  Accurate time-series forecasting is vital for numerous areas of application
such as transportation, energy, finance, economics, etc. However, while modern
techniques are able to explore large sets of temporal data to build forecasting
models, they typically neglect valuable information that is often available
under the form of unstructured text. Although this data is in a radically
different format, it often contains contextual explanations for many of the
patterns that are observed in the temporal data. In this paper, we propose two
deep learning architectures that leverage word embeddings, convolutional layers
and attention mechanisms for combining text information with time-series data.
We apply these approaches for the problem of taxi demand forecasting in event
areas. Using publicly available taxi data from New York, we empirically show
that by fusing these two complementary cross-modal sources of information, the
proposed models are able to significantly reduce the error in the forecasts.
