deseats: Data-Driven Locally Weighted Regression for Trend and
Seasonality in TS
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 and in
more detail in Schulz (2026) <doi:10.32614/RJ-2026-025>.
Furthermore, the package allows its users to apply the base model of the
Berlin procedure, version 4.1, as described in Speth (2004) <https://www.destatis.de/DE/Methoden/Saisonbereinigung/BV41-methodenbericht-Heft3_2004.pdf?__blob=publicationFile>.
Permission to include this procedure was kindly provided by the Federal
Statistical Office of Germany.
| Version: |
1.1.3 |
| 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, tidyr |
| LinkingTo: |
Rcpp, RcppArmadillo |
| Suggests: |
badger, knitr, rmarkdown, smoots, testthat (≥ 3.0.0) |
| Published: |
2026-10-11 |
| DOI: |
10.32614/CRAN.package.deseats |
| Author: |
Yuanhua Feng [aut] (Paderborn University, Germany),
Dominik Schulz [aut, cre] (formerly Paderborn University, Germany) |
| Maintainer: |
Dominik Schulz <dominik.schulz.r at gmail.com> |
| License: |
GPL-3 |
| URL: |
https://github.com/dschulz13/deseats |
| NeedsCompilation: |
yes |
| Citation: |
deseats citation info |
| Materials: |
README, NEWS |
| In views: |
TimeSeries |
| CRAN checks: |
deseats results |
Documentation:
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