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

# US holidays, multiplicative

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

96 daily rows from 2024, country holidays plus custom January events, including simultaneous activations. Prediction spans late 2024 and early 2025; fitting includes lookup, and saved models embed the country snapshot.

## Input

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

## Output

Largest difference: **6.0e-12** (< 0.0001% of the input's range).

Every forecast row from both libraries.

| Date | Effect Prophet | Python Prophet | Difference |
| --- | ---: | ---: | ---: |
| 2024-12-25 | 20.1198 | 20.1198 | 4.3e-12 |
| 2025-01-01 | 23.1763 | 23.1763 | 6.0e-12 |
| 2025-01-02 | 20.1232 | 20.1232 | 4.3e-12 |
| 2025-01-05 | 21.5778 | 21.5778 | 5.0e-12 |

## Timing

Median of all measured repetitions.

| Step | Effect Prophet | Python Prophet | Effect Prophet is |
| --- | ---: | ---: | --- |
| Convert input rows | 14 µs | 473 µs | 33× faster |
| Fit | 5.13 ms | 21.8 ms | 4.3× faster |
| Predict | 276 µs | 5.60 ms | 20× faster |
| Fit and predict | 4.76 ms | 27.4 ms | 5.8× faster |
| Convert, fit and predict | 4.56 ms | 28.0 ms | 6.1× faster |
| First forecast in a new process | 183 ms | 1.63 s | 8.9× faster |
| Save model to JSON | 162 µs | 684 µs | 4.2× faster |
| Load model from JSON | 1.51 ms | 3.90 ms | 2.6× faster |
| Load and predict in a new process | 179 ms | 1.59 s | 8.9× faster |

## Settings

The case declaration both libraries ran.

```json
{
  "kind": "map",
  "comparison": {
    "kind": "equivalent-objective",
    "evidenceId": "country-calendar-public-v1"
  },
  "configuration": {
    "growth": "linear",
    "countryHolidays": {
      "country": "US",
      "coverage": "reject-known-gaps",
      "priorScale": 10
    },
    "scaling": "absmax",
    "seasonalityMode": "additive",
    "holidaysMode": "multiplicative",
    "changepoints": {
      "mode": "auto",
      "count": 2,
      "range": 0.8
    },
    "changepointPriorScale": 0.05,
    "seasonalities": [],
    "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": []
  },
  "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
  }
}
```