update.online

Continues learning using new observations and new expert advice.

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

update.online function

<dl><dt>object</dt>
<dd>Object of class inheriting from 'online'</dd>
<dt>new_y</dt>
<dd>new observations</dd>
<dt>new_experts</dt>
<dd>new expert predictions. This must be left unspecified</dd>
<dt>trace</dt>
<dd>If a progress bar shall be shown. Defaults to FALSE
if the model already contains the expert predictions corresponding to new_y.</dd>
<dt>...</dt>
<dd>further arguments are ignored</dd></dl>

Arguments

Update method for online models — update.online

<dl>

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


<dt>new_y</dt>
<dd>new observations</dd>


<dt>new_experts</dt>
<dd>new expert predictions. This must be left unspecified</dd>


<dt>trace</dt>
<dd>If a progress bar shall be shown. Defaults to FALSE
if the model already contains the expert predictions corresponding to new_y.</dd>


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

</dl>

update.online: Update method for online models

Description

Usage

Value

Arguments