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:
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)
# Install 'tsrobprep' in R: |
install.packages('tsrobprep', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
- GBload - The electricity actual total load in Great Britain in year 2018
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 3 years agofrom:7370696311. Checks:OK: 2. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Dec 24 2024 |
R-4.5-linux | OK | Dec 24 2024 |
Exports:auto_data_cleaningdetect_outliersimpute_modelled_datamodel_missing_datarobust_decompose
Dependencies:BHcodetoolsdata.tableforeachglmnetiteratorslatticeMASSMatrixMatrixModelsmclustquantregR6rbibutilsRcppRcppArmadilloRcppEigenRdpackshapeSparseMsurvivaltextTinyRzoo
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Perform automatic data cleaning of time series data | auto_data_cleaning |
Detects unreliable outliers in univariate time series data based on model-based clustering | detect_outliers |
The electricity actual total load in Great Britain in year 2018 | GBload |
Impute modelled missing time series data | impute_modelled_data |
Model missing time series data | model_missing_data |
Robust time series seasonal decomposition | robust_decompose |