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Automatic time series modelling with neural networks. Allows fully automatic, semi-manual or fully manual specification of networks. For details of the specification methodology see: (i) Crone and Kourentzes (2010) <doi:10.1016/j.neucom.2010.01.017>; and (ii) Kourentzes et al. (2014) <doi:10.1016/j.eswa.2013.12.011>.
Version: | 0.9.9 |
Depends: | generics |
Imports: | forecast, glmnet, neuralnet, plotrix, MASS, tsutils, uroot, methods |
Suggests: | thief |
Published: | 2023-11-15 |
DOI: | 10.32614/CRAN.package.nnfor |
Author: | Nikolaos Kourentzes [aut, cre] |
Maintainer: | Nikolaos Kourentzes <nikolaos at kourentzes.com> |
BugReports: | https://github.com/trnnick/nnfor/issues |
License: | GPL-3 |
URL: | https://kourentzes.com/forecasting/2019/01/16/tutorial-for-the-nnfor-r-package/ |
NeedsCompilation: | no |
Materials: | README NEWS |
In views: | TimeSeries |
CRAN checks: | nnfor results |
Reference manual: | nnfor.pdf |
Package source: | nnfor_0.9.9.tar.gz |
Windows binaries: | r-devel: nnfor_0.9.9.zip, r-release: nnfor_0.9.9.zip, r-oldrel: nnfor_0.9.9.zip |
macOS binaries: | r-release (arm64): nnfor_0.9.9.tgz, r-oldrel (arm64): nnfor_0.9.9.tgz, r-release (x86_64): nnfor_0.9.9.tgz, r-oldrel (x86_64): nnfor_0.9.9.tgz |
Old sources: | nnfor archive |
Reverse imports: | EEMDelm, hybridts, mrf, MSGARCHelm, stlELM, stlTDNN, tswge, VMDML, vmdTDNN |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.