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deseats

Data-Driven Locally Weighted Regression for Trend and Seasonality in TS

Installation

About

Various methods for the identification of trend and seasonal components in time series (TS) are provided. Among them is a data-driven locally weighted regression approach with automatically selected bandwidth for equidistant short-memory time series. The approach is a combination / extension of the algorithms by Feng (2013) doi:10.1080/02664763.2012.740626 and Feng, Y., Gries, T., and Fritz, M. (2020) doi:10.1080/10485252.2020.1759598 and a brief description of this new method is provided in the package documentation. Furthermore, the package allows its users to apply the base model of the Berlin procedure, version 4.1, as described in Speth (2004) . Permission to include this procedure was kindly provided by the Federal Statistical Office of Germany.

Key Metrics

Version 1.0.0
R ≥ 2.10
Published 2023-11-08 177 days ago
Needs compilation? yes
License GPL-3
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Maintainer

Maintainer

Dominik Schulz

dominik.schulz@uni-paderborn.de

Authors

Yuanhua Feng

aut

(Paderborn University, Germany)

Dominik Schulz

aut / cre

(Paderborn University, Germany)

Material

README
Reference manual
Package source

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-develnot available

x86_64

r-releasenot available

x86_64

r-oldrelnot available

x86_64

Depends

R ≥ 2.10
methods

Imports

Rcpp ≥ 1.0.6
ggplot2
stats
graphics
animation
utils
shiny
tools
zoo
future
furrr
future.apply
progressr
purrr
rlang

Suggests

badger
knitr
rmarkdown
smoots
testthat ≥ 3.0.0

LinkingTo

Rcpp
RcppArmadillo