deseats
1.1.2Data-Driven Locally Weighted Regression for Trend and Seasonality in TS
Overview
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.
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Health
- OK2026-08-0513 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- NOTE2026-08-0112 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- OK2026-05-0213 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- NOTE2026-04-2212 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-04-1811 OK · 2 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
Show 1 earlier snapshots
- NOTE2026-03-1012 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 56%
- Documented parameters
- 96%
- Return-value docs
- 100%
- References docs
- 8%
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Package metadata
- First published
- 2023-11-08
- Total releases
- 4 / 3 yrs
- License
- GPL-3 OSI
- Minimum R
- ≥ 2.10
- Bundled data
- 28 KB / 15 files
- Download size
- 311 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet
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