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