The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
Univariate time series forecasting with STL decomposition based Extreme Learning Machine hybrid model. For method details see Xiong T, Li C, Bao Y (2018). <doi:10.1016/j.neucom.2017.11.053>.
| Version: | 0.1.1 |
| Depends: | R (≥ 2.10) |
| Imports: | forecast, nnfor |
| Published: | 2022-08-09 |
| DOI: | 10.32614/CRAN.package.stlELM |
| Author: | Girish Kumar Jha [aut, cre], Ronit Jaiswal [aut, ctb], Kapil Choudhary [ctb], Rajeev Ranjan Kumar [ctb] |
| Maintainer: | Girish Kumar Jha <girish.stat at gmail.com> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| In views: | Agriculture |
| CRAN checks: | stlELM results |
| Reference manual: | stlELM.pdf |
| Package source: | stlELM_0.1.1.tar.gz |
| Windows binaries: | r-devel: stlELM_0.1.1.zip, r-release: stlELM_0.1.1.zip, r-oldrel: stlELM_0.1.1.zip |
| macOS binaries: | r-release (arm64): stlELM_0.1.1.tgz, r-oldrel (arm64): stlELM_0.1.1.tgz, r-release (x86_64): stlELM_0.1.1.tgz, r-oldrel (x86_64): stlELM_0.1.1.tgz |
| Old sources: | stlELM archive |
Please use the canonical form https://CRAN.R-project.org/package=stlELM to link to this page.
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.
Health stats visible at Monitor.