## arima.ARIMAParams


Parameters for the ARIMA model.


Usage

``` python
arima.ARIMAParams(
    *,
    holidays=None,
    order=(0, 0, 0),
    seasonal_order=(0, 0, 0, 0),
    enforce_stationarity=True,
    max_iter=50,
    interval_width=0.95
)
```


## Parameters


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

`order: tuple = (0, 0, 0)`  
The (p, d, q) order of the model.

`seasonal_order: tuple = (0, 0, 0, 0)`  
The (P, D, Q, s) order of the seasonal component of the model.

`enforce_stationarity: bool = ``True`  
Whether or not to require the autoregressive parameters to correspond to a stationarity process.

`max_iter: int = ``50`  
The maximum number of iterations. Using ARIMA default (50), we did observe a warning that "Maximum Likelihood optimisation failed to converge". This warning can be resolved by increasing the maximum.

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