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Forecasting univariate time series with ensemble empirical mode decomposition (EEMD) with long short-term memory (LSTM). For method details see Jaiswal, R. et al. (2022). <doi:10.1007/s00521-021-06621-3>.
| Version: | 0.1.0 |
| Depends: | R (≥ 2.10) |
| Imports: | keras, tensorflow, reticulate, tsutils, BiocGenerics, utils, graphics, magrittr, Rlibeemd, TSdeeplearning |
| Published: | 2022-09-26 |
| Author: | Kapil Choudhary [aut, cre], Girish Kumar Jha [aut, ths, ctb], Ronit Jaiswal [ctb], Rajeev Ranjan Kumar [ctb] |
| Maintainer: | Kapil Choudhary <kapiliasri at gmail.com> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| CRAN checks: | EEMDlstm results |
| Reference manual: | EEMDlstm.pdf |
| Package source: | EEMDlstm_0.1.0.tar.gz |
| Windows binaries: | r-devel: EEMDlstm_0.1.0.zip, r-release: EEMDlstm_0.1.0.zip, r-oldrel: EEMDlstm_0.1.0.zip |
| macOS binaries: | r-release (arm64): EEMDlstm_0.1.0.tgz, r-oldrel (arm64): EEMDlstm_0.1.0.tgz, r-release (x86_64): EEMDlstm_0.1.0.tgz, r-oldrel (x86_64): EEMDlstm_0.1.0.tgz |
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