Package: VBV 0.6.2
VBV: The Generalized Berlin Method for Time Series Decomposition
Time series decomposition for univariate time series using the "Verallgemeinerte Berliner Verfahren" (Generalized Berlin Method) as described in 'Kontinuierliche Messgrößen und Stichprobenstrategien in Raum und Zeit mit Anwendungen in den Natur-, Umwelt-, Wirtschafts- und Finanzwissenschaften', by Hebbel and Steuer, Springer Berlin Heidelberg, 2022 <doi:10.1007/978-3-662-65638-9>, or 'Decomposition of Time Series using the Generalised Berlin Method (VBV)' by Hebbel and Steuer, in Jan Beran, Yuanhua Feng, Hartmut Hebbel (Eds.): Empirical Economic and Financial Research - Theory, Methods and Practice, Festschrift in Honour of Prof. Siegfried Heiler. Series: Advanced Studies in Theoretical and Applied Econometrics. Springer 2014, p. 9-40.
Authors:
VBV_0.6.2.tar.gz
VBV_0.6.2.zip(r-4.5)VBV_0.6.2.zip(r-4.4)VBV_0.6.2.zip(r-4.3)
VBV_0.6.2.tgz(r-4.4-any)VBV_0.6.2.tgz(r-4.3-any)
VBV_0.6.2.tar.gz(r-4.5-noble)VBV_0.6.2.tar.gz(r-4.4-noble)
VBV_0.6.2.tgz(r-4.4-emscripten)VBV_0.6.2.tgz(r-4.3-emscripten)
VBV.pdf |VBV.html✨
VBV/json (API)
# Install 'VBV' in R: |
install.packages('VBV', repos = c('https://dsteuer.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 2 years agofrom:df8d81e9e9. Checks:OK: 3 NOTE: 4. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Oct 31 2024 |
R-4.5-win | NOTE | Oct 31 2024 |
R-4.5-linux | NOTE | Oct 31 2024 |
R-4.4-win | NOTE | Oct 31 2024 |
R-4.4-mac | NOTE | Oct 31 2024 |
R-4.3-win | OK | Oct 31 2024 |
R-4.3-mac | OK | Oct 31 2024 |
Exports:decompositionestimationmoving.decompositionmoving.estimation
Dependencies:
Readme and manuals
Help Manual
Help page | Topics |
---|---|
decomposition - decompose a time series with VBV | decomposition |
estimation - estimate trend and seasonal components statically | estimation |
moving.decomposition - decompose a times series into locally estimated trend and season figures | moving.decomposition |
moving.estimation - estimate locally optimized trend and season figures | moving.estimation |