WaveletETS: Wavelet Based Error Trend Seasonality Model

ETS stands for Error, Trend, and Seasonality, and it is a popular time series forecasting method. Wavelet decomposition can be used for denoising, compression, and feature extraction of signals. By removing the high-frequency components, wavelet decomposition can remove noise from the data while preserving important features. A hybrid Wavelet ETS (Error Trend-Seasonality) model has been developed for time series forecasting using algorithm of Anjoy and Paul (2017) <doi:10.1007/s00521-017-3289-9>.

Version: 0.1.0
Imports: dplyr, Metrics, tseries, stats, wavelets, forecast, caretForecast
Published: 2023-04-05
Author: Dr. Ranjit Kumar Paul [aut], Dr. Md Yeasin [aut, cre]
Maintainer: Dr. Md Yeasin <yeasin.iasri at gmail.com>
License: GPL-3
NeedsCompilation: no
CRAN checks: WaveletETS results


Reference manual: WaveletETS.pdf


Package source: WaveletETS_0.1.0.tar.gz
Windows binaries: r-devel: WaveletETS_0.1.0.zip, r-release: WaveletETS_0.1.0.zip, r-oldrel: WaveletETS_0.1.0.zip
macOS binaries: r-release (arm64): WaveletETS_0.1.0.tgz, r-oldrel (arm64): WaveletETS_0.1.0.tgz, r-release (x86_64): WaveletETS_0.1.0.tgz, r-oldrel (x86_64): WaveletETS_0.1.0.tgz


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