---
url: >-
  https://jer-k.github.io/effect-prophet/benchmarks/flat/flat-prefix-63-absmax.md
---

# First 63 days, absmax scaling

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

The 96-day mixed-feature history cut to its first 63 days: the training window of one cross-validation fold.

## Input

[`benchmark/inputs/v1/flat-mixed-components-prefix-63.json`](https://github.com/jer-k/effect-prophet/blob/main/benchmark/inputs/v1/flat-mixed-components-prefix-63.json): 63 history rows and 7 forecast rows.

## Output

Largest difference: **2.8e-7** (< 0.0001% of the input's range).

Every forecast row from both libraries.

| Date | Effect Prophet | Python Prophet | Difference |
| --- | ---: | ---: | ---: |
| 2020-03-04 | 34.9780 | 34.9780 | 1.2e-7 |
| 2020-03-05 | 45.9405 | 45.9405 | 3.0e-8 |
| 2020-03-06 | 35.0019 | 35.0019 | 1.0e-7 |
| 2020-03-07 | 41.0645 | 41.0645 | 7.7e-9 |
| 2020-03-08 | 28.8939 | 28.8939 | 1.7e-7 |
| 2020-03-09 | 22.5775 | 22.5775 | 2.8e-7 |
| 2020-03-10 | 24.0826 | 24.0826 | 2.6e-7 |

## Timing

Median of all measured repetitions.

| Step | Effect Prophet | Python Prophet | Effect Prophet is |
| --- | ---: | ---: | --- |
| Fit | 1.83 ms | 7.36 ms | 4.0× faster |
| Predict | 220 µs | 4.78 ms | 22× faster |
| Save model to JSON | 19 µs | 644 µs | 33× faster |
| Load model from JSON | 199 µs | 3.59 ms | 18× faster |
| Load and predict in a new process | 161 ms | 1.44 s | 8.9× faster |

## Settings

The case declaration both libraries ran.

```json
{
  "kind": "map",
  "comparison": {
    "kind": "equivalent-objective",
    "evidenceId": "flat-growth-mixed-scaling-v1"
  },
  "configuration": {
    "growth": "flat",
    "scaling": "absmax",
    "seasonalityMode": "additive",
    "holidaysMode": "additive",
    "changepoints": {
      "mode": "auto",
      "count": 25,
      "range": 0.8
    },
    "changepointPriorScale": 0.05,
    "seasonalities": [
      {
        "name": "weekly-custom",
        "periodDays": 7,
        "fourierOrder": 2,
        "priorScale": 10,
        "conditionName": "active",
        "mode": "multiplicative"
      }
    ],
    "events": [
      {
        "name": "campaign",
        "date": "2020-01-22",
        "lowerWindowDays": 0,
        "upperWindowDays": 0,
        "priorScale": 10
      }
    ],
    "regressors": [
      {
        "name": "promotion",
        "priorScale": 10,
        "standardization": "never"
      }
    ]
  },
  "effectOptimizer": {
    "algorithm": "lbfgs",
    "maxIterations": 10000,
    "fallback": "none",
    "lbfgs": {
      "historySize": 5,
      "initAlpha": 0.001,
      "tolObj": 1e-12,
      "tolRelObj": 10000,
      "tolGrad": 1e-8,
      "tolRelGrad": 10000000,
      "tolParam": 1e-8
    }
  },
  "pythonOptimizer": {
    "algorithm": "LBFGS",
    "maxIterations": 10000,
    "newtonFallback": false
  }
}
```