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.
@article{arxiv.2003.13977,
title = {A Spatio-Temporal Spot-Forecasting Framework for Urban Traffic Prediction},
author = {Rodrigo de Medrano and José L. Aznarte},
journal= {arXiv preprint arXiv:2003.13977},
year = {2020}
}