Combining time-series and textual data for taxi demand prediction in event areas: a deep learning approach

@article{Rodrigues2018CombiningTA,
  title={Combining time-series and textual data for taxi demand prediction in event areas: a deep learning approach},
  author={Filipe Rodrigues and Ioulia Markou and Francisco C. Pereira},
  journal={CoRR},
  year={2018},
  volume={abs/1808.05535}
}
learning approach DTU Orbit (17/11/2018) Combining time-series and textual data for taxi demand prediction in event areas: a deep learning approach 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… CONTINUE READING

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