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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. For details, see Narajewski et al. (2021) <doi:10.1016/j.softx.2021.100809>.
Version: | 0.3.2 |
Depends: | R (≥ 3.2.0) |
Imports: | glmnet, MASS, Matrix, mclust, quantreg, Rdpack, splines, textTinyR, zoo |
Published: | 2022-02-22 |
DOI: | 10.32614/CRAN.package.tsrobprep |
Author: | Michał Narajewski |
Maintainer: | Michał Narajewski <michal.narajewski at uni-due.de> |
License: | MIT + file LICENSE |
NeedsCompilation: | no |
Citation: | tsrobprep citation info |
In views: | MissingData, TimeSeries |
CRAN checks: | tsrobprep results |
Reference manual: | tsrobprep.pdf |
Package source: | tsrobprep_0.3.2.tar.gz |
Windows binaries: | r-devel: tsrobprep_0.3.2.zip, r-release: tsrobprep_0.3.2.zip, r-oldrel: tsrobprep_0.3.2.zip |
macOS binaries: | r-release (arm64): tsrobprep_0.3.2.tgz, r-oldrel (arm64): tsrobprep_0.3.2.tgz, r-release (x86_64): tsrobprep_0.3.2.tgz, r-oldrel (x86_64): tsrobprep_0.3.2.tgz |
Old sources: | tsrobprep archive |
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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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