---
url: >-
  https://jer-k.github.io/effect-prophet/benchmarks/flat/flat-mixed-components-absmax-defaults.md
---

# Mixed effects, both libraries on default settings

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

The mixed-effects rows with absmax scaling, where both libraries pick their own optimizer. The other mixed-effects cases pin both to L-BFGS.

## Input

[`benchmark/inputs/v1/flat-mixed-components.json`](https://github.com/jer-k/effect-prophet/blob/main/benchmark/inputs/v1/flat-mixed-components.json): 96 history rows and 24 forecast rows.

## Output

Largest difference: **0.021** (0.073% of the input's range).

Every forecast row from both libraries.

| Date | Effect Prophet | Python Prophet | Difference |
| --- | ---: | ---: | ---: |
| 2020-04-06 | 21.3445 | 21.3492 | 0.0047 |
| 2020-04-07 | 24.0577 | 24.0640 | 0.0064 |
| 2020-04-08 | 36.1988 | 36.2142 | 0.015 |
| 2020-04-09 | 45.9285 | 45.9466 | 0.018 |
| 2020-04-10 | 34.9902 | 35.0026 | 0.012 |
| 2020-04-11 | 41.0592 | 41.0756 | 0.016 |
| 2020-04-12 | 28.9157 | 28.9241 | 0.0085 |
| 2020-04-13 | 21.3445 | 21.3492 | 0.0047 |
| 2020-04-14 | 25.2552 | 25.2655 | 0.010 |
| 2020-04-15 | 34.9902 | 35.0026 | 0.012 |
| 2020-04-16 | 45.9285 | 45.9466 | 0.018 |
| 2020-04-17 | 48.6246 | 48.6457 | 0.021 |
| 2020-04-18 | 41.0592 | 41.0756 | 0.016 |
| 2020-04-19 | 28.9157 | 28.9241 | 0.0085 |
| 2020-04-20 | 36.1877 | 36.2040 | 0.016 |
| 2020-04-21 | 24.0577 | 24.0640 | 0.0064 |
| 2020-04-22 | 35.0012 | 35.0127 | 0.011 |
| 2020-04-23 | 45.9285 | 45.9466 | 0.018 |
| 2020-04-24 | 48.6246 | 48.6457 | 0.021 |
| 2020-04-25 | 34.9902 | 35.0026 | 0.012 |
| 2020-04-26 | 30.1132 | 30.1256 | 0.012 |
| 2020-04-27 | 21.3445 | 21.3492 | 0.0047 |
| 2020-04-28 | 24.0577 | 24.0640 | 0.0064 |
| 2020-04-29 | 35.0012 | 35.0127 | 0.011 |

## Timing

Median of all measured repetitions.

| Step | Effect Prophet | Python Prophet | Effect Prophet is |
| --- | ---: | ---: | --- |
| Convert input rows | 52 µs | 507 µs | 9.7× faster |
| Fit | 3.61 ms | 43.6 ms | 12× faster |
| Predict | 287 µs | 4.73 ms | 16× faster |
| Fit and predict | 3.11 ms | 48.8 ms | 16× faster |
| Convert, fit and predict | 2.95 ms | 49.0 ms | 17× faster |
| First forecast in a new process | 175 ms | 1.46 s | 8.3× faster |
| Save model to JSON | 31 µs | 703 µs | 23× faster |
| Load model from JSON | 137 µs | 3.77 ms | 28× faster |
| Load and predict in a new process | 160 ms | 1.42 s | 8.9× faster |

## Settings

The case declaration both libraries ran.

```json
{
  "kind": "map",
  "comparison": {
    "kind": "equivalent-objective",
    "evidenceId": "flat-map-mixed-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"
      }
    ]
  },
  "pythonOptimizer": {
    "algorithm": "Auto",
    "maxIterations": 10000,
    "newtonFallback": true,
    "sigFigs": 12
  }
}
```