## forecast.run_forecasts()


Run forecasts for every metric and area, sequentially or in parallel.


Usage

``` python
forecast.run_forecasts(
    forecast_function, train, params, test=None, horizon=None, cores=1
)
```


## Parameters


`forecast_function: prophet | arima`  
Forecasting function to run.

`train: pd.DataFrame`  
Historic data used to train the model.

`params: ProphetParams | ARIMAParams`  
Parameters for the selected forecasting model.

`test: pd.DataFrame = None`  
Held-out data to forecast and later compare against. If None, provide `horizon` instead,

`horizon: int = None`  
Number of days to forecast after the final training date. If None, provide `test` instead.

`cores: int = ``1`  
Number of CPU cores to use. Set to 1 for sequential processing or -1 to use all available cores.


## Returns


`pd.DataFrame`  
Forecasts for every metric and area.