Bidirectional f-Divergence-Based Deep Generative Method for Imputing Missing Values in Time-Series Data

Wen-Shan Liu, Tong Si (), Aldas Kriauciunas, Marcus Snell and Haijun Gong ()
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Wen-Shan Liu: Department of Health and Clinical Outcomes Research, Saint Louis University, St. Louis, MO 63103, USA
Tong Si: Department of Mathematics and Computer Science, Culver-Stockton College, Canton, MO 63435, USA
Aldas Kriauciunas: Department of Mathematics and Statistics, Saint Louis University, St. Louis, MO 63103, USA
Marcus Snell: Department of Mathematics and Statistics, Saint Louis University, St. Louis, MO 63103, USA
Haijun Gong: Department of Mathematics and Statistics, Saint Louis University, St. Louis, MO 63103, USA

Stats, 2025, vol. 8, issue 1, 1-18

Abstract: Imputing missing values in high-dimensional time-series data remains a significant challenge in statistics and machine learning. Although various methods have been proposed in recent years, many struggle with limitations and reduced accuracy, particularly when the missing rate is high. In this work, we present a novel f-divergence-based bidirectional generative adversarial imputation network, tf-BiGAIN, designed to address these challenges in time-series data imputation. Unlike traditional imputation methods, tf-BiGAIN employs a generative model to synthesize missing values without relying on distributional assumptions. The imputation process is achieved by training two neural networks, implemented using bidirectional modified gated recurrent units, with f-divergence serving as the objective function to guide optimization. Compared to existing deep learning-based methods, tf-BiGAIN introduces two key innovations. First, the use of f-divergence provides a flexible and adaptable framework for optimizing the model across diverse imputation tasks, enhancing its versatility. Second, the use of bidirectional gated recurrent units allows the model to leverage both forward and backward temporal information. This bidirectional approach enables the model to effectively capture dependencies from both past and future observations, enhancing its imputation accuracy and robustness. We applied tf-BiGAIN to analyze two real-world time-series datasets, demonstrating its superior performance in imputing missing values and outperforming existing methods in terms of accuracy and robustness.

Keywords: missing value imputation; time series; generative adversarial network; f-divergence; bidirectional gated recurrent unit (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2025
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