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arXiv · 2506.09258

CFMI: Flow Matching for Missing Data Imputation

Vaidotas Simkus·Michael U. Gutmann
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Abstract

We introduce conditional flow matching for imputation (CFMI), a new general-purpose method to impute missing data. The method combines continuous normalising flows, flow-matching, and shared conditional modelling to deal with intractabilities of traditional multiple imputation. Our comparison with nine classical and state-of-the-art imputation methods on 24 small to moderate-dimensional tabular data sets shows that CFMI matches or outperforms both traditional and modern techniques across a wide range of metrics. Applying the method to zero-shot imputation of time-series data, we find that it matches the accuracy of a related diffusion-based method while outperforming it in terms of computational efficiency. Overall, CFMI performs at least as well as traditional methods on lower-dimensional data while remaining scalable to high-dimensional settings, matching or exceeding the performance of other deep learning-based approaches, making it a go-to imputation method for a wide range of data types and dimensionalities.

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Vaidotas Simkus, Michael U. Gutmann. 2025-06-10. CFMI: Flow Matching for Missing Data Imputation. https://arxiv.org/abs/2506.09258

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