Explains the behavior of a time series by decomposing it into its trend, seasonality and residuals. It is built to perform very well in the presence of significant level shifts. It is designed to play well with any breakpoint algorithm and any smoothing algorithm. Currently defaults to 'lowess' for smoothing and 'strucchange' for breakpoint identification. The package is useful in areas such as trend analysis, time series decomposition, breakpoint identification and anomaly detection.
| Version: | 0.1.1 |
| Depends: | R (≥ 2.10) |
| Imports: | changepoint, segmented, strucchange |
| Suggests: | knitr, rmarkdown, testthat (≥ 3.0.0) |
| Published: | 2023-02-13 |
| Author: | Allen Sunny [aut, cre] |
| Maintainer: | Allen Sunny <allensunny1242 at gmail.com> |
| License: | MIT + file LICENSE |
| URL: | https://allen-1242.github.io/StructuralDecompose/ |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| In views: | TimeSeries |
| CRAN checks: | StructuralDecompose results |
| Reference manual: | StructuralDecompose.pdf |
| Vignettes: |
Decomposition Example-Walkthrough Introduction |
| Package source: | StructuralDecompose_0.1.1.tar.gz |
| Windows binaries: | r-prerel: StructuralDecompose_0.1.1.zip, r-release: StructuralDecompose_0.1.1.zip, r-oldrel: StructuralDecompose_0.1.1.zip |
| macOS binaries: | r-prerel (arm64): StructuralDecompose_0.1.1.tgz, r-release (arm64): StructuralDecompose_0.1.1.tgz, r-oldrel (arm64): StructuralDecompose_0.1.1.tgz, r-prerel (x86_64): StructuralDecompose_0.1.1.tgz, r-release (x86_64): StructuralDecompose_0.1.1.tgz |
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