rolling_origin

It uses the model and the time series associated with a <code>grnnForecast</code>
object to assess the forecasting accuracy of the model using the last
<code>h</code> values of the time series to build test sets applying a rolling
origin evaluation.

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

rolling_origin function

<dl><dt>grnnf</dt>
<dd>A <code>grnnForecast</code> object.</dd>
<dt>h</dt>
<dd>A positive integer. The forecast horizon. If <code>NULL</code> (the
default) the prediction horizon of the <code>gnnForecast</code> object is used.</dd>
<dt>rolling</dt>
<dd>A logical. If <code>TRUE</code> (the default), forecasting horizons
from 1 to <code>h</code> are used. Otherwise, only horizon <code>h</code> is used.</dd></dl>

Arguments

Assessing forecasting accuracy with rolling origin — rolling_origin

<dl>

<dt>grnnf</dt>
<dd>A <code>grnnForecast</code> object.</dd>


<dt>h</dt>
<dd>A positive integer. The forecast horizon. If <code>NULL</code> (the
default) the prediction horizon of the <code>gnnForecast</code> object is used.</dd>


<dt>rolling</dt>
<dd>A logical. If <code>TRUE</code> (the default), forecasting horizons
from 1 to <code>h</code> are used. Otherwise, only horizon <code>h</code> is used.</dd>

</dl>

rolling_origin: Assessing forecasting accuracy with rolling origin

Description

Usage

Value

Arguments

Details

Examples