predict.online

Calculates predictions based on new expert advice.
This does not update weights. If new observations are available
use update instead. The latter updates and weights and computes predictions.

Combine probabilistic forecasts using CRPS learning algorithms proposed in Berrisch, Ziel (2021) <doi:10.48550/arXiv.2102.00968> <doi:10.1016/j.jeconom.2021.11.008>. The package implements multiple online learning algorithms like Bernstein online aggregation; see Wintenberger (2014) <doi:10.48550/arXiv.1404.1356>. Quantile regression is also implemented for comparison purposes. Model parameters can be tuned automatically with respect to the loss of the forecast combination. Methods like predict(), update(), plot() and print() are available for convenience. This package utilizes the optim C++ library for numeric optimization <https://github.com/kthohr/optim>.

Jonathan Berrisch

profoc

Probabilistic Forecast Combination Using CRPS Learning

Florian Ziel

predict.online function

<dl><dt>object</dt>
<dd>Object of class inheriting from 'online'</dd>
<dt>new_experts</dt>
<dd>new expert predictions</dd>
<dt>update_model</dt>
<dd>Defines whether the model object should be updated or not.
If TRUE, new forecaster and expert predictions are appended onto
the respective object items. Defaults to TRUE.</dd>
<dt>...</dt>
<dd>further arguments are ignored</dd></dl>

Arguments

Predict method for online models — predict.online

<dl>

<dt>object</dt>
<dd>Object of class inheriting from 'online'</dd>


<dt>new_experts</dt>
<dd>new expert predictions</dd>


<dt>update_model</dt>
<dd>Defines whether the model object should be updated or not.
If TRUE, new forecaster and expert predictions are appended onto
the respective object items. Defaults to TRUE.</dd>


<dt>...</dt>
<dd>further arguments are ignored</dd>

</dl>

predict.online: Predict method for online models

Description

Usage

Value

Arguments