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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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These binaries (installable software) and packages are in development.
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