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R package imputeTestbench to compare imputation methods for univariate time series
Citation:
Beck, M., N. Bokde, G. Asencio-Córtes, AND K. Kulat. R package imputeTestbench to compare imputation methods for univariate time series. The R Journal. The R Foundation, Vienna, Austria, 10(1):218-233, (2018).
Impact/Purpose:
This manuscript describes the R package imputeTestbench that provides a testbench for comparing imputation methods for missing data in univariate time series. The package can be used to simulate the amount and type of missing data in a complete dataset and compare filled data using different imputation methods. It has value in the research community by providing an open source package to address the common issue of incomplete observations in a dataset.
Description:
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.
