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The employment of the Wavelet decomposition technique proves to be highly advantageous in the modelling of noisy time series data. Wavelet decomposition technique using the "haar" algorithm has been incorporated to formulate a hybrid Wavelet KNN (K-Nearest Neighbour) model for time series forecasting, as proposed by Anjoy and Paul (2017) <doi:10.1007/s00521-017-3289-9>.
| Version: | 0.1.0 |
| Imports: | caret, dplyr, caretForecast, Metrics, tseries, stats, wavelets |
| 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: | WaveletKNN results |
| Reference manual: | WaveletKNN.pdf |
| Package source: | WaveletKNN_0.1.0.tar.gz |
| Windows binaries: | r-devel: WaveletKNN_0.1.0.zip, r-release: WaveletKNN_0.1.0.zip, r-oldrel: WaveletKNN_0.1.0.zip |
| macOS binaries: | r-release (arm64): WaveletKNN_0.1.0.tgz, r-oldrel (arm64): WaveletKNN_0.1.0.tgz, r-release (x86_64): WaveletKNN_0.1.0.tgz, r-oldrel (x86_64): WaveletKNN_0.1.0.tgz |
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