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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) <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: | 1.0.0 |
| 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 uni-paderborn.de> |
| 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: deseats_1.0.0.zip, r-release: deseats_1.0.0.zip, r-oldrel: deseats_1.0.0.zip |
| 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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