tsrobprep: Robust Preprocessing of Time Series Data

Methods for handling the missing values outliers are introduced in this package. The recognized missing values and outliers are replaced using a model-based approach. The model may consist of both autoregressive components and external regressors. The methods work robust and efficient, and they are fully tunable. The primary motivation for writing the package was preprocessing of the energy systems data, e.g. power plant production time series, but the package could be used with any time series data.

Version: 0.1.0
Depends: R (≥ 3.2.0)
Imports: Matrix, quantreg, mclust, MASS
Published: 2021-04-11
Author: Michał Narajewski ORCID iD [aut, cre], Florian Ziel ORCID iD [aut], Jens Kley-Holsteg [ctb]
Maintainer: Michał Narajewski <michal.narajewski at uni-due.de>
License: MIT + file LICENSE
NeedsCompilation: no
CRAN checks: tsrobprep results


Reference manual: tsrobprep.pdf
Package source: tsrobprep_0.1.0.tar.gz
Windows binaries: r-devel: tsrobprep_0.1.0.zip, r-release: tsrobprep_0.1.0.zip, r-oldrel: tsrobprep_0.0.0.2.zip
macOS binaries: r-release: tsrobprep_0.0.0.2.tgz, r-oldrel: tsrobprep_0.1.0.tgz
Old sources: tsrobprep archive


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