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 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) and Francisco Martinez et al. (2022) . 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 Roxygen: list(markdown = TRUE) 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 Repository: https://franciscomartinezdelrio.r-universe.dev Date/Publication: 2024-02-15 08:35:10 UTC RemoteUrl: https://github.com/franciscomartinezdelrio/tsfgrnn RemoteRef: HEAD RemoteSha: 3dea6b4fa42ef2ffcf29a92bd8c21541dc3b7d41 NeedsCompilation: yes Packaged: 2026-07-04 06:03:07 UTC; root Author: Maria Pilar Frias-Bustamante [aut], Ana Maria Martinez-Rodriguez [aut], Antonio Conde-Sanchez [aut], Francisco Martinez [aut, cre]