31: A Structure-Preserving Assessment of VBPBB for Time Series Imputation Under Periodic Trends

Thomas M. Menino Convention & Exhibition Center 

Incomplete time series data pose challenges for accurate analysis, especially when periodic structures like seasonal trends are present. Traditional imputation methods often fail to preserve these temporal dynamics, leading to biased estimates. This study introduces a structure-preserving imputation framework that integrates periodic components into the multiple imputation process using the Variable Bandpass Periodic Block Bootstrap (VBPBB). We simulate time series data with annual and monthly periodicities, varying noise levels (low, moderate, high), and missingness under Missing Completely at Random (MCAR) across missingness proportions (5%-70%). VBPBB extracts dominant periodic components, which are bootstrapped and incorporated as covariates in the Amelia II imputation model. Results show that VBPBB-enhanced imputation consistently outperforms standard methods, with the most significant gains observed in high-noise settings and when multiple periodic components are retained. This framework provides a flexible solution that preserves temporal structure, offering potential for improving imputation in temporally correlated data environments.

Missing data; Time Series; Periodicity; periodic component; Variable Bandpass Periodic Block Bootstrap;Multiple Imputation; Amelia II; Bootstrap

MCAR: Missing Completely at Random; MAE: Mean Absolute Error; RMSE: Root Mean Square Error; VBPBB: Variable Bandpass Periodic Block Bootstrap; PC: Periodic Component;

kzft:Kolmogorov-Zurbenko Fourier Transform 

Section on Statistical Learning and Data Science