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'))

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 exports 0.00 score 45 dependencies 186 downloads

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

Target	Result	Date
Doc / Vignettes	OK	Sep 14 2024
R-4.5-win	OK	Sep 14 2024
R-4.5-linux	OK	Sep 14 2024
R-4.4-win	OK	Sep 14 2024
R-4.4-mac	OK	Sep 14 2024
R-4.3-win	OK	Sep 14 2024
R-4.3-mac	OK	Sep 14 2024