plot.grnnForecastRO

It plots the forecast associated with a test set generated with the function
<code>rolling_origin</code>.

A general regression neural network (GRNN) is a variant of a
Radial Basis Function Network characterized by a fast single-pass learning.
'tsfgrnn' allows you to forecast time series using a GRNN model Francisco
Martinez et al. (2019) <doi:10.1007/978-3-030-20521-8_17> and Francisco
Martinez et al. (2022) <doi:10.1016/j.neucom.2021.12.028>. When the forecasting
horizon is higher than 1, two multi-step ahead forecasting strategies can be used.
The model built is autoregressive, that is, it is only based on the
observations of the time series. You can consult and plot how the
prediction was done. It is also possible to assess the forecasting accuracy
of the model using rolling origin evaluation.

Francisco Martinez

tsfgrnn

Time Series Forecasting Using GRNN

Maria Pilar Frias-Bustamante

Ana Maria Martinez-Rodriguez

Antonio Conde-Sanchez

plot.grnnForecastRO function

<dl><dt>x</dt>
<dd>the object obtained from a call to <code>rolling_origin</code>.</dd>
<dt>h</dt>
<dd>an integer. The forecasting horizon. If <code>NULL</code> (the default),
the maximum forecasting horizon of all the test sets is used.</dd>
<dt>...</dt>
<dd>Other plotting parameters to affect the plot.</dd></dl>

Arguments

Plot the prediction for a test set — plot.grnnForecastRO

<dl>

<dt>x</dt>
<dd>the object obtained from a call to <code>rolling_origin</code>.</dd>


<dt>h</dt>
<dd>an integer. The forecasting horizon. If <code>NULL</code> (the default),
the maximum forecasting horizon of all the test sets is used.</dd>


<dt>...</dt>
<dd>Other plotting parameters to affect the plot.</dd>

</dl>

plot.grnnForecastRO: Plot the prediction for a test set

Description

Usage

Value

Arguments