Enhancing data completeness in time series: Imputation strategies for missing data using significant periodically correlated components.

Asmaa Ahmad, Eric J Rose, Michael S Roy, Edward Valachovic

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Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 authors.

Asmaa AhmadDepartment of Epidemiology and Biostatistics, College of Integrated Health Sciences, University at Albany, State University of New York, Albany, New York, United States of America.ORCID https://orcid.org/0009-0006-3613-0298

Eric J RoseDepartment of Epidemiology and Biostatistics, College of Integrated Health Sciences, University at Albany, State University of New York, Albany, New York, United States of America.

Michael S RoyNYS Department of Health, Albany, New York, United States of America.

Edward ValachovicDepartment of Epidemiology and Biostatistics, College of Integrated Health Sciences, University at Albany, State University of New York, Albany, New York, United States of America.ORCID https://orcid.org/0000-0002-2372-9713

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Missing data in periodic time series can bias inference when temporal dependence is not preserved during imputation. We propose a framework that integrates the Variable Bandpass Periodic Block Bootstrap (VBPBB) with multiple imputation using Amelia II by incorporating statistically significant periodic components as auxiliary covariates. Performance was evaluated using simulated missingness in temperature time series data with seasonal structure under a Missing at Random (MAR) mechanism. Imputation accuracy was assessed using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE), comparing Amelia II models with and without VBPBB-derived periodic covariates. Incorporating periodic components reduced RMSE and MAE by approximately 55%, indicating improved reconstruction of seasonal patterns. These results suggest that preserving periodic dependence can enhance imputation performance in time series with strong seasonal structure.

Identifiers

PMID42406871

DOI10.1371/journal.pone.0350666

PMCPMC13336469

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.