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tsrobprep
View on CRAN: Click
here
Download and install tsrobprep package within the R console
Install from CRAN:
install.packages("tsrobprep")
Install from Github:
library("remotes")
install_github("cran/tsrobprep") Install by package version:
library("remotes")
install_version("tsrobprep", "0.3.2") Attach the package and use:
library("tsrobprep")
Maintained by
Michał Narajewski
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2020-09-11
Latest Update: 2022-02-22
Description:
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>.
How to cite:
Michał Narajewski (2020). tsrobprep: Robust Preprocessing of Time Series Data. R package version 0.3.2, https://cran.r-project.org/web/packages/tsrobprep. Accessed 04 Jun. 2026.
Previous versions and publish date:
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Complete documentation for tsrobprep
Functions, R codes and Examples using
the tsrobprep R package
Some associated functions: GBload . auto_data_cleaning . detect_outliers . impute_modelled_data . model_missing_data . robust_decompose .
Some associated R codes: auto_data_cleaning.R . data.R . detect_outliers.R . impute_modelled_data.R . model_missing_data.R . robust_decompose.R . Full tsrobprep package functions and examples
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