CFMI: Flow Matching for Missing Data Imputation

TL;DR

CFMI introduces a versatile flow-matching approach for missing data imputation, outperforming traditional and modern methods across various datasets and efficiently handling high-dimensional data.

Contribution

The paper presents CFMI, a novel conditional flow matching technique that improves imputation accuracy and scalability for diverse data types and dimensions.

Findings

CFMI matches or outperforms classical and state-of-the-art imputation methods.

It achieves comparable accuracy to diffusion-based methods in zero-shot time-series imputation.

CFMI is scalable and efficient for high-dimensional data imputation.

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.