---
url: >-
  https://jer-k.github.io/effect-prophet/benchmarks/linear/map-training-duplicates-explicit.md
---

# Repeated timestamps, hand-picked changepoint

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

Some dates appear several times with different values. Every row counts as its own measurement.

## Input

[`benchmark/inputs/v1/training-duplicates.json`](https://github.com/jer-k/effect-prophet/blob/main/benchmark/inputs/v1/training-duplicates.json): 96 history rows and 8 forecast rows.

## Output

Largest difference: **1.7e-11** (< 0.0001% of the input's range).

Every forecast row from both libraries.

| Date | Effect Prophet | Python Prophet | Difference |
| --- | ---: | ---: | ---: |
| 2020-01-25 | 16.2514 | 16.2514 | 1.4e-11 |
| 2020-01-26 | 16.4832 | 16.4832 | 1.4e-11 |
| 2020-01-27 | 16.7149 | 16.7149 | 1.5e-11 |
| 2020-01-28 | 16.9467 | 16.9467 | 1.5e-11 |
| 2020-01-29 | 17.1784 | 17.1784 | 1.6e-11 |
| 2020-01-30 | 17.4102 | 17.4102 | 1.6e-11 |
| 2020-01-31 | 17.6420 | 17.6420 | 1.6e-11 |
| 2020-02-01 | 17.8737 | 17.8737 | 1.7e-11 |

## Timing

Median of all measured repetitions.

| Step | Effect Prophet | Python Prophet | Effect Prophet is |
| --- | ---: | ---: | --- |
| Fit | 4.05 ms | 19.8 ms | 4.9× faster |
| Predict | 158 µs | 2.69 ms | 17× faster |
| Save model to JSON | 20 µs | 391 µs | 19× faster |
| Load model from JSON | 176 µs | 2.06 ms | 12× faster |
| Load and predict in a new process | 166 ms | 1.45 s | 8.7× faster |

## Settings

The case declaration both libraries ran.

```json
{
  "kind": "map",
  "comparison": {
    "kind": "equivalent-objective",
    "evidenceId": "linear-output-first-v4"
  },
  "configuration": {
    "growth": "linear",
    "scaling": "absmax",
    "seasonalityMode": "additive",
    "holidaysMode": "additive",
    "changepoints": {
      "mode": "explicit",
      "timestamps": [
        "2020-01-17T00:00:00.000Z"
      ]
    },
    "changepointPriorScale": 0.05,
    "seasonalities": [],
    "events": [],
    "regressors": []
  },
  "effectOptimizer": {
    "algorithm": "auto",
    "maxIterations": 10000,
    "fallback": "newton",
    "lbfgs": {
      "historySize": 5,
      "initAlpha": 0.001,
      "tolObj": 1e-12,
      "tolRelObj": 10000,
      "tolGrad": 1e-8,
      "tolRelGrad": 10000000,
      "tolParam": 1e-8
    }
  },
  "pythonOptimizer": {
    "algorithm": "Auto",
    "maxIterations": 10000,
    "newtonFallback": true,
    "sigFigs": 12
  }
}
```