Enhancing Data Completeness in Time Series: Imputation Strategies for Missing Data Using Significant Periodically Correlated Components

TL;DR

This paper introduces a novel imputation method using the Variable Bandpass Periodic Block Bootstrap (VBPBB) technique to improve data completeness in time series by incorporating significant periodic components, thus preserving statistical properties and enhancing analysis reliability.

Contribution

The study presents a new imputation approach leveraging VBPBB to incorporate periodic components, outperforming traditional methods in maintaining data structure and accuracy.

Findings

VBPBB-enhanced imputation outperforms traditional methods.

Method preserves mean and variance of datasets.

Approach applicable to real-world datasets, especially in healthcare.

Abstract

Missing data is a pervasive issue in statistical analyses, affecting the reliability and validity of research across diverse scientific disciplines. Failure to adequately address missing data can lead to biased estimates and consequently flawed conclusions. In this study, we present a novel imputation method that leverages significant annual components identified through the Variable Bandpass Periodic Block Bootstrap (VBPBB) technique to improve the accuracy and integrity of imputed datasets. Our approach enhances the completeness of datasets by systematically incorporating periodic components into the imputation process, thereby preserving key statistical properties, including mean and variance. We conduct a comparative analysis of various imputation techniques, demonstrating that our VBPBB-enhanced approach consistently outperforms traditional methods in maintaining the statistical structure of the original dataset. The results of our study underscore the robustness and reliability of VBPBB-enhanced imputation, highlighting its potential for broader application in real-world datasets, particularly in fields such as healthcare, where data quality is critical. These findings provide a robust framework for improving the accuracy of imputed datasets, offering substantial implications for advancing research methodologies across scientific and analytical contexts. Our method not only impute missing data but also ensures that the imputed values align with underlying temporal patterns, thereby facilitating more accurate and reliable conclusions.