Helper function to get the unit of a single forecast, i.e.
the column names that define where a single forecast was made for.
This just takes all columns that are available in the data and subtracts
the columns that are protected, i.e. those returned by
get_protected_columns() as well as the names of the metrics that were
specified during scoring, if any.
Forecast unit
In order to score forecasts, scoringutils needs to know which of the rows
of the data belong together and jointly form a single forecasts. This is
easy e.g. for point forecast, where there is one row per forecast. For
quantile or sample-based forecasts, however, there are multiple rows that
belong to a single forecast.
The forecast unit or unit of a single forecast is then described by the
combination of columns that uniquely identify a single forecast.
For example, we could have forecasts made by different models in various
locations at different time points, each for several weeks into the future.
The forecast unit could then be described as
forecast_unit = c("model", "location", "forecast_date", "forecast_horizon").
scoringutils automatically tries to determine the unit of a single
forecast. It uses all existing columns for this, which means that no columns
must be present that are unrelated to the forecast unit. As a very simplistic
example, if you had an additional row, "even", that is one if the row number
is even and zero otherwise, then this would mess up scoring as scoringutils
then thinks that this column was relevant in defining the forecast unit.
In order to avoid issues, we recommend setting the forecast unit explicitly,
using the forecast_unit argument. This will simply drop unneeded columns,
while making sure that all necessary, 'protected columns' like "predicted"
or "observed" are retained.