tsfgrnn: Time Series Forecasting Using GRNN

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 Weizhong Yan (2012) <doi:10.1109/TNNLS.2012.2198074>. 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.

Version: 0.1.0
Imports: ggplot2
Suggests: testthat, knitr, rmarkdown
Published: 2019-11-25
Author: Maria Pilar Frias-Bustamante [aut], Ana Maria Martinez-Rodriguez [aut], Antonio Conde-Sanchez [aut], Francisco Martinez [aut, cre]
Maintainer: Francisco Martinez <fmartin at ujaen.es>
BugReports: https://github.com/franciscomartinezdelrio/tsfgrnn
License: GPL-2
URL: https://github.com/franciscomartinezdelrio/tsfgrnn
NeedsCompilation: no
Materials: README NEWS
CRAN checks: tsfgrnn results

Downloads:

Reference manual: tsfgrnn.pdf
Vignettes: tsfgrnn
Package source: tsfgrnn_0.1.0.tar.gz
Windows binaries: r-devel: tsfgrnn_0.1.0.zip, r-devel-gcc8: tsfgrnn_0.1.0.zip, r-release: tsfgrnn_0.1.0.zip, r-oldrel: tsfgrnn_0.1.0.zip
OS X binaries: r-release: tsfgrnn_0.1.0.tgz, r-oldrel: tsfgrnn_0.1.0.tgz

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