as_forecast_quantile: Create a 'forecast' object for quantile-based forecasts

In epiforecasts/scoringutils: Utilities for Scoring and Assessing Predictions

Create a forecast object for quantile-based forecasts

Description

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_quantile(data, ...)

## Default S3 method:
as_forecast_quantile(
  data,
  forecast_unit = NULL,
  observed = NULL,
  predicted = NULL,
  quantile_level = NULL,
  ...
)

## S3 method for class 'forecast_sample'
as_forecast_quantile(
  data,
  probs = c(0.05, 0.25, 0.5, 0.75, 0.95),
  type = 7,
  ...
)

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
forecast_unit	(optional) Name of the columns in data (after any renaming of columns) that denote the unit of a single 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.
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".
quantile_level	(optional) Name of the column in data that contains the quantile level of the predicted values. This column will be renamed to "quantile_level". Only applicable to quantile-based forecasts.
probs	A numeric vector of quantile levels for which quantiles will be computed. Corresponds to the probs argument in quantile().
type	Type argument passed down to the quantile function. For more information, see quantile().

Value

A forecast object of class forecast_quantile

Required input

The input 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 quantiles of the predictive distribution.

• quantile_level: Column of type numeric, denoting the quantile level of the corresponding predicted value. Quantile levels must be between 0 and 1.

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_quantile data set for an example.

Converting from forecast_sample to forecast_quantile

When creating a forecast_quantile object from a forecast_sample object, the quantiles are estimated by computing empircal quantiles from the samples via quantile(). Note that empirical quantiles are a biased estimator for the true quantiles in particular in the tails of the distribution and when the number of available samples is low.

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.

See Also

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

Examples

as_forecast_quantile(
  example_quantile,
  predicted = "predicted",
  forecast_unit = c("model", "target_type", "target_end_date",
                    "horizon", "location")
)

epiforecasts/scoringutils documentation built on Dec. 11, 2024, 11:12 a.m.