Package: WaveletKNN 0.1.0

WaveletKNN: Wavelet Based K-Nearest Neighbor Model

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>.

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

WaveletKNN_0.1.0.tar.gz
WaveletKNN_0.1.0.zip(r-4.5)WaveletKNN_0.1.0.zip(r-4.4)WaveletKNN_0.1.0.zip(r-4.3)
WaveletKNN_0.1.0.tgz(r-4.5-any)WaveletKNN_0.1.0.tgz(r-4.4-any)WaveletKNN_0.1.0.tgz(r-4.3-any)
WaveletKNN_0.1.0.tar.gz(r-4.5-noble)WaveletKNN_0.1.0.tar.gz(r-4.4-noble)
WaveletKNN_0.1.0.tgz(r-4.4-emscripten)WaveletKNN_0.1.0.tgz(r-4.3-emscripten)
WaveletKNN.pdf |WaveletKNN.html
WaveletKNN/json (API)

# Install 'WaveletKNN' in R:
install.packages('WaveletKNN', repos = c('https://yeasinstat.r-universe.dev', 'https://cloud.r-project.org'))

On CRAN:

Conda:

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

1.00 score 134 downloads 1 exports 92 dependencies

Last updated 2 years agofrom:713da2de2a. Checks:9 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 26 2025
R-4.5-winOKMar 26 2025
R-4.5-macOKMar 26 2025
R-4.5-linuxOKMar 26 2025
R-4.4-winOKMar 26 2025
R-4.4-macOKMar 26 2025
R-4.4-linuxOKMar 26 2025
R-4.3-winOKMar 26 2025
R-4.3-macOKMar 26 2025

Exports:WaveletKNN

Dependencies:caretcaretForecastclasscliclockcodetoolscolorspacecpp11curldata.tablediagramdigestdplyre1071fansifarverforeachforecastfracdifffuturefuture.applygenericsggplot2globalsgluegowergtablehardhatipredisobanditeratorsjsonliteKernSmoothlabelinglatticelavalifecyclelistenvlmtestlubridatemagrittrMASSMatrixMetricsmgcvModelMetricsmunsellnlmennetnumDerivparallellypillarpkgconfigplyrpROCprodlimprogressrproxypurrrquadprogquantmodR6RColorBrewerRcppRcppArmadillorecipesreshape2rlangrpartscalesshapesparsevctrsSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetseriesTTRtzdburcautf8vctrsviridisLitewaveletswithrxtszoo