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. 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 |
| Author: |
Michał Narajewski 
[aut, cre],
Jens Kley-Holsteg [aut],
Florian Ziel
[aut] |
| 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 |
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