Package: EEMDlstm 0.1.0
EEMDlstm: EEMD Based LSTM Model for Time Series Forecasting
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>.
Authors:
EEMDlstm_0.1.0.tar.gz
EEMDlstm_0.1.0.zip(r-4.5)EEMDlstm_0.1.0.zip(r-4.4)EEMDlstm_0.1.0.zip(r-4.3)
EEMDlstm_0.1.0.tgz(r-4.5-any)EEMDlstm_0.1.0.tgz(r-4.4-any)EEMDlstm_0.1.0.tgz(r-4.3-any)
EEMDlstm_0.1.0.tar.gz(r-4.5-noble)EEMDlstm_0.1.0.tar.gz(r-4.4-noble)
EEMDlstm_0.1.0.tgz(r-4.4-emscripten)EEMDlstm_0.1.0.tgz(r-4.3-emscripten)
EEMDlstm.pdf |EEMDlstm.html✨
EEMDlstm/json (API)
| # Install 'EEMDlstm' in R: |
| install.packages('EEMDlstm', repos = c('https://kapiliasri.r-universe.dev', 'https://cloud.r-project.org')) |
- Data_Maize - Monthly International Maize Price Data
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 3 years agofrom:a526c29d39. Checks:1 OK, 8 NOTE. Indexed: yes.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Mar 14 2025 |
| R-4.5-win | NOTE | Mar 14 2025 |
| R-4.5-mac | NOTE | Mar 14 2025 |
| R-4.5-linux | NOTE | Mar 14 2025 |
| R-4.4-win | NOTE | Mar 14 2025 |
| R-4.4-mac | NOTE | Mar 14 2025 |
| R-4.4-linux | NOTE | Mar 14 2025 |
| R-4.3-win | NOTE | Mar 14 2025 |
| R-4.3-mac | NOTE | Mar 14 2025 |
Dependencies:askpassbackportsbase64encBiocGenericsclicolorspaceconfigcurlfansifarverforecastfracdiffgenericsggplot2gluegreyboxgtableherehttrisobandjsonlitekeraslabelinglatticelifecyclelmtestmagrittrMAPAMASSMatrixmgcvmimemunsellnlmenloptrnnetopensslpillarpkgconfigplotrixpngpracmaprocessxpsquadprogquantmodR6rappdirsRColorBrewerRcppRcppArmadilloRcppTOMLreticulaterlangRlibeemdrprojrootrstudioapiscalessmoothstatmodsystensorflowtexregtfautographtfrunstibbletidyselecttimeDateTSdeeplearningtseriestsutilsTTRurcautf8vctrsviridisLitewhiskerwithrxtablextsyamlzeallotzoo
