continuous_example_data

A toy dataset for a probabilistic forecast of a continuous outcome variable

datasets

Combines a collection of metrics and proper scoring rules
(Tilmann Gneiting & Adrian E Raftery (2007)
<doi:10.1198/016214506000001437>) with an easy to
use wrapper that can be used to automatically evaluate predictions.
Apart from proper scoring rules functions are provided to assess bias,
sharpness and calibration (Sebastian Funk, Anton Camacho, Adam J. Kucharski,
Rachel Lowe, Rosalind M. Eggo, W. John Edmunds (2019)
<doi:10.1371/journal.pcbi.1006785>) of forecasts.
Several types of predictions can be evaluated:
probabilistic forecasts (generally
predictive samples generated by Markov Chain Monte Carlo procedures),
quantile forecasts or point forecasts. Observed values and predictions
can be either continuous, integer, or binary. Users can either choose
to apply these rules separately in a vector / matrix format that can
be flexibly used within other packages, or they can choose to do an
automatic evaluation of their forecasts. This is implemented with
'data.table' and provides a consistent and very efficient framework for
evaluating various types of predictions.

Nikos Bosse

scoringutils

Utilities for Scoring and Assessing Predictions

Sam Abbott 

Joel Hellewell

Sophie Meakins 

James Munday

Katharine Sherratt

Sebastian Funk

continuous_example_data function

A data frame with 6000 rows and 6 variables:<dl class="dl-horizontal">
 <dt>id</dt><dd>unique identifier for true observed values</dd>
 <dt>model</dt><dd>name of the model that generated the forecasts</dd>
 <dt>horizon</dt><dd>forecast horzin (e.g. 1 day ahead forecast)</dd>
 <dt>true_value</dt><dd>true observed values</dd>
 <dt>sample</dt><dd>number that identifies the predictive sample generated by
 a specific model for a specific observed value</dd>
 <dt>prediction</dt><dd>predictive sample for the corresponding true
 value</dd>
</dl>

Format

Continuous Example Data — continuous_example_data

A data frame with 6000 rows and 6 variables:<dl class='dl-horizontal'>
 <dt>id</dt><dd>unique identifier for true observed values</dd>
 <dt>model</dt><dd>name of the model that generated the forecasts</dd>
 <dt>horizon</dt><dd>forecast horzin (e.g. 1 day ahead forecast)</dd>
 <dt>true_value</dt><dd>true observed values</dd>
 <dt>sample</dt><dd>number that identifies the predictive sample generated by
 a specific model for a specific observed value</dd>
 <dt>prediction</dt><dd>predictive sample for the corresponding true
 value</dd>
</dl>

continuous_example_data: Continuous Example Data

Description

Usage

Arguments

Format