Getting started with qrensemble

The qrensemble package can be used to train ensemble models from forecasts given in quantile format. It uses the quantgen package for the underlying optimisation.

It works with data frames of forecasts and data, similar to the ones used in the scoringutils package. The outputs it produces are in the same format.

Definitions

The qra() function which is at the heart of the package needs a complete set of past forecasts and observations in order to optimise predictive performance of the resulting ensemble. It works with the forecast format defined by the scoringutils package which can be constructed from data frames of forecasts and data using the as_forecast() function. This function defines the column that determine a “forecast unit”. A subgroup of these and the quantile_level column can be used in qra() are treated as “pooling variables”. These are variables that are seen to representing the same forecasting task, and they are treated as nuisance variables when constributing the ensmble. For example, if horizon is a column that represents how far ahead a forecast has been made, and it is treated as a pooling variable, then each ensemble will cover all horizons, and will have weights constructed using forecasts and scores across all horizons. If is not treated as a pooling variable, then a separate ensemble will be created for each horizon.

The complimentary set of the pooling variables amongst all columns representing a forecast unit are so-called grouping variables. An ensemble is created for each combination of grouping variables. For example, if horizon is specified as a grouping variable then it is not treated as pooling variable, an ensemble will be created for each horizon (and/or combination of horizon and any other grouping variable). If it is not specified as grouping variable it is treated as pooling variable.

Prerequisites

In order to create an ensemble, each composite model needs to be present for each combination of pooling and grouping variables that is present in the data. Any model with missing forecasts is excluded from the ensemble.

Example

Create an ensemble for each location, and separately for cases and deaths, for the 24th of July 2021:

library("scoringutils")
example_quantile |>
  as_forecast_quantile() |>
  qra(
    group = c("target_type", "location", "location_name"),
    target = c(target_end_date = "2021-07-24")
  )