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-4.7)stlARIMA_0.1.0.zip(r-4.6)stlARIMA_0.1.0.zip(r-4.5)
stlARIMA_0.1.0.tgz(r-4.6-any)stlARIMA_0.1.0.tgz(r-4.5-any)
stlARIMA_0.1.0.tar.gz(r-4.7-any)stlARIMA_0.1.0.tar.gz(r-4.6-any)
stlARIMA_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
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.

1.00 score 178 downloads 1 exports 31 dependencies

Last updated from:a7a5f03b02. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	143
source / vignettes	OK	139
linux-release-x86_64	OK	120
macos-release-arm64	OK	157
macos-oldrel-arm64	OK	211
windows-devel	OK	97
windows-release	OK	85
windows-oldrel	OK	80
wasm-release	OK	105