A generic sparse regression imputation method for time series and tabular data

Abstract

Although many missing data imputation methods have been proposed in the relevant literature, they focus on either time series or tabular data, but not on both. Hence, a generic sparse regression method for missing data imputation is proposed. The imputed values of a target feature are generated by solving a sparse least squares problem using a preconditioned iterative method based on generic approximate sparse pseudoinverse. Sparsity is introduced by dummy encoding existing or constructed (through discretization) categorical features. Extensive experiments were conducted on several datasets, and the results demonstrate the effectiveness of the method for both time series and tabular data.