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Wavelet decomposition method is very useful for modelling noisy time series data. Wavelet decomposition using 'haar' algorithm has been implemented to developed hybrid Wavelet GBM (Gradient Boosting Method) model for time series forecasting using algorithm by Anjoy and Paul (2017) <doi:10.1007/s00521-017-3289-9>.
| Version: | 0.1.0 |
| Imports: | caret, dplyr, caretForecast, Metrics, tseries, stats, wavelets, gbm |
| Published: | 2023-04-07 |
| DOI: | 10.32614/CRAN.package.WaveletGBM |
| Author: | Dr. Ranjit Kumar Paul [aut, cre], Dr. Md Yeasin [aut] |
| Maintainer: | Dr. Ranjit Kumar Paul <ranjitstat at gmail.com> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| CRAN checks: | WaveletGBM results |
| Reference manual: | WaveletGBM.pdf |
| Package source: | WaveletGBM_0.1.0.tar.gz |
| Windows binaries: | r-devel: WaveletGBM_0.1.0.zip, r-release: WaveletGBM_0.1.0.zip, r-oldrel: WaveletGBM_0.1.0.zip |
| macOS binaries: | r-release (arm64): WaveletGBM_0.1.0.tgz, r-oldrel (arm64): WaveletGBM_0.1.0.tgz, r-release (x86_64): WaveletGBM_0.1.0.tgz, r-oldrel (x86_64): WaveletGBM_0.1.0.tgz |
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These binaries (installable software) and packages are in development.
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