Package: WaveletETS 0.1.0

WaveletETS: Wavelet Based Error Trend Seasonality Model

ETS stands for Error, Trend, and Seasonality, and it is a popular time series forecasting method. Wavelet decomposition can be used for denoising, compression, and feature extraction of signals. By removing the high-frequency components, wavelet decomposition can remove noise from the data while preserving important features. A hybrid Wavelet ETS (Error Trend-Seasonality) model has been developed for time series forecasting using algorithm of Anjoy and Paul (2017) <doi:10.1007/s00521-017-3289-9>.

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

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WaveletETS.pdf |WaveletETS.html✨
WaveletETS/json (API)

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

Peer review:

On CRAN:

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

1.00 score 121 downloads 1 exports 91 dependencies

Last updated 2 years agofrom:fadc66fdd6. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 31 2024
R-4.5-winOKOct 31 2024
R-4.5-linuxOKOct 31 2024
R-4.4-winOKOct 31 2024
R-4.4-macOKOct 31 2024
R-4.3-winOKOct 31 2024
R-4.3-macOKOct 31 2024

Exports:WaveletETS

Dependencies:caretcaretForecastclasscliclockcodetoolscolorspacecpp11curldata.tablediagramdigestdplyre1071fansifarverforeachforecastfracdifffuturefuture.applygenericsggplot2globalsgluegowergtablehardhatipredisobanditeratorsjsonliteKernSmoothlabelinglatticelavalifecyclelistenvlmtestlubridatemagrittrMASSMatrixMetricsmgcvModelMetricsmunsellnlmennetnumDerivparallellypillarpkgconfigplyrpROCprodlimprogressrproxypurrrquadprogquantmodR6RColorBrewerRcppRcppArmadillorecipesreshape2rlangrpartscalesshapeSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetseriesTTRtzdburcautf8vctrsviridisLitewaveletswithrxtszoo