CFMI: Flow Matching for Missing Data Imputation
This provides a scalable and efficient imputation solution for researchers and practitioners dealing with missing data across various data types and dimensionalities, though it is incremental as it builds on existing flow-matching and deep learning techniques.
The authors tackled the problem of missing data imputation by introducing CFMI, a new general-purpose method that combines continuous normalizing flows and flow-matching, which matches or outperforms nine existing methods on 24 tabular datasets and achieves competitive accuracy with improved computational efficiency in time-series data.
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.