clean_forecast

The function makes it possible to silently validate an object. In addition,
it can return a copy of the data and remove rows with missing values.

internal

Facilitate the evaluation of forecasts in a convenient
framework based on data.table. It allows user to to check their forecasts
and diagnose issues, to visualise forecasts and missing data, to transform
data before scoring, to handle missing forecasts, to aggregate scores, and
to visualise the results of the evaluation. The package mostly focuses on
the evaluation of probabilistic forecasts and allows evaluating several
different forecast types and input formats. Find more information about the
package in the Vignettes as well as 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

Toshiaki Asakura 

James Mba Azam

Sebastian Funk

Michael Chirico

clean_forecast function

<dl><dt>forecast</dt>
<dd>A forecast object (a validated data.table with predicted and
observed values).</dd>
<dt>copy</dt>
<dd>Logical, default is <code>FALSE</code>. If <code>TRUE</code>, a copy of the input data
is created.</dd>
<dt>na.omit</dt>
<dd>Logical, default is <code>FALSE</code>. If <code>TRUE</code>, rows with missing
values are removed.</dd></dl>

Arguments

Clean forecast object — clean_forecast

<dl>

<dt>forecast</dt>
<dd>A forecast object (a validated data.table with predicted and
observed values).</dd>


<dt>copy</dt>
<dd>Logical, default is <code>FALSE</code>. If <code>TRUE</code>, a copy of the input data
is created.</dd>


<dt>na.omit</dt>
<dd>Logical, default is <code>FALSE</code>. If <code>TRUE</code>, rows with missing
values are removed.</dd>

</dl>

Clean forecast object

clean_forecast: Clean forecast object

Description

Usage

Arguments