Package: eemdARIMA 0.1.0

eemdARIMA: EEMD Based Auto Regressive Integrated Moving Average Model

Forecasting time series with different decomposition based ARIMA models. For method details see Yu L, Wang S, Lai KK (2008). <doi:10.1016/j.eneco.2008.05.003>.

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

eemdARIMA_0.1.0.tar.gz
eemdARIMA_0.1.0.zip(r-4.7-any)eemdARIMA_0.1.0.zip(r-4.6-any)eemdARIMA_0.1.0.zip(r-4.5-any)
eemdARIMA_0.1.0.tgz(r-4.6-any)eemdARIMA_0.1.0.tgz(r-4.5-any)
eemdARIMA_0.1.0.tar.gz(r-4.7-any)eemdARIMA_0.1.0.tar.gz(r-4.6-any)
eemdARIMA_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
eemdARIMA/json (API)

# Install 'eemdARIMA' in R:
install.packages('eemdARIMA', repos = c('https://rrk4910.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 310 downloads 2 exports 32 dependencies

Last updated from:1d0a31c494. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK127
source / vignettesOK192
linux-releaseOK118
macos-releaseOK170
macos-oldrelOK150
windows-develOK83
windows-releaseOK88
windows-oldrelOK82
wasm-releaseOK105

Exports:EEMDARIMAemdARIMA

Dependencies:clicolorspacecpp11farverforecastfracdiffgenericsggplot2gluegtableisobandlabelinglatticelifecyclelmtestmagrittrnlmennetR6RColorBrewerRcppRcppArmadillorlangRlibeemdS7scalestimeDateurcavctrsviridisLitewithrzoo