## prophet.ProphetParams


Parameters for the Prophet model.


Usage

``` python
prophet.ProphetParams(
    *,
    holidays=None,
    yearly_seasonality=True,
    weekly_seasonality=True,
    daily_seasonality=False,
    seasonality_mode="additive",
    changepoint_range=0.8,
    changepoint_prior_scale=0.05,
    seasonality_prior_scale=10,
    holidays_prior_scale=10,
    regressors=(),
    interval_width=0.95,
    plot_components=False
)
```


For full range of parameters see: https://facebook.github.io/prophet/api/prophet.html


## Parameters


`holidays: pd.DataFrame | None = None`  
Holiday dataframe. If None, no holiday effects are fitted.

`yearly_seasonality: bool | int | {auto} = ``True`  
Whether to fit yearly seasonality, which is a repeating pattern across a calendar year (e.g., peaks in certain seasons).

`weekly_seasonality: bool | int | {auto} = ``True`  
Whether to fit weekly seasonality, which is a repeating pattern within a single week (e.g., peaks on certain days of the week).

`daily_seasonality: bool | int | {auto} = ``False`  
Whether to fit daily seasonality, which is a repeating pattern within a day (e.g., peaks at certain hours of day).

`seasonality_mode: (additive, multiplicative) = ``"additive"`  
Either "additive" (seasonal effect is add to the trend, so size of effect is constant) or "multiplicative" (seasonality effect grows with the trend). May wish to switch to multiplicative if a seasonal effect is too large near the start of a series but too small by the end.

`changepoint_range: float = ``0.8`  
Proportion of history in which trend changepoints will be estimated.

`changepoint_prior_scale: float = ``0.05`  
Parameter modulating the flexibility of the automatic changepoint selection. Large values will allow many changepoints, small values will allow few changepoints.

`seasonality_prior_scale: float = ``10`  
Parameter modulating strength of seasonality model. Larger values allow the model to fit larger seasonal fluctuations, smaller values dampen the seasonality.

`holidays_prior_scale: float = ``10`  
Parameter modulating the strength of the holiday components model.

`regressors: tuple[ProphetRegressor, …] = ()`  
Additional regressors to add before fitting.

`interval_width: float = ``0.95`  
Width of the prediction intervals - for example, 0.95 will produce 95% prediction intervals.

`plot_components: bool = ``False`  
If True, will display the Prophet plot_components figure.