  We present DeepMVI, a deep learning method for missing value imputation in
multidimensional time-series datasets. Missing values are commonplace in
decision support platforms that aggregate data over long time stretches from
disparate sources, and reliable data analytics calls for careful handling of
missing data. One strategy is imputing the missing values, and a wide variety
of algorithms exist spanning simple interpolation, matrix factorization methods
like SVD, statistical models like Kalman filters, and recent deep learning
methods. We show that often these provide worse results on aggregate analytics
compared to just excluding the missing data. DeepMVI uses a neural network to
combine fine-grained and coarse-grained patterns along a time series, and
trends from related series across categorical dimensions. After failing with
off-the-shelf neural architectures, we design our own network that includes a
temporal transformer with a novel convolutional window feature, and kernel
regression with learned embeddings. The parameters and their training are
designed carefully to generalize across different placements of missing blocks
and data characteristics. Experiments across nine real datasets, four different
missing scenarios, comparing seven existing methods show that DeepMVI is
significantly more accurate, reducing error by more than 50% in more than half
the cases, compared to the best existing method. Although slower than simpler
matrix factorization methods, we justify the increased time overheads by
showing that DeepMVI is the only option that provided overall more accurate
analytics than dropping missing values.
