Missing observations are common in time series data and several methods are available to impute these values prior to analysis. Variation in statistical characteristics of univariate time series can have a profound effect on characteristics of missing observations and, therefore, the accuracy of different imputation methods. The imputeTestbench package can be used to compare the prediction accuracy of different methods as related to the amount and type of missing data for a user-supplied dataset. Missing data are simulated by removing observations completely at random or in blocks of different sizes depending on characteristics of the data. Several imputation algorithms are included with the package that vary from simple replacement with means to more complex interpolation methods. The testbench is not limited to the default functions and users can add or remove methods as needed. Plotting functions also allow comparative visualization of the behavior and effectiveness of different algorithms. We present example applications that demonstrate how the package can be used to understand differences in prediction accuracy between methods as affected by characteristics of a dataset and the nature of missing data.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2018-024.zip
imputeTestbench, dplyr, reshape2, tidyr, ggplot2, forecast, imputeTS, zoo, stats, datasets, Rcpp, matlabr
TimeSeries, Econometrics, Environmetrics, Finance, Graphics, HighPerformanceComputing, ModelDeployment, NumericalMathematics, Phylogenetics
Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".
For attribution, please cite this work as
Beck, et al., "The R Journal: R Package imputeTestbench to Compare Imputation Methods for Univariate Time Series", The R Journal, 2018
BibTeX citation
@article{RJ-2018-024,
author = {Beck, Marcus W and Bokde, Neeraj and Asencio-Cortés, Gualberto and Kulat, Kishore},
title = {The R Journal: R Package imputeTestbench to Compare Imputation Methods for Univariate Time Series},
journal = {The R Journal},
year = {2018},
note = {https://doi.org/10.32614/RJ-2018-024},
doi = {10.32614/RJ-2018-024},
volume = {10},
issue = {1},
issn = {2073-4859},
pages = {218-233}
}