  Multivariate time series modeling and prediction problems are abundant in
many machine learning application domains. Accurate interpretation of such
prediction outcomes from a machine learning model that explicitly captures
temporal correlations can significantly benefit the domain experts. In this
context, temporal attention has been successfully applied to isolate the
important time steps for the input time series. However, in multivariate time
series problems, spatial interpretation is also critical to understand the
contributions of different variables on the model outputs. We propose a novel
deep learning architecture, called spatiotemporal attention mechanism (STAM)
for simultaneous learning of the most important time steps and variables. STAM
is a causal (i.e., only depends on past inputs and does not use future inputs)
and scalable (i.e., scales well with an increase in the number of variables)
approach that is comparable to the state-of-the-art models in terms of
computational tractability. We demonstrate our models' performance on two
popular public datasets and a domain-specific dataset. When compared with the
baseline models, the results show that STAM maintains state-of-the-art
prediction accuracy while offering the benefit of accurate spatiotemporal
interpretability. The learned attention weights are validated from a domain
knowledge perspective for these real-world datasets.
