---
url: >-
  https://jer-k.github.io/effect-prophet/benchmarks/logistic/evaluation-logistic-large-intervals.md
---

# Cross-validation with ranges, eight months

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

The same long folds with simulated futures for interval coverage.

## Input

[`benchmark/inputs/v1/logistic-implicit-floor-large.json`](https://github.com/jer-k/effect-prophet/blob/main/benchmark/inputs/v1/logistic-implicit-floor-large.json): 256 history rows and 64 forecast rows.

## Output

Largest difference: **0.028** (0.034% of the input's range).

Each row is one forecast made during cross-validation, next to the value that actually happened.

| Date | Actual | Effect Prophet | Python Prophet | Difference |
| --- | ---: | ---: | ---: | ---: |
| 2020-08-13 | 87.3514 | 87.3331 | 87.3137 | 0.019 |
| 2020-08-14 | 87.6488 | 87.5376 | 87.5413 | 0.0037 |
| 2020-08-15 | 87.4249 | 87.4024 | 87.4110 | 0.0086 |
| 2020-08-16 | 87.0921 | 87.1415 | 87.1515 | 0.010 |
| 2020-08-17 | 87.0732 | 87.0283 | 87.0564 | 0.028 |
| 2020-08-18 | 87.2845 | 87.2244 | 87.2453 | 0.021 |
| 2020-08-19 | 87.6215 | 87.6939 | 87.6790 | 0.015 |
| 2020-08-20 | 88.1043 | 88.1796 | 88.1595 | 0.020 |
| 2020-08-21 | 88.4210 | 88.3871 | 88.3894 | 0.0024 |
| 2020-08-22 | 88.2403 | 88.2486 | 88.2558 | 0.0072 |
| 2020-08-23 | 87.8794 | 87.9853 | 87.9940 | 0.0086 |
| 2020-08-24 | 87.8558 | 87.8750 | 87.9012 | 0.026 |
| 2020-08-25 | 88.1455 | 88.0707 | 88.0902 | 0.019 |
| 2020-08-26 | 88.5220 | 88.5367 | 88.5214 | 0.015 |

## Timing

Median of all measured repetitions.

| Step | Effect Prophet | Python Prophet | Effect Prophet is |
| --- | ---: | ---: | --- |
| Cross-validate with ranges | 15.1 ms | 56.4 ms | Not compared: different work |

Some steps aren't compared, because the two libraries' public calls produce different outputs. Their times show what each call costs, not which library is faster.

* **Cross-validate with ranges:** Python Prophet's cross-validation returns different outputs, and baselines, search and holdout reports have no Python API, so the benchmark times equivalent application code. The two libraries also compute ranges and some metrics differently.

## Settings

The case declaration both libraries ran.

```json
{
  "kind": "evaluation",
  "comparison": {
    "kind": "different-public-work",
    "evidenceId": "rolling-origin-evaluation-v1"
  },
  "configuration": {
    "growth": "logistic",
    "scaling": "minmax",
    "seasonalityMode": "additive",
    "holidaysMode": "additive",
    "changepoints": {
      "mode": "explicit",
      "timestamps": [
        "2020-02-10T00:00:00.000Z"
      ]
    },
    "changepointPriorScale": 0.2,
    "seasonalities": [
      {
        "name": "weekly-custom",
        "periodDays": 7,
        "fourierOrder": 2,
        "priorScale": 10
      }
    ],
    "events": [],
    "regressors": []
  },
  "pythonOptimizer": {
    "algorithm": "Auto",
    "maxIterations": 10000,
    "newtonFallback": true,
    "sigFigs": 12
  },
  "plan": {
    "horizonMs": 604800000,
    "cutoffs": [
      "2020-08-12T00:00:00.000Z",
      "2020-08-19T00:00:00.000Z"
    ]
  },
  "metricAggregation": "rolling",
  "interval": {
    "seed": 19,
    "samples": 256,
    "intervalWidth": 0.8
  }
}
```