Create a forecast object for sample-based multivariate forecasts

Process and validate a data.frame (or similar) or similar with forecasts and observations. If the input passes all input checks, those functions will be converted to a forecast object. A forecast object is a data.table with a class forecast and an additional class that depends on the forecast type.

The arguments observed, predicted, etc. make it possible to rename existing columns of the input data to match the required columns for a forecast object. Using the argument forecast_unit, you can specify the columns that uniquely identify a single forecast (and thereby removing other, unneeded columns. See section "Forecast Unit" below for details).

Usage

as_forecast_multivariate_sample(data, ...)

# Default S3 method
as_forecast_multivariate_sample(
  data,
  joint_across,
  forecast_unit = NULL,
  observed = NULL,
  predicted = NULL,
  sample_id = NULL,
  ...
)

Arguments

data

A data.frame (or similar) with predicted and observed values. See the details section of for additional information on the required input format.

...

Unused

joint_across

Character vector with columns names that define the variables which are forecasted jointly. Conceptually, several univariate forecasts are pooled together to form a single multivariate forecasts. For example, if you have a column country and want to define a multivariate forecast for several countries at once, you could set joint_across = "country".

forecast_unit

(optional) Name of the columns in data (after any renaming of columns) that denote the unit of a single univariate (!) forecast. See get_forecast_unit() for details. If NULL (the default), all columns that are not required columns are assumed to form the unit of a single forecast. If specified, all columns that are not part of the forecast unit (or required columns) will be removed. Multivariate forecasts are defined by a) specifying the univariate forecast unit (i.e. the unit of a single forecast if that forecast were univariate) and b) specifying which variables are pooled together to form a multivariate forecast.

observed

(optional) Name of the column in data that contains the observed values. This column will be renamed to "observed".

predicted

(optional) Name of the column in data that contains the predicted values. This column will be renamed to "predicted".

sample_id

(optional) Name of the column in data that contains the sample id. This column will be renamed to "sample_id".

Value

A forecast object of class forecast_sample

Target format

The input for all further scoring needs to be a data.frame or similar with the following columns:

• observed: Column of type numeric with observed values.

• predicted: Column of type numeric with predicted values. Predicted values represent random samples from the predictive distribution.

• sample_id: Column of any type with unique identifiers (unique within a single forecast) for each sample.

• mv_group_id: Column of any type with unique identifiers (unique within a single forecast) for each multivariate group. This column is created automatically using the forecast_unit and the joint_across arguments.

For convenience, we recommend an additional column model holding the name of the forecaster or model that produced a prediction, but this is not strictly necessary.

See the example_sample_continuous and example_sample_discrete data set for an example

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 forecast. 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. (For a multivariate forecast, several univariate forecasts are pooled together to form a joint forecast. In the multivariate case, "forecast unit" still refers to the forecast unit of the univariate forecasts that are pooled together to form the multivariate 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.

See also

Other functions to create forecast objects: as_forecast_binary(), as_forecast_nominal(), as_forecast_ordinal(), as_forecast_point(), as_forecast_quantile(), as_forecast_sample()