grnn_weights

It shows the input vector and the weights of the training examples used in a
prediction associated with a "grnnForecast" object.

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

grnn_weights function

<dl><dt>forecast</dt>
<dd>A <code>grnnForecast</code> object.</dd></dl>

Arguments

Training examples and their corresponding weights used in a prediction — grnn_weights

<dl>

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

</dl>

grnn_weights: Training examples and their corresponding weights used in a prediction

Description

Usage

Value

Arguments

Examples