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Multivariate time series modeling and prediction problems are abundant inmany machine learning application domains. Accurate interpretation of suchprediction outcomes from a machine learning model that explicitly capturestemporal correlations can significantly benefit the domain experts. In thiscontext, temporal attention has been successfully applied to isolate theimportant time steps for the input time series. However, in multivariate timeseries problems, spatial interpretation is also critical to understand thecontributions of different variables on the model outputs. We propose a noveldeep learning architecture, called spatiotemporal attention mechanism (STAM)for simultaneous learning of the most important time steps and variables. STAMis 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 ofcomputational tractability. We demonstrate our models' performance on twopopular public datasets and a domain-specific dataset. When compared with thebaseline models, the results show that STAM maintains state-of-the-artprediction accuracy while offering the benefit of accurate spatiotemporalinterpretability. The learned attention weights are validated from a domainknowledge perspective for these real-world datasets.

Spatiotemporal Attention for Multivariate Time Series Prediction and Interpretation