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) <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 |
Documentation:
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