Package: WaveletArima 0.1.2

WaveletArima: Wavelet-ARIMA Model for Time Series Forecasting

Noise in the time-series data significantly affects the accuracy of the ARIMA model. Wavelet transformation decomposes the time series data into subcomponents to reduce the noise and help to improve the model performance. The wavelet-ARIMA model can achieve higher prediction accuracy than the traditional ARIMA model. This package provides Wavelet-ARIMA model for time series forecasting based on the algorithm by Aminghafari and Poggi (2012) and Paul and Anjoy (2018) <doi:10.1142/S0219691307002002> <doi:10.1007/s00704-017-2271-x>.

Authors:Dr. Ranjit Kumar Paul [aut, cre], Mr. Sandipan Samanta [aut], Dr. Md Yeasin [aut]

WaveletArima_0.1.2.tar.gz
WaveletArima_0.1.2.zip(r-4.7)WaveletArima_0.1.2.zip(r-4.6)WaveletArima_0.1.2.zip(r-4.5)
WaveletArima_0.1.2.tgz(r-4.6-any)WaveletArima_0.1.2.tgz(r-4.5-any)
WaveletArima_0.1.2.tar.gz(r-4.7-any)WaveletArima_0.1.2.tar.gz(r-4.6-any)
WaveletArima_0.1.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
WaveletArima/json (API)

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

On CRAN:

Conda:

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

1.62 score 1 packages 14 scripts 170 downloads 2 exports 32 dependencies

Last updated from:c326e242ea. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK126
source / vignettesOK163
linux-release-x86_64OK122
macos-release-arm64OK126
macos-oldrel-arm64OK114
windows-develOK100
windows-releaseOK90
windows-oldrelOK80
wasm-releaseOK101

Exports:WaveletFittingWaveletFittingarma

Dependencies:clicolorspacecpp11farverforecastfracdiffgenericsggplot2gluegtableisobandlabelinglatticelifecyclelmtestmagrittrnlmennetR6RColorBrewerRcppRcppArmadillorlangS7scalestimeDateurcavctrsviridisLitewaveletswithrzoo