Selected work

Representative Papers

A Structure-Preserving Assessment of VBPBB for Time Series Imputation Under Periodic Trends, Noise, and Missingness Mechanisms

Aug 26, 2025

Traditional imputation methods struggle to preserve the dynamic structure of time series exhibiting periodic trends (e.g., seasonality), substantial noise, and complex missingness mechanisms (e.g., Missing Completely at Random, MCAR), leading to statistical bias. To address this, we propose a structure-preserving multiple imputation framework that innovatively integrates Variable Bandpass Periodic Block Bootstrap (VBPBB) for multi-band periodic component extraction into the Amelia II model, enabling periodicity-guided imputation. Our method significantly outperforms state-of-the-art imputation techniques under challenging conditions—including high noise levels, multiple superimposed periodicities, and high missing rates—particularly in preserving periodic structure fidelity and improving estimation accuracy. Extensive experiments demonstrate its robust superiority across diverse missingness proportions and noise intensities. This work establishes a novel paradigm for reliable statistical inference on periodic time series.

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

May 04, 2025

This paper addresses statistical bias induced by missing data in time series. We propose a novel imputation method that explicitly incorporates salient periodic structures. Our core contribution is the Variable-Bandpass Periodic Block Bootstrap (VBPBB) framework, which—uniquely—enables adaptive identification of dominant periodic components and leverages them to guide imputation. Crucially, VBPBB preserves key statistical properties—including mean and variance—while accurately capturing both structural regularity and dynamic temporal patterns. Extensive experiments across diverse scenarios demonstrate that our method significantly outperforms conventional imputation approaches in distributional fidelity and pattern consistency. Furthermore, empirical validation in high-stakes, data-quality-sensitive domains—such as clinical time-series analysis—confirms its robustness and practical utility. By unifying periodic structure learning with statistically grounded resampling, VBPBB establishes a new paradigm for integrity-preserving modeling of periodic time series.

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Recent publications

Latest Papers

A Structure-Preserving Assessment of VBPBB for Time Series Imputation Under Periodic Trends, Noise, and Missingness Mechanisms

Aug 26, 2025

Traditional imputation methods struggle to preserve the dynamic structure of time series exhibiting periodic trends (e.g., seasonality), substantial noise, and complex missingness mechanisms (e.g., Missing Completely at Random, MCAR), leading to statistical bias. To address this, we propose a structure-preserving multiple imputation framework that innovatively integrates Variable Bandpass Periodic Block Bootstrap (VBPBB) for multi-band periodic component extraction into the Amelia II model, enabling periodicity-guided imputation. Our method significantly outperforms state-of-the-art imputation techniques under challenging conditions—including high noise levels, multiple superimposed periodicities, and high missing rates—particularly in preserving periodic structure fidelity and improving estimation accuracy. Extensive experiments demonstrate its robust superiority across diverse missingness proportions and noise intensities. This work establishes a novel paradigm for reliable statistical inference on periodic time series.

0 citationsRead paper

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

May 04, 2025

This paper addresses statistical bias induced by missing data in time series. We propose a novel imputation method that explicitly incorporates salient periodic structures. Our core contribution is the Variable-Bandpass Periodic Block Bootstrap (VBPBB) framework, which—uniquely—enables adaptive identification of dominant periodic components and leverages them to guide imputation. Crucially, VBPBB preserves key statistical properties—including mean and variance—while accurately capturing both structural regularity and dynamic temporal patterns. Extensive experiments across diverse scenarios demonstrate that our method significantly outperforms conventional imputation approaches in distributional fidelity and pattern consistency. Furthermore, empirical validation in high-stakes, data-quality-sensitive domains—such as clinical time-series analysis—confirms its robustness and practical utility. By unifying periodic structure learning with statistically grounded resampling, VBPBB establishes a new paradigm for integrity-preserving modeling of periodic time series.

0 citationsRead paper