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Highlights • Two data fusion approaches for combining time-series with text data are proposed. • Text data is modeled using word embeddings, convolutional layers and attention. • Proposed models are applied to taxi demand prediction in event areas in New York. • Text information about events is shown to significantly reduce prediction error. • Proposed models are shown to substantially outperform traditional approaches. Abstract 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. [ABSTRACT FROM AUTHOR]