outliers.ts.oga: Efficient Outlier Detection in Heterogeneous Time Series Databases

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 ORCID iD [aut, cre], Daniel Peña ORCID iD [aut], Ruey S. Tsay ORCID iD [aut]
Maintainer: Pedro Galeano <pedro.galeano at uc3m.es>
License: GPL-3
NeedsCompilation: no
CRAN checks: outliers.ts.oga results

Documentation:

Reference manual: outliers.ts.oga.pdf

Downloads:

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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