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Programs for detecting and cleaning outliers in single time series and in time series from homogeneous and heterogeneous databases using an Orthogonal Greedy Algorithm (OGA) for saturated linear regression models. The programs implement the procedures presented in the paper entitled "Efficient outlier detection in heterogeneous time series databases" by Pedro Galeano, Daniel Peña and Ruey S. Tsay (2024), working paper, Universidad Carlos III de Madrid.
Version: | 0.0.1 |
Depends: | R (≥ 4.3.0) |
Imports: | caret (≥ 6.0-94), forecast (≥ 8.22.0), gsarima (≥ 0.1-5), parallel (≥ 3.6.2), parallelly (≥ 1.37.1), robust (≥ 0.7-4), SLBDD (≥ 0.0.4) |
Suggests: | knitr, rmarkdown |
Published: | 2024-05-28 |
DOI: | 10.32614/CRAN.package.outliers.ts.oga |
Author: | Pedro Galeano |
Maintainer: | Pedro Galeano <pedro.galeano at uc3m.es> |
License: | GPL-3 |
NeedsCompilation: | no |
CRAN checks: | outliers.ts.oga results |
Reference manual: | outliers.ts.oga.pdf |
Package source: | outliers.ts.oga_0.0.1.tar.gz |
Windows binaries: | r-devel: outliers.ts.oga_0.0.1.zip, r-release: outliers.ts.oga_0.0.1.zip, r-oldrel: outliers.ts.oga_0.0.1.zip |
macOS binaries: | r-release (arm64): outliers.ts.oga_0.0.1.tgz, r-oldrel (arm64): outliers.ts.oga_0.0.1.tgz, r-release (x86_64): outliers.ts.oga_0.0.1.tgz, r-oldrel (x86_64): outliers.ts.oga_0.0.1.tgz |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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