example_continuous

A data set with continuous predictions for COVID-19 cases and deaths
constructed from data submitted to the European Forecast Hub.

datasets

Provides a collection of metrics and proper scoring rules
(Tilmann Gneiting & Adrian E Raftery (2007)
<doi:10.1198/016214506000001437>, Jordan, A., Krüger, F., & Lerch, S. (2019)
<doi:10.18637/jss.v090.i12>) within a consistent framework for
evaluation, comparison and visualisation of forecasts.
In addition to 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 (e.g. binary, discrete, continuous) which may
come in different formats (e.g. forecasts represented by predictive samples
or by quantiles of the predictive distribution) can be evaluated.
Scoring metrics can be used either through a convenient data.frame format,
or can be applied as individual functions in a vector / matrix format.
All functionality has been implemented with a focus on performance and is
robustly tested. Find more information about the package in the
accompanying paper (<doi:10.48550/arXiv.2205.07090>).

Nikos Bosse

scoringutils

Utilities for Scoring and Assessing Predictions

Sam Abbott 

Hugo Gruson

Johannes Bracher 

Sebastian Funk

example_continuous function

A data frame with 13,429 rows and 10 columns:<dl>
<dt>location</dt>
<dd>the country for which a prediction was made</dd><dt>target_end_date</dt>
<dd>the date for which a prediction was made</dd><dt>target_type</dt>
<dd>the target to be predicted (cases or deaths)</dd><dt>true_value</dt>
<dd>true observed values</dd><dt>location_name</dt>
<dd>name of the country for which a prediction was made</dd><dt>forecast_date</dt>
<dd>the date on which a prediction was made</dd><dt>model</dt>
<dd>name of the model that generated the forecasts</dd><dt>horizon</dt>
<dd>forecast horizon in weeks</dd><dt>prediction</dt>
<dd>predicted value</dd><dt>sample</dt>
<dd>id for the corresponding sample</dd>
</dl>

Format

Continuous Forecast Example Data — example_continuous

A data frame with 13,429 rows and 10 columns:<dl>
<dt>location</dt>
<dd>the country for which a prediction was made</dd>

<dt>target_end_date</dt>
<dd>the date for which a prediction was made</dd>

<dt>target_type</dt>
<dd>the target to be predicted (cases or deaths)</dd>

<dt>true_value</dt>
<dd>true observed values</dd>

<dt>location_name</dt>
<dd>name of the country for which a prediction was made</dd>

<dt>forecast_date</dt>
<dd>the date on which a prediction was made</dd>

<dt>model</dt>
<dd>name of the model that generated the forecasts</dd>

<dt>horizon</dt>
<dd>forecast horizon in weeks</dd>

<dt>prediction</dt>
<dd>predicted value</dd>

<dt>sample</dt>
<dd>id for the corresponding sample</dd>


</dl>

example_continuous: Continuous Forecast Example Data

Description

Usage

Arguments

Format

Details