Package: EEMDlstm 1.0.1

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:Kapil Choudhary [aut], Girish Kumar Jha [aut, ths, ctb], Ronit Jaiswal [ctb, cre], Rajeev Ranjan Kumar [ctb]

EEMDlstm_1.0.1.tar.gz
EEMDlstm_1.0.1.zip(r-4.7)EEMDlstm_1.0.1.zip(r-4.6)EEMDlstm_1.0.1.zip(r-4.5)
EEMDlstm_1.0.1.tgz(r-4.6-any)EEMDlstm_1.0.1.tgz(r-4.5-any)
EEMDlstm_1.0.1.tar.gz(r-4.7-any)EEMDlstm_1.0.1.tar.gz(r-4.6-any)
EEMDlstm_1.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
EEMDlstm/json (API)

# Install 'EEMDlstm' in R:
install.packages('EEMDlstm', repos = c('https://ronit10976.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:
  • Data_Maize - Monthly International Maize Price Data

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 547 downloads 2 exports 73 dependencies

Last updated from:3bc62d4a86. Checks:7 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64NOTE185
source / vignettesOK227
linux-release-x86_64NOTE218
macos-release-arm64NOTE142
macos-oldrel-arm64NOTE102
windows-develNOTE115
windows-releaseNOTE119
windows-oldrelNOTE106
wasm-releaseOK148

Exports:eemdLSTMemdLSTM

Dependencies:askpassbackportsbase64encBiocGenericsclicolorspaceconfigcpp11curlfarverforecastfracdiffgenericsggplot2gluegreyboxgtableherehttrisobandjsonlitekeraslabelinglatticelifecyclelmtestmagrittrMAPAMASSMatrixmimenlmenloptrnnetopensslplotrixpngpracmaprocessxpsR6rappdirsRColorBrewerRcppRcppArmadilloRcppTOMLreticulaterlangRlibeemdrprojrootrstudioapiS7scalessmoothstatmodsystensorflowtexregtfautographtfrunstidyselecttimeDateTSdeeplearningtsutilsurcavctrsviridisLitewhiskerwithrxtableyamlzeallotzoo