  Spatio-temporal forecasting is an open research field whose interest is
growing exponentially. In this work we focus on creating a complex deep neural
framework for spatio-temporal traffic forecasting with comparatively very good
performance and that shows to be adaptable over several spatio-temporal
conditions while remaining easy to understand and interpret. Our proposal is
based on an interpretable attention-based neural network in which several
modules are combined in order to capture key spatio-temporal time series
components. Through extensive experimentation, we show how the results of our
approach are stable and better than those of other state-of-the-art
alternatives.
