WaveletGBM
Wavelet Based Gradient Boosting Method
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>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.0 |
rolling linux/jammy R-4.5 | WaveletGBM_0.1.0.tar.gz |
20.9 KiB |
0.1.0 |
rolling linux/noble R-4.5 | WaveletGBM_0.1.0.tar.gz |
20.9 KiB |
0.1.0 |
rolling source/ R- | WaveletGBM_0.1.0.tar.gz |
2.8 KiB |
0.1.0 |
latest linux/jammy R-4.5 | WaveletGBM_0.1.0.tar.gz |
20.9 KiB |
0.1.0 |
latest linux/noble R-4.5 | WaveletGBM_0.1.0.tar.gz |
20.9 KiB |
0.1.0 |
latest source/ R- | WaveletGBM_0.1.0.tar.gz |
2.8 KiB |
0.1.0 |
2026-04-26 source/ R- | WaveletGBM_0.1.0.tar.gz |
2.8 KiB |
0.1.0 |
2026-04-23 source/ R- | WaveletGBM_0.1.0.tar.gz |
2.8 KiB |
0.1.0 |
2026-04-09 windows/windows R-4.5 | WaveletGBM_0.1.0.zip |
23.7 KiB |
0.1.0 |
2025-04-20 source/ R- | WaveletGBM_0.1.0.tar.gz |
2.8 KiB |