    "regressors": []
  },
  "pythonOptimizer": {
    "algorithm": "Auto",
    "maxIterations": 10000,
    "newtonFallback": true,
    "sigFigs": 12
  }
}
```