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

# India with explicit known-gap opt-in

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

A 2036 history and 2037 forecasts use the pinned partial provider result with allow-known-gaps. The omitted table-backed holidays are not claimed complete.

## Input

[`benchmark/inputs/v5/country-partial-96.json`](https://github.com/jer-k/effect-prophet/blob/main/benchmark/inputs/v5/country-partial-96.json): 96 history rows and 3 forecast rows.

## Output

Largest difference: **3.2e-12** (< 0.0001% of the input's range).

Every forecast row from both libraries.

| Date | Effect Prophet | Python Prophet | Difference |
| --- | ---: | ---: | ---: |
| 2037-01-01 | 20.0510 | 20.0510 | 3.0e-12 |
| 2037-01-26 | 19.9291 | 19.9291 | 2.9e-12 |
| 2037-08-15 | 20.0510 | 20.0510 | 3.2e-12 |

## Timing

Median of all measured repetitions.

| Step | Effect Prophet | Python Prophet | Effect Prophet is |
| --- | ---: | ---: | --- |
| Convert input rows | 15 µs | 446 µs | 30× faster |
| Fit | 3.64 ms | 15.2 ms | 4.2× faster |
| Predict | 208 µs | 4.85 ms | 23× faster |
| Fit and predict | 3.40 ms | 20.3 ms | 6.0× faster |
| Convert, fit and predict | 3.10 ms | 20.9 ms | 6.7× faster |
| First forecast in a new process | 179 ms | 1.59 s | 8.9× faster |
| Save model to JSON | 120 µs | 510 µs | 4.2× faster |
| Load model from JSON | 1.27 ms | 3.06 ms | 2.4× faster |
| Load and predict in a new process | 163 ms | 1.58 s | 9.7× 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": "IN",
      "coverage": "allow-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
  }
}
```