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tsfgrnn
View on CRAN: Click
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Download and install tsfgrnn package within the R console
Install from CRAN:
install.packages("tsfgrnn")
Install from Github:
library("remotes")
install_github("cran/tsfgrnn") Install by package version:
library("remotes")
install_version("tsfgrnn", "1.0.5") Attach the package and use:
library("tsfgrnn")
Maintained by
Francisco Martinez
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First Published: 2019-11-25
Latest Update: 2024-02-15
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.
How to cite:
Francisco Martinez (2019). tsfgrnn: Time Series Forecasting Using GRNN. R package version 1.0.5, https://cran.r-project.org/web/packages/tsfgrnn. Accessed 26 Aug. 2026.
Previous versions and publish date:
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Complete documentation for tsfgrnn
Functions, R codes and Examples using
the tsfgrnn R package
Some associated functions: autoplot.grnnForecast . grnn_examples . grnn_forecasting . grnn_weights . plot.grnnForecastRO . plot_example . predict.grnnForecast . rolling_origin .
Some associated R codes: RcppExports.R . forecasting.R . grnn.R . plotting.R . print_grnnForecast.R . rolling_origin.R . tsfgrnn-package.R . utilities.R . Full tsfgrnn package functions and examples
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