---
url: >-
  https://jer-k.github.io/effect-prophet/benchmarks/flat/flat-prefix-70-absmax.md
---

# First 70 days, absmax scaling

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

The 96-day mixed-feature history cut to its first 70 days: the training window of one cross-validation fold.

## Input

[`benchmark/inputs/v1/flat-mixed-components-prefix-70.json`](https://github.com/jer-k/effect-prophet/blob/main/benchmark/inputs/v1/flat-mixed-components-prefix-70.json): 70 history rows and 7 forecast rows.

## Output

Largest difference: **1.9e-7** (< 0.0001% of the input's range).

Every forecast row from both libraries.

| Date | Effect Prophet | Python Prophet | Difference |
| --- | ---: | ---: | ---: |
| 2020-03-11 | 35.0021 | 35.0021 | 1.0e-7 |
| 2020-03-12 | 45.9398 | 45.9398 | 9.8e-8 |
| 2020-03-13 | 48.6775 | 48.6775 | 2.6e-8 |
| 2020-03-14 | 41.0759 | 41.0759 | 1.0e-8 |
| 2020-03-15 | 30.0881 | 30.0881 | 4.7e-8 |
| 2020-03-16 | 35.0021 | 35.0021 | 1.0e-7 |
| 2020-03-17 | 24.0795 | 24.0795 | 1.9e-7 |

## Timing

Median of all measured repetitions.

| Step | Effect Prophet | Python Prophet | Effect Prophet is |
| --- | ---: | ---: | --- |
| Fit | 2.15 ms | 8.05 ms | 3.8× faster |
| Predict | 212 µs | 4.80 ms | 23× faster |
| Save model to JSON | 24 µs | 648 µs | 27× faster |
| Load model from JSON | 207 µs | 3.60 ms | 17× faster |
| Load and predict in a new process | 160 ms | 1.44 s | 9.0× 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
  }
}
```