---
url: >-
  https://jer-k.github.io/effect-prophet/benchmarks/linear/warm-start-linear-defaults-1-new-row.md
---

# Refit after one new day

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

The same refit after one new day: a fit of the first 255 days starts the fit of all 256.

## Input

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

## Output

Largest difference: **0.0012** (0.0012% of the input's range).

Every forecast row from both libraries.

| Date | Effect Prophet | Python Prophet | Difference |
| --- | ---: | ---: | ---: |
| 2020-09-13 | 107.7959 | 107.7956 | 0.00023 |
| 2020-09-14 | 107.5632 | 107.5630 | 0.00024 |
| 2020-09-15 | 108.3426 | 108.3423 | 0.00024 |
| 2020-09-16 | 109.9334 | 109.9330 | 0.00034 |
| 2020-09-17 | 111.5461 | 111.5458 | 0.00031 |
| 2020-09-18 | 112.3360 | 112.3357 | 0.00031 |
| 2020-09-19 | 112.0779 | 112.0775 | 0.00034 |
| 2020-09-20 | 111.3663 | 111.3660 | 0.00034 |
| 2020-09-21 | 111.1337 | 111.1334 | 0.00034 |
| 2020-09-22 | 111.9130 | 111.9127 | 0.00034 |
| 2020-09-23 | 113.5038 | 113.5034 | 0.00044 |
| 2020-09-24 | 115.1165 | 115.1161 | 0.00041 |
| 2020-09-25 | 115.9064 | 115.9060 | 0.00041 |
| 2020-09-26 | 115.6483 | 115.6479 | 0.00044 |
| 2020-09-27 | 114.9368 | 114.9363 | 0.00044 |
| 2020-09-28 | 114.7042 | 114.7037 | 0.00045 |
| 2020-09-29 | 115.4835 | 115.4830 | 0.00044 |
| 2020-09-30 | 117.0743 | 117.0738 | 0.00055 |
| 2020-10-01 | 118.6870 | 118.6865 | 0.00052 |
| 2020-10-02 | 119.4769 | 119.4764 | 0.00052 |
| 2020-10-03 | 119.2188 | 119.2183 | 0.00054 |
| 2020-10-04 | 118.5072 | 118.5067 | 0.00055 |
| 2020-10-05 | 118.2746 | 118.2741 | 0.00055 |
| 2020-10-06 | 119.0539 | 119.0534 | 0.00055 |
| 2020-10-07 | 120.6448 | 120.6441 | 0.00065 |
| 2020-10-08 | 122.2575 | 122.2568 | 0.00062 |
| 2020-10-09 | 123.0473 | 123.0467 | 0.00062 |
| 2020-10-10 | 122.7893 | 122.7886 | 0.00065 |
| 2020-10-11 | 122.0777 | 122.0771 | 0.00065 |
| 2020-10-12 | 121.8451 | 121.8444 | 0.00065 |
| 2020-10-13 | 122.6244 | 122.6237 | 0.00065 |
| 2020-10-14 | 124.2152 | 124.2145 | 0.00075 |
| 2020-10-15 | 125.8279 | 125.8272 | 0.00072 |
| 2020-10-16 | 126.6178 | 126.6171 | 0.00073 |
| 2020-10-17 | 126.3597 | 126.3590 | 0.00075 |
| 2020-10-18 | 125.6482 | 125.6474 | 0.00075 |
| 2020-10-19 | 125.4155 | 125.4148 | 0.00076 |
| 2020-10-20 | 126.1949 | 126.1941 | 0.00076 |
| 2020-10-21 | 127.7857 | 127.7848 | 0.00086 |
| 2020-10-22 | 129.3984 | 129.3975 | 0.00083 |
| 2020-10-23 | 130.1883 | 130.1874 | 0.00083 |
| 2020-10-24 | 129.9302 | 129.9293 | 0.00085 |
| 2020-10-25 | 129.2186 | 129.2178 | 0.00086 |
| 2020-10-26 | 128.9860 | 128.9852 | 0.00086 |
| 2020-10-27 | 129.7653 | 129.7645 | 0.00086 |
| 2020-10-28 | 131.3561 | 131.3552 | 0.00096 |
| 2020-10-29 | 132.9688 | 132.9679 | 0.00093 |
| 2020-10-30 | 133.7587 | 133.7578 | 0.00093 |
| 2020-10-31 | 133.5006 | 133.4997 | 0.00096 |
| 2020-11-01 | 132.7891 | 132.7881 | 0.00096 |
| 2020-11-02 | 132.5565 | 132.5555 | 0.00096 |
| 2020-11-03 | 133.3358 | 133.3348 | 0.00096 |
| 2020-11-04 | 134.9266 | 134.9255 | 0.0011 |
| 2020-11-05 | 136.5393 | 136.5383 | 0.0010 |
| 2020-11-06 | 137.3292 | 137.3282 | 0.0010 |
| 2020-11-07 | 137.0711 | 137.0700 | 0.0011 |
| 2020-11-08 | 136.3595 | 136.3585 | 0.0011 |
| 2020-11-09 | 136.1269 | 136.1259 | 0.0011 |
| 2020-11-10 | 136.9062 | 136.9052 | 0.0011 |
| 2020-11-11 | 138.4971 | 138.4959 | 0.0012 |
| 2020-11-12 | 140.1098 | 140.1086 | 0.0011 |
| 2020-11-13 | 140.8997 | 140.8985 | 0.0011 |
| 2020-11-14 | 140.6416 | 140.6404 | 0.0012 |
| 2020-11-15 | 139.9300 | 139.9288 | 0.0012 |

## Internal fit differences

The forecasts match, so this case passes. Both libraries search for the best-fitting parameters and stop when they are close enough. On this input they stopped at slightly different points, so these internal numbers differ by more than the threshold where we take a closer look.

| Check | Effect Prophet | Python Prophet | Threshold |
| --- | ---: | ---: | ---: |
| Fitting objective (lower is a better fit) | -1762.0385 | -1762.2584 | 0.01 |
| Stationarity residual (lower is closer to the best fit) | 29142.8986 | 15679.3058 | 0.01 |

## Timing

Median of all measured repetitions.

| Step | Effect Prophet | Python Prophet | Effect Prophet is |
| --- | ---: | ---: | --- |
| Fit | 26.3 ms | 93.2 ms | 3.5× faster |
| Refit from an earlier fit | 8.14 ms | 21.5 ms | 2.6× faster |

## Settings

The case declaration both libraries ran.

```json
{
  "kind": "map",
  "comparison": {
    "kind": "equivalent-objective",
    "evidenceId": "linear-output-first-v4"
  },
  "fitRequest": "growth-only",
  "configuration": {
    "growth": "linear",
    "scaling": "absmax",
    "seasonalityMode": "additive",
    "holidaysMode": "additive",
    "changepoints": {
      "mode": "auto",
      "count": 25,
      "range": 0.8
    },
    "changepointPriorScale": 0.05,
    "seasonalities": [
      {
        "name": "weekly",
        "periodDays": 7,
        "fourierOrder": 3,
        "priorScale": 10
      }
    ],
    "events": [],
    "regressors": []
  },
  "pythonOptimizer": {
    "algorithm": "Auto",
    "maxIterations": 10000,
    "newtonFallback": true
  },
  "warmStart": {
    "baseRows": 255,
    "investigateParameterAbsolute": 0.01
  }
}
```