This paper describes the R package imputeTestbench that provides a testbenchfor comparing imputation methods for missing data in univariate time series.The imputeTestbench package can be used to simulate the amount and type ofmissing data in a complete dataset and compare filled data using differentimputation methods. The user has the option to simulate missing data byremoving observations completely at random or in blocks of different sizes.Several default imputation methods are included with the package, includinghistorical means, linear interpolation, and last observation carried forward.The testbench is not limited to the default functions and users can add orremove additional methods using a simple two-step process. The testbenchcompares the actual missing and imputed data for each method with differenterror metrics, including RMSE, MAE, and MAPE. Alternative error metrics canalso be supplied by the user. The simplicity of use and significant reductionin time to compare imputation methods for missing data in univariate timeseries is a significant advantage of the package. This paper provides anoverview of the core functions, including a demonstration with examples.
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