The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
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>.
Version: | 0.1.2 |
Imports: | stats, wavelets, fracdiff, forecast |
Published: | 2022-07-02 |
DOI: | 10.32614/CRAN.package.WaveletArima |
Author: | Dr. Ranjit Kumar Paul [aut, cre], Mr. Sandipan Samanta [aut], Dr. Md Yeasin [aut] |
Maintainer: | Dr. Ranjit Kumar Paul <ranjitstat at gmail.com> |
License: | GPL-3 |
NeedsCompilation: | no |
CRAN checks: | WaveletArima results |
Reference manual: | WaveletArima.pdf |
Package source: | WaveletArima_0.1.2.tar.gz |
Windows binaries: | r-devel: WaveletArima_0.1.2.zip, r-release: WaveletArima_0.1.2.zip, r-oldrel: WaveletArima_0.1.2.zip |
macOS binaries: | r-release (arm64): WaveletArima_0.1.2.tgz, r-oldrel (arm64): WaveletArima_0.1.2.tgz, r-release (x86_64): WaveletArima_0.1.2.tgz, r-oldrel (x86_64): WaveletArima_0.1.2.tgz |
Old sources: | WaveletArima archive |
Reverse imports: | hybridts |
Please use the canonical form https://CRAN.R-project.org/package=WaveletArima to link to this page.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.