---
url: >-
  https://jer-k.github.io/effect-prophet/benchmarks/linear/mcmc-linear-mixed-components.md
---

# Mixed features, 600 draws

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

96 daily rows with switchable multiplicative weekly seasonality, an event, a binary regressor and two automatic changepoints. `mcmc_samples=300`: 4 chains of 150 draws, and 80% ranges from 1,200 simulated futures, two per draw, on 24 rows.

## Input

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

## Output

Largest difference: **0.0069** (0.019% of the input's range).

Every forecast row from both libraries.

| Date | Effect Prophet | Python Prophet | Difference |
| --- | ---: | ---: | ---: |
| 2020-04-06 | 26.0165 | 26.0168 | 0.00028 |
| 2020-04-07 | 29.3869 | 29.3873 | 0.00048 |
| 2020-04-08 | 44.1649 | 44.1664 | 0.0015 |
| 2020-04-09 | 56.3159 | 56.3195 | 0.0036 |
| 2020-04-10 | 42.9831 | 42.9856 | 0.0025 |
| 2020-04-11 | 50.5385 | 50.5423 | 0.0038 |
| 2020-04-12 | 35.6511 | 35.6536 | 0.0025 |
| 2020-04-13 | 26.3563 | 26.3573 | 0.00093 |
| 2020-04-14 | 31.1080 | 31.1084 | 0.00039 |
| 2020-04-15 | 43.3811 | 43.3844 | 0.0033 |
| 2020-04-16 | 57.0473 | 57.0523 | 0.0050 |
| 2020-04-17 | 60.5083 | 60.5138 | 0.0054 |
| 2020-04-18 | 51.1925 | 51.1975 | 0.0050 |
| 2020-04-19 | 36.1115 | 36.1150 | 0.0034 |
| 2020-04-20 | 45.1319 | 45.1352 | 0.0033 |
| 2020-04-21 | 30.1530 | 30.1549 | 0.0019 |
| 2020-04-22 | 43.9564 | 43.9609 | 0.0045 |
| 2020-04-23 | 57.7787 | 57.7851 | 0.0065 |
| 2020-04-24 | 61.2827 | 61.2896 | 0.0069 |
| 2020-04-25 | 44.1771 | 44.1819 | 0.0048 |
| 2020-04-26 | 37.9396 | 37.9431 | 0.0035 |
| 2020-04-27 | 27.0359 | 27.0381 | 0.0022 |
| 2020-04-28 | 30.5361 | 30.5388 | 0.0027 |
| 2020-04-29 | 44.5139 | 44.5194 | 0.0055 |

## Timing

Median of all measured repetitions.

| Step | Effect Prophet | Python Prophet | Effect Prophet is |
| --- | ---: | ---: | --- |
| Fit | 139 ms | 186 ms | 1.3× faster |
| Predict | 288 µs | 5.07 ms | Not compared: different work |
| Save model to JSON | 273 µs | 13.0 ms | 48× faster |
| Load model from JSON | 2.62 ms | 9.23 ms | 3.5× faster |
| Simulate uncertainty | 2.95 ms | 164 ms | Not compared: different work |
| Load and simulate in a new process | 187 ms | 1.64 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.

* **Predict:** Python's `predict` on a posterior also computes every component's range over the draws, while Effect Prophet's `predict` returns point forecasts and leaves ranges to `predictWithIntervals`.
* **Simulate uncertainty, Load and simulate in a new process:** Python's `predict(vectorized=False)` also builds the point forecast and every component, while Effect Prophet's `predictUncertainty` returns only the ranges.

## Settings

The case declaration both libraries ran.

```json
{
  "kind": "map",
  "comparison": {
    "kind": "equivalent-posterior",
    "evidenceId": "posterior-nuts-v1"
  },
  "configuration": {
    "growth": "linear",
    "scaling": "absmax",
    "seasonalityMode": "multiplicative",
    "holidaysMode": "additive",
    "changepoints": {
      "mode": "auto",
      "count": 2,
      "range": 0.8
    },
    "changepointPriorScale": 0.2,
    "seasonalities": [
      {
        "name": "weekly-custom",
        "periodDays": 7,
        "fourierOrder": 2,
        "priorScale": 10,
        "conditionName": "active"
      }
    ],
    "events": [
      {
        "name": "campaign",
        "date": "2020-01-22",
        "lowerWindowDays": 0,
        "upperWindowDays": 0,
        "priorScale": 10
      }
    ],
    "regressors": [
      {
        "name": "promotion",
        "priorScale": 10,
        "standardization": "never"
      }
    ]
  },
  "pythonOptimizer": {
    "algorithm": "Auto",
    "maxIterations": 10000,
    "newtonFallback": true,
    "sigFigs": 12
  },
  "uncertainty": {
    "seed": 19,
    "samples": 1000,
    "intervalWidth": 0.8,
    "output": "intervals",
    "algorithm": "scalar-continuous-time",
    "evidence": "docs/validation/uncertainty.md#evidence-classes",
    "samplerMemoryLimitBytes": 67108864
  },
  "mcmc": {
    "samples": 300,
    "seed": 19,
    "pointWidthFraction": 0.15
  }
}
```