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

# Observed spillovers and unseen future names

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

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: **1.9e-9** (< 0.0001% of the input's range).

Every forecast row from both libraries.

| Date | Effect Prophet | Python Prophet | Difference |
| --- | ---: | ---: | ---: |
| 2021-06-18 | 22.4917 | 22.4917 | 4.2e-10 |
| 2021-06-19 | 22.4923 | 22.4923 | 4.2e-10 |
| 2021-12-31 | 22.6104 | 22.6104 | 4.4e-10 |
| 2060-01-01 | 33.9385 | 33.9385 | 1.9e-9 |

## Timing

Median of all measured repetitions.

| Step | Effect Prophet | Python Prophet | Effect Prophet is |
| --- | ---: | ---: | --- |
| Convert input rows | 14 µs | 483 µs | 34× faster |
| Fit | 5.60 ms | 21.6 ms | 3.9× faster |
| Predict | 230 µs | 5.50 ms | 24× faster |
| Fit and predict | 5.28 ms | 27.8 ms | 5.3× faster |
| Convert, fit and predict | 4.98 ms | 27.8 ms | 5.6× faster |
| First forecast in a new process | 193 ms | 1.64 s | 8.5× faster |
| Save model to JSON | 118 µs | 529 µs | 4.5× faster |
| Load model from JSON | 1.31 ms | 3.51 ms | 2.7× faster |
| Load and predict in a new process | 180 ms | 1.63 s | 9.0× 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": "additive",
    "changepoints": {
      "mode": "auto",
      "count": 2,
      "range": 0.8
    },
    "changepointPriorScale": 0.05,
    "seasonalities": [],
    "events": [],
    "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
  }
}
```