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

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.(c) 2023 Elsevier B.V. All rights reserved.

Automated summary

Although existing missing data imputation methods mainly focus on either time series or tabular data, this paper proposes a generic sparse regression method that can handle missing data in both types of data. The method utilizes a preconditioned iterative approach based on generic approximate sparse pseudoinverse to solve a sparse least squares problem, and introduces sparsity by dummy encoding categorical features. Extensive experiments conducted on multiple datasets demonstrate the effectiveness of the proposed method.