Automation package to impute missing values in a time series
Introduction to ‘imputeTestbench’, an R package
Background
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 the 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 the characteristics of the data. Several imputation algorithms are included with the package that varies 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.
This post present example of applications that demonstrate how the package can be used to understand differences in prediction accuracy between methods as affected by…