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✨
DESCRIPTION
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 248 downloads 1 exports 31 dependencies

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

Target	Result	Time
linux-devel-x86_64	OK	127
source / vignettes	OK	180
linux-release-x86_64	OK	128
macos-release-arm64	OK	205
macos-oldrel-arm64	OK	201
windows-devel	OK	80
windows-release	OK	78
windows-oldrel	OK	71
wasm-release	OK	126