Package: stlARIMA 0.1.0

stlARIMA: STL Decomposition and ARIMA Hybrid Forecasting Model

Univariate time series forecasting with STL decomposition based auto regressive integrated moving average (ARIMA) hybrid model. For method details see Xiong T, Li C, Bao Y (2018). <doi:10.1016/j.neucom.2017.11.053>.

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

stlARIMA_0.1.0.tar.gz
stlARIMA_0.1.0.zip(r-4.5)stlARIMA_0.1.0.zip(r-4.4)stlARIMA_0.1.0.zip(r-4.3)
stlARIMA_0.1.0.tgz(r-4.4-any)stlARIMA_0.1.0.tgz(r-4.3-any)
stlARIMA_0.1.0.tar.gz(r-4.5-noble)stlARIMA_0.1.0.tar.gz(r-4.4-noble)
stlARIMA_0.1.0.tgz(r-4.4-emscripten)stlARIMA_0.1.0.tgz(r-4.3-emscripten)
stlARIMA.pdf |stlARIMA.html✨
stlARIMA/json (API)

# Install 'stlARIMA' in R:
install.packages('stlARIMA', repos = c('https://ronit10976.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • Data_potato - Normalized Monthly Average Potato Price of India

On CRAN:

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

1.00 score 172 downloads 1 exports 45 dependencies

Last updated 3 years agofrom:a7a5f03b02. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 13 2024
R-4.5-winOKNov 13 2024
R-4.5-linuxOKNov 13 2024
R-4.4-winOKNov 13 2024
R-4.4-macOKNov 13 2024
R-4.3-winOKNov 13 2024
R-4.3-macOKNov 13 2024

Exports:STLARIMA

Dependencies:clicolorspacecurlfansifarverforecastfracdiffgenericsggplot2gluegtableisobandjsonlitelabelinglatticelifecyclelmtestmagrittrMASSMatrixmgcvmunsellnlmennetpillarpkgconfigquadprogquantmodR6RColorBrewerRcppRcppArmadillorlangscalestibbletimeDatetseriesTTRurcautf8vctrsviridisLitewithrxtszoo