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>.

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

Documentation:

Reference manual:

WaveletArima.pdf

Downloads:

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 dependencies:

Reverse imports:

hybridts

Linking:

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