---
url: >-
  https://jer-k.github.io/effect-prophet/benchmarks/linear/map-seasonal-breaks-irregular-medium.md
---

# Weekly pattern on irregular dates

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

192 unevenly spaced rows with a custom weekly seasonality and two changepoints.

## Input

[`benchmark/inputs/v1/map-irregular-medium.json`](https://github.com/jer-k/effect-prophet/blob/main/benchmark/inputs/v1/map-irregular-medium.json): 192 history rows and 48 forecast rows.

## Output

Largest difference: **6.9e-5** (< 0.0001% of the input's range).

Every forecast row from both libraries.

| Date | Effect Prophet | Python Prophet | Difference |
| --- | ---: | ---: | ---: |
| 2020-08-17 | 84.4721 | 84.4720 | 3.6e-5 |
| 2020-08-18 | 84.7457 | 84.7457 | 1.9e-5 |
| 2020-08-19 | 85.3132 | 85.3132 | 3.1e-5 |
| 2020-08-20 | 85.8875 | 85.8875 | 4.4e-5 |
| 2020-08-22 | 86.6631 | 86.6631 | 4.8e-5 |
| 2020-08-23 | 87.0718 | 87.0718 | 3.5e-5 |
| 2020-08-24 | 87.4771 | 87.4770 | 4.0e-5 |
| 2020-08-25 | 87.7507 | 87.7507 | 2.2e-5 |
| 2020-08-26 | 88.3182 | 88.3182 | 3.5e-5 |
| 2020-08-27 | 88.8925 | 88.8925 | 4.8e-5 |
| 2020-08-28 | 89.1471 | 89.1470 | 4.6e-5 |
| 2020-08-30 | 90.0768 | 90.0768 | 3.9e-5 |
| 2020-08-31 | 90.4820 | 90.4820 | 4.3e-5 |
| 2020-09-01 | 90.7557 | 90.7556 | 2.6e-5 |
| 2020-09-02 | 91.3232 | 91.3231 | 3.8e-5 |
| 2020-09-03 | 91.8975 | 91.8974 | 5.1e-5 |
| 2020-09-04 | 92.1521 | 92.1520 | 4.9e-5 |
| 2020-09-06 | 93.0818 | 93.0818 | 4.2e-5 |
| 2020-09-08 | 93.7606 | 93.7606 | 2.9e-5 |
| 2020-09-09 | 94.3282 | 94.3281 | 4.2e-5 |
| 2020-09-10 | 94.9025 | 94.9024 | 5.5e-5 |
| 2020-09-11 | 95.1570 | 95.1570 | 5.3e-5 |
| 2020-09-12 | 95.6780 | 95.6780 | 5.8e-5 |
| 2020-09-13 | 96.0868 | 96.0867 | 4.6e-5 |
| 2020-09-14 | 96.4920 | 96.4919 | 5.0e-5 |
| 2020-09-16 | 97.3331 | 97.3331 | 4.5e-5 |
| 2020-09-17 | 97.9074 | 97.9074 | 5.8e-5 |
| 2020-09-18 | 98.1620 | 98.1620 | 5.6e-5 |
| 2020-09-19 | 98.6830 | 98.6829 | 6.2e-5 |
| 2020-09-20 | 99.0917 | 99.0917 | 4.9e-5 |
| 2020-09-21 | 99.4970 | 99.4969 | 5.4e-5 |
| 2020-09-22 | 99.7706 | 99.7706 | 3.6e-5 |
| 2020-09-24 | 100.9124 | 100.9124 | 6.2e-5 |
| 2020-09-25 | 101.1670 | 101.1669 | 6.0e-5 |
| 2020-09-26 | 101.6880 | 101.6879 | 6.5e-5 |
| 2020-09-27 | 102.0967 | 102.0967 | 5.3e-5 |
| 2020-09-29 | 102.7756 | 102.7755 | 4.0e-5 |
| 2020-09-30 | 103.3431 | 103.3430 | 5.2e-5 |
| 2020-10-01 | 103.9174 | 103.9173 | 6.5e-5 |
| 2020-10-03 | 104.6930 | 104.6929 | 6.9e-5 |
| 2020-10-04 | 105.1017 | 105.1016 | 5.6e-5 |
| 2020-10-05 | 105.5069 | 105.5069 | 6.1e-5 |
| 2020-10-06 | 105.7806 | 105.7805 | 4.3e-5 |
| 2020-10-07 | 106.3481 | 106.3480 | 5.6e-5 |
| 2020-10-08 | 106.9224 | 106.9223 | 6.9e-5 |
| 2020-10-09 | 107.1769 | 107.1769 | 6.7e-5 |
| 2020-10-11 | 108.1067 | 108.1066 | 6.0e-5 |
| 2020-10-12 | 108.5119 | 108.5118 | 6.4e-5 |

## Timing

Median of all measured repetitions.

| Step | Effect Prophet | Python Prophet | Effect Prophet is |
| --- | ---: | ---: | --- |
| Convert input rows | 37 µs | 528 µs | 14× faster |
| Fit | 2.40 ms | 8.16 ms | 3.4× faster |
| Predict | 265 µs | 3.09 ms | 12× faster |
| Fit and predict | 1.99 ms | 11.1 ms | 5.6× faster |
| Convert, fit and predict | 1.35 ms | 11.6 ms | 8.6× faster |
| Save model to JSON | 20 µs | 519 µs | 26× faster |
| Load model from JSON | 92 µs | 2.24 ms | 24× faster |
| Load and predict in a new process | 165 ms | 1.44 s | 8.7× faster |

## Settings

The case declaration both libraries ran.

```json
{
  "kind": "linear-map",
  "comparison": {
    "kind": "equivalent-objective",
    "evidenceId": "linear-output-first-v4"
  },
  "configuration": {
    "effectMap": "explicit",
    "changepoints": {
      "mode": "explicit",
      "timestamps": [
        "2020-03-26T00:00:00.000Z",
        "2020-06-09T00:00:00.000Z"
      ]
    },
    "changepointPriorScale": 0.2,
    "seasonalities": [
      {
        "name": "weekly-custom",
        "periodDays": 7,
        "fourierOrder": 3,
        "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
  }
}
```