outliers.ts.oga: Efficient Outlier Detection for Large 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 for Large Time Series Databases" by Pedro Galeano, Daniel Peña and Ruey S. Tsay (2026), working paper, Universidad Carlos III de Madrid. Version 1.1.2 fixes one bug.
| Version: |
1.1.2 |
| Depends: |
R (≥ 4.3.0) |
| Imports: |
caret (≥ 6.0-94), forecast (≥ 8.22.0), future (≥ 1.67.0), future.apply (≥ 1.20.0), gsarima (≥ 0.1-5), parallelly (≥
1.37.1), robust (≥ 0.7-4) |
| Suggests: |
knitr, rmarkdown |
| Published: |
2026-01-30 |
| DOI: |
10.32614/CRAN.package.outliers.ts.oga |
| Author: |
Pedro Galeano 
[aut, cre],
Daniel Peña [aut],
Ruey S. Tsay
[aut] |
| Maintainer: |
Pedro Galeano <pedro.galeano at uc3m.es> |
| License: |
GPL-3 |
| NeedsCompilation: |
no |
| In views: |
AnomalyDetection |
| CRAN checks: |
outliers.ts.oga results |
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