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Missing value imputation is crucial for real-world data science workflows.Imputation is harder in the online setting, as it requires the imputationmethod itself to be able to evolve over time. For practical applications,imputation algorithms should produce imputations that match the true datadistribution, handle data of mixed types, including ordinal, boolean, andcontinuous variables, and scale to large datasets. In this work we develop anew online imputation algorithm for mixed data using the Gaussian copula. Theonline Gaussian copula model meets all the desiderata: its imputations matchthe data distribution even for mixed data, improve over its offline counterparton the accuracy when the streaming data has a changing distribution, and on thespeed (up to an order of magnitude) especially on large scale datasets. Byfitting the copula model to online data, we also provide a new method to detectchange points in the multivariate dependence structure with missing values.Experimental results on synthetic and real world data validate the performanceof the proposed methods.

Online Missing Value Imputation and Change Point Detection with the Gaussian Copula