---
url: >-
  https://jer-k.github.io/effect-prophet/benchmarks/flat/evaluation-flat-mixed-point.md
---

# Cross-validation and metrics

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

Three rolling cutoffs, a 7-day horizon, all metrics and a baseline comparison.

## Input

[`benchmark/inputs/v1/flat-mixed-components.json`](https://github.com/jer-k/effect-prophet/blob/main/benchmark/inputs/v1/flat-mixed-components.json): 96 history rows and 24 forecast rows.

## Output

Largest difference: **2.8e-7** (< 0.0001% 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 |
| --- | ---: | ---: | ---: | ---: |
| 2020-03-04 | 35.0775 | 34.9780 | 34.9780 | 1.2e-7 |
| 2020-03-05 | 45.8872 | 45.9405 | 45.9405 | 3.0e-8 |
| 2020-03-06 | 34.9423 | 35.0019 | 35.0019 | 1.0e-7 |
| 2020-03-07 | 41.1218 | 41.0645 | 41.0645 | 7.7e-9 |
| 2020-03-08 | 28.9133 | 28.8939 | 28.8939 | 1.7e-7 |
| 2020-03-09 | 22.4365 | 22.5775 | 22.5775 | 2.8e-7 |
| 2020-03-10 | 24.0283 | 24.0826 | 24.0826 | 2.6e-7 |
| 2020-03-11 | 35.0568 | 35.0021 | 35.0021 | 1.0e-7 |
| 2020-03-12 | 45.9078 | 45.9398 | 45.9398 | 9.8e-8 |
| 2020-03-13 | 48.5866 | 48.6775 | 48.6775 | 2.6e-8 |
| 2020-03-14 | 41.1455 | 41.0759 | 41.0759 | 1.0e-8 |
| 2020-03-15 | 30.2075 | 30.0881 | 30.0881 | 4.7e-8 |
| 2020-03-16 | 34.9665 | 35.0021 | 35.0021 | 1.0e-7 |
| 2020-03-17 | 24.0576 | 24.0795 | 24.0795 | 1.9e-7 |
| 2020-03-18 | 35.1048 | 34.9897 | 34.9897 | 3.0e-9 |
| 2020-03-19 | 45.9668 | 45.9330 | 45.9330 | 8.4e-8 |
| 2020-03-20 | 48.5708 | 48.6675 | 48.6675 | 5.7e-8 |
| 2020-03-21 | 36.2070 | 36.2037 | 36.2037 | 5.4e-8 |
| 2020-03-22 | 28.9741 | 28.9071 | 28.9071 | 1.7e-7 |
| 2020-03-23 | 21.2751 | 21.3559 | 21.3559 | 2.0e-7 |
| 2020-03-24 | 23.9598 | 24.0746 | 24.0746 | 1.2e-7 |

## Timing

Median of all measured repetitions.

| Step | Effect Prophet | Python Prophet | Effect Prophet is |
| --- | ---: | ---: | --- |
| Convert input rows | 52 µs | 423 µs | Not compared: different work |
| Plan cross-validation folds | 1.57 ms | 769 µs | Not compared: different work |
| Cross-validate | 4.50 ms | 46.2 ms | Not compared: different work |
| Compute metrics | 108 µs | 5.44 ms | Not compared: different work |
| Score baselines | 1.23 ms | 1.47 ms | Not compared: different work |
| First cross-validation in a new process | 190 ms | 1.48 s | 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.

* **Convert input rows, Plan cross-validation folds, Cross-validate, Compute metrics, Score baselines, First cross-validation in a new process:** 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": "flat",
    "scaling": "minmax",
    "seasonalityMode": "additive",
    "holidaysMode": "additive",
    "changepoints": {
      "mode": "auto",
      "count": 25,
      "range": 0.8
    },
    "changepointPriorScale": 0.05,
    "seasonalities": [
      {
        "name": "weekly-custom",
        "periodDays": 7,
        "fourierOrder": 2,
        "priorScale": 10,
        "conditionName": "active",
        "mode": "multiplicative"
      }
    ],
    "events": [
      {
        "name": "campaign",
        "date": "2020-01-22",
        "lowerWindowDays": 0,
        "upperWindowDays": 0,
        "priorScale": 10
      }
    ],
    "regressors": [
      {
        "name": "promotion",
        "priorScale": 10,
        "standardization": "never"
      }
    ]
  },
  "effectOptimizer": {
    "algorithm": "lbfgs",
    "maxIterations": 10000,
    "fallback": "none",
    "lbfgs": {
      "historySize": 5,
      "initAlpha": 0.001,
      "tolObj": 1e-12,
      "tolRelObj": 10000,
      "tolGrad": 1e-8,
      "tolRelGrad": 10000000,
      "tolParam": 1e-8
    }
  },
  "pythonOptimizer": {
    "algorithm": "LBFGS",
    "maxIterations": 10000,
    "newtonFallback": false
  },
  "plan": {
    "horizonMs": 604800000,
    "cutoffs": [
      "2020-03-03T00:00:00.000Z",
      "2020-03-10T00:00:00.000Z",
      "2020-03-17T00:00:00.000Z"
    ]
  },
  "metricAggregation": "horizons"
}
```