Package: tsrobprep 0.3.2

Michał Narajewski

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>.

Authors:Michał Narajewski [aut, cre], Jens Kley-Holsteg [aut], Florian Ziel [aut]

tsrobprep_0.3.2.tar.gz
tsrobprep_0.3.2.tar.gz(r-4.5-noble)tsrobprep_0.3.2.tar.gz(r-4.4-noble)
tsrobprep_0.3.2.tgz(r-4.4-emscripten)tsrobprep_0.3.2.tgz(r-4.3-emscripten)
tsrobprep.pdf |tsrobprep.html✨
tsrobprep/json (API)

Peer review:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

5 exports 2 stars 0.09 score 23 dependencies 277 downloads

Last updated 3 years agofrom:7370696311. Checks:OK: 2. Indexed: yes.

Exports:auto_data_cleaningdetect_outliersimpute_modelled_datamodel_missing_datarobust_decompose

Dependencies:BHcodetoolsdata.tableforeachglmnetiteratorslatticeMASSMatrixMatrixModelsmclustquantregR6rbibutilsRcppRcppArmadilloRcppEigenRdpackshapeSparseMsurvivaltextTinyRzoo