Package: tsfgrnn
Type: Package
Title: Time Series Forecasting Using GRNN
Version: 1.0.5
Authors@R: c(
    person("Maria Pilar", "Frias-Bustamante", role = c("aut"), email = "mpfrias@ujaen.es"),
    person("Ana Maria", "Martinez-Rodriguez", role = c("aut"), email = "ammartin@ujaen.es"),
    person("Antonio", "Conde-Sanchez", role = c("aut"), email = "aconde@ujaen.es"),
    person("Francisco", "Martinez", role = c("aut", "cre"), email = "fmartin@ujaen.es"))
Maintainer: Francisco Martinez <fmartin@ujaen.es>
Description: 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.
License: GPL-2
Encoding: UTF-8
RoxygenNote: 7.2.3
Suggests: testthat (>= 3.0.0), knitr, rmarkdown
Imports: ggplot2, Rcpp
VignetteBuilder: knitr
URL: https://github.com/franciscomartinezdelrio/tsfgrnn
BugReports: https://github.com/franciscomartinezdelrio/tsfgrnn
LinkingTo: Rcpp
Config/testthat/edition: 3
NeedsCompilation: yes
Packaged: 2024-02-15 09:00:58 UTC; UJA
Author: Maria Pilar Frias-Bustamante [aut],
  Ana Maria Martinez-Rodriguez [aut],
  Antonio Conde-Sanchez [aut],
  Francisco Martinez [aut, cre]
Repository: CRAN
Date/Publication: 2024-02-15 09:10:02 UTC
Built: R 4.6.0; x86_64-apple-darwin20; 2026-04-28 12:39:47 UTC; unix
Archs: tsfgrnn.so.dSYM
