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Implementation of hybrid STL decomposition based time delay neural network model for univariate time series forecasting. For method details see Jha G K, Sinha, K (2014). <doi:10.1007/s00521-012-1264-z>, Xiong T, Li C, Bao Y (2018). <doi:10.1016/j.neucom.2017.11.053>.
| Version: | 0.1.0 |
| Depends: | R (≥ 2.10) |
| Imports: | forecast, nnfor |
| Published: | 2021-02-24 |
| DOI: | 10.32614/CRAN.package.stlTDNN |
| 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 |
| CRAN checks: | stlTDNN results |
| Reference manual: | stlTDNN.pdf |
| Package source: | stlTDNN_0.1.0.tar.gz |
| Windows binaries: | r-devel: stlTDNN_0.1.0.zip, r-release: stlTDNN_0.1.0.zip, r-oldrel: stlTDNN_0.1.0.zip |
| macOS binaries: | r-release (arm64): stlTDNN_0.1.0.tgz, r-oldrel (arm64): stlTDNN_0.1.0.tgz, r-release (x86_64): stlTDNN_0.1.0.tgz, r-oldrel (x86_64): stlTDNN_0.1.0.tgz |
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These binaries (installable software) and packages are in development.
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