Iterated one-step forecasting (no refitting)

This function faciliates the forecasting approach in Ward et al. 2014. It forecasts the last num_ahead points of the time series:

1. fit the model to all but the last num_ahead time points

2. make 1-step ahead forecasts for the num_ahead time points

forecast_iterated(fun, timeseries, num_ahead = 5, ...)

Arguments

fun	a function for doing the forecasting. It should have arguments: training the data for training the model observed the observed values to be forecasted ... any optional arguments and should return a data.frame with at least the observed and predicted values (with the column names observed and predicted). Other columns are optional, and may be specific to the forecasting method)
timeseries	the time series to forecast
num_ahead	the number of points at the end of the time series to forecast
...	arguments to pass to fun

Value

a data.frame of the observed and predicted values, with any other remaining values returned by fun. If any error occurs, then NA values for the observed and predicted