Package: WaveletGBM 0.1.0

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>.

Authors:Dr. Ranjit Kumar Paul [aut, cre], Dr. Md Yeasin [aut]

WaveletGBM_0.1.0.tar.gz
WaveletGBM_0.1.0.zip(r-4.5)WaveletGBM_0.1.0.zip(r-4.4)WaveletGBM_0.1.0.zip(r-4.3)
WaveletGBM_0.1.0.tgz(r-4.4-any)WaveletGBM_0.1.0.tgz(r-4.3-any)
WaveletGBM_0.1.0.tar.gz(r-4.5-noble)WaveletGBM_0.1.0.tar.gz(r-4.4-noble)
WaveletGBM_0.1.0.tgz(r-4.4-emscripten)WaveletGBM_0.1.0.tgz(r-4.3-emscripten)
WaveletGBM.pdf |WaveletGBM.html✨
WaveletGBM/json (API)

Peer review:

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1 exports 0.00 score 92 dependencies 231 downloads

Last updated 1 years agofrom:3639d59be4. Checks:OK: 7. Indexed: yes.

Exports:WaveletGBM

Dependencies:caretcaretForecastclasscliclockcodetoolscolorspacecpp11curldata.tablediagramdigestdplyre1071fansifarverforeachforecastfracdifffuturefuture.applygbmgenericsggplot2globalsgluegowergtablehardhatipredisobanditeratorsjsonliteKernSmoothlabelinglatticelavalifecyclelistenvlmtestlubridatemagrittrMASSMatrixMetricsmgcvModelMetricsmunsellnlmennetnumDerivparallellypillarpkgconfigplyrpROCprodlimprogressrproxypurrrquadprogquantmodR6RColorBrewerRcppRcppArmadillorecipesreshape2rlangrpartscalesshapeSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetseriesTTRtzdburcautf8vctrsviridisLitewaveletswithrxtszoo