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Application of empirical mode decomposition based artificial neural network model for nonlinear and non stationary univariate time series forecasting. For method details see (i) Choudhury (2019) <https://www.indianjournals.com/ijor.aspx?target=ijor:ijee3&volume=55&issue=1&article=013>; (ii) Das (2020) <https://www.indianjournals.com/ijor.aspx?target=ijor:ijee3&volume=56&issue=2&article=002>.
Version: | 0.2.0 |
Depends: | EMD, forecast |
Suggests: | knitr, rmarkdown, testthat (≥ 3.0.0) |
Published: | 2023-09-14 |
DOI: | 10.32614/CRAN.package.EMDANNhybrid |
Author: | Pankaj Das [aut, cre], Achal Lama [aut], Girish Kumar Jha [aut] |
Maintainer: | Pankaj Das <pankaj.das2 at icar.gov.in> |
License: | GPL-3 |
NeedsCompilation: | no |
CRAN checks: | EMDANNhybrid results |
Reference manual: | EMDANNhybrid.pdf |
Vignettes: |
EMDANNhybrid |
Package source: | EMDANNhybrid_0.2.0.tar.gz |
Windows binaries: | r-devel: EMDANNhybrid_0.2.0.zip, r-release: EMDANNhybrid_0.2.0.zip, r-oldrel: EMDANNhybrid_0.2.0.zip |
macOS binaries: | r-release (arm64): EMDANNhybrid_0.2.0.tgz, r-oldrel (arm64): EMDANNhybrid_0.2.0.tgz, r-release (x86_64): EMDANNhybrid_0.2.0.tgz, r-oldrel (x86_64): EMDANNhybrid_0.2.0.tgz |
Old sources: | EMDANNhybrid archive |
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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.