R package imputeTestbench to compare imputations methods for univariate time series

TL;DR

The paper introduces the R package imputeTestbench, which facilitates easy comparison of various imputation methods for missing data in univariate time series through simulation and error metric evaluation.

Contribution

The package allows flexible simulation of missing data and comparison of multiple imputation methods with customizable error metrics, streamlining the evaluation process.

Findings

Supports simulation of missing data at random or in blocks

Includes default imputation methods like mean, interpolation, last observation

Enables easy addition of custom imputation methods

Abstract

This paper describes the R package imputeTestbench that provides a testbench for comparing imputation methods for missing data in univariate time series. The imputeTestbench 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. The user has the option to simulate missing data by removing observations completely at random or in blocks of different sizes. Several default imputation methods are included with the package, including historical means, linear interpolation, and last observation carried forward. The testbench is not limited to the default functions and users can add or remove additional methods using a simple two-step process. The testbench compares the actual missing and imputed data for each method with different error metrics, including RMSE, MAE, and MAPE. Alternative error metrics can also be supplied by the user. The simplicity of use and significant reduction in time to compare imputation methods for missing data in univariate time series is a significant advantage of the package. This paper provides an overview of the core functions, including a demonstration with examples.