---
url: >-
  https://jer-k.github.io/effect-prophet/benchmarks/flat/country-mixed-us-flat.md
---

# Country, custom events and mixed features

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

96 days with US country holidays, custom events, conditional weekly seasonality and a promotion regressor. Holiday effects are multiplicative.

## Input

[`benchmark/inputs/v5/country-mixed-96.json`](https://github.com/jer-k/effect-prophet/blob/main/benchmark/inputs/v5/country-mixed-96.json): 96 history rows and 4 forecast rows.

## Output

Largest difference: **3.0e-5** (0.00062% of the input's range).

Every forecast row from both libraries.

| Date | Effect Prophet | Python Prophet | Difference |
| --- | ---: | ---: | ---: |
| 2024-12-25 | 21.7611 | 21.7611 | 3.0e-5 |
| 2025-01-01 | 23.0406 | 23.0407 | 1.1e-5 |
| 2025-01-02 | 20.6095 | 20.6095 | 9.9e-6 |
| 2025-01-05 | 20.7451 | 20.7450 | 3.0e-5 |

## Timing

Median of all measured repetitions.

| Step | Effect Prophet | Python Prophet | Effect Prophet is |
| --- | ---: | ---: | --- |
| Convert input rows | 46 µs | 499 µs | 11× faster |
| Fit | 17.8 ms | 54.9 ms | 3.1× faster |
| Predict | 222 µs | 6.23 ms | 28× faster |
| Fit and predict | 17.1 ms | 61.4 ms | 3.6× faster |
| Convert, fit and predict | 18.3 ms | 61.6 ms | 3.4× faster |
| First forecast in a new process | 196 ms | 1.65 s | 8.4× faster |
| Save model to JSON | 108 µs | 704 µs | 6.5× faster |
| Load model from JSON | 810 µs | 4.30 ms | 5.3× faster |
| Load and predict in a new process | 167 ms | 1.60 s | 9.6× faster |

## Settings

The case declaration both libraries ran.

```json
{
  "kind": "map",
  "comparison": {
    "kind": "equivalent-objective",
    "evidenceId": "country-calendar-public-v1"
  },
  "configuration": {
    "growth": "flat",
    "countryHolidays": {
      "country": "US",
      "coverage": "reject-known-gaps",
      "priorScale": 10
    },
    "scaling": "absmax",
    "seasonalityMode": "additive",
    "holidaysMode": "multiplicative",
    "changepoints": {
      "mode": "auto",
      "count": 25,
      "range": 0.8
    },
    "changepointPriorScale": 0.05,
    "seasonalities": [
      {
        "name": "weekly-active",
        "periodDays": 7,
        "fourierOrder": 1,
        "priorScale": 10,
        "conditionName": "active"
      }
    ],
    "events": [
      {
        "name": "launch",
        "date": "2024-01-01",
        "lowerWindowDays": 0,
        "upperWindowDays": 0,
        "priorScale": 10
      },
      {
        "name": "launch",
        "date": "2024-01-05",
        "lowerWindowDays": 0,
        "upperWindowDays": 0,
        "priorScale": 10
      },
      {
        "name": "launch",
        "date": "2025-01-01",
        "lowerWindowDays": 0,
        "upperWindowDays": 0,
        "priorScale": 10
      },
      {
        "name": "launch",
        "date": "2025-01-05",
        "lowerWindowDays": 0,
        "upperWindowDays": 0,
        "priorScale": 10
      }
    ],
    "regressors": [
      {
        "name": "promotion",
        "priorScale": 10,
        "standardization": "never"
      }
    ]
  },
  "pythonOptimizer": {
    "algorithm": "Auto",
    "maxIterations": 10000,
    "newtonFallback": true,
    "sigFigs": 12
  }
}
```