A general regression neural network (GRNN) is a variant of a Radial Basis Function Network characterized by a fast singlepass learning. 'tsfgrnn' allows you to forecast time series using a GRNN model Francisco Martinez et al. (2019) <doi:10.1007/9783030205218_17> and Weizhong Yan (2012) <doi:10.1109/TNNLS.2012.2198074>. When the forecasting horizon is higher than 1, two multistep 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.
Package details 


Author  Maria Pilar FriasBustamante [aut], Ana Maria MartinezRodriguez [aut], Antonio CondeSanchez [aut], Francisco Martinez [aut, cre] 
Maintainer  Francisco Martinez <fmartin@ujaen.es> 
License  GPL2 
Version  1.0.1 
URL  https://github.com/franciscomartinezdelrio/tsfgrnn 
Package repository  View on CRAN 
Installation 
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