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. 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.

Version: 1.0.0
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
LinkingTo: Rcpp, RcppArmadillo
Suggests: badger, knitr, rmarkdown, smoots, testthat (≥ 3.0.0)
Published: 2023-11-08
Author: Yuanhua Feng [aut] (Paderborn University, Germany), Dominik Schulz [aut, cre] (Paderborn University, Germany)
Maintainer: Dominik Schulz <dominik.schulz at>
License: GPL-3
NeedsCompilation: yes
Materials: README
In views: TimeSeries
CRAN checks: deseats results


Reference manual: deseats.pdf


Package source: deseats_1.0.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): deseats_1.0.0.tgz, r-oldrel (arm64): deseats_1.0.0.tgz, r-release (x86_64): deseats_1.0.0.tgz, r-oldrel (x86_64): deseats_1.0.0.tgz


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