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-4.7)WaveletKNN_0.1.0.zip(r-4.6)WaveletKNN_0.1.0.zip(r-4.5)
WaveletKNN_0.1.0.tgz(r-4.6-any)WaveletKNN_0.1.0.tgz(r-4.5-any)
WaveletKNN_0.1.0.tar.gz(r-4.7-any)WaveletKNN_0.1.0.tar.gz(r-4.6-any)
WaveletKNN_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
WaveletKNN/json (API)

# Install 'WaveletKNN' in R:

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

On CRAN:

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

1.00 score 178 downloads 1 exports 90 dependencies

Last updated from:713da2de2a. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	176
source / vignettes	OK	177
linux-release-x86_64	OK	178
macos-release-arm64	OK	112
macos-oldrel-arm64	OK	135
windows-devel	OK	139
windows-release	OK	114
windows-oldrel	OK	128
wasm-release	OK	124