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

# Observed spillovers and unseen future names

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

96 daily rows from 2010; sparse prediction years 2021 and 2060. December 31's observed New Year's Day is retained while unseen Juneteenth names are ignored.

## Input

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

## Output

Largest difference: **5.2e-12** (< 0.0001% of the input's range).

Every forecast row from both libraries.

| Date | Effect Prophet | Python Prophet | Difference |
| --- | ---: | ---: | ---: |
| 2021-06-18 | 20.0158 | 20.0158 | 4.1e-12 |
| 2021-06-19 | 20.0158 | 20.0158 | 4.1e-12 |
| 2021-12-31 | 20.0158 | 20.0158 | 2.5e-12 |
| 2060-01-01 | 23.0350 | 23.0350 | 5.2e-12 |

## Timing

Median of all measured repetitions.

| Step | Effect Prophet | Python Prophet | Effect Prophet is |
| --- | ---: | ---: | --- |
| Convert input rows | 15 µs | 444 µs | 29× faster |
| Fit | 21.8 ms | 65.4 ms | 3.0× faster |
| Predict | 314 µs | 5.26 ms | 17× faster |
| Fit and predict | 21.5 ms | 71.3 ms | 3.3× faster |
| Convert, fit and predict | 21.3 ms | 72.2 ms | 3.4× faster |
| First forecast in a new process | 227 ms | 1.65 s | 7.3× faster |
| Save model to JSON | 135 µs | 529 µs | 3.9× faster |
| Load model from JSON | 1.44 ms | 3.28 ms | 2.3× faster |
| Load and predict in a new process | 188 ms | 1.57 s | 8.4× 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": "additive",
    "changepoints": {
      "mode": "auto",
      "count": 25,
      "range": 0.8
    },
    "changepointPriorScale": 0.05,
    "seasonalities": [],
    "events": [],
    "regressors": []
  },
  "pythonOptimizer": {
    "algorithm": "Auto",
    "maxIterations": 10000,
    "newtonFallback": true,
    "sigFigs": 12
  }
}
```