Engineering Extreme Event Forecasting at Uber with Recurrent Neural Networks

Date: June 19, 2017 Published by



  • Our goal was to design a generic, end-to-end time series forecasting model that is scalable, accurate, and applicable to heterogeneous time series.
  • Calculating demand time series forecasting during extreme events is a critical component of anomaly detection, optimal resource allocation, and budgeting.
  • Although extreme event forecasting is a crucial piece of Uber operations, data sparsity makes accurate prediction challenging.
  • Automatic time series forecasting: the forecast package for R. Journal of Statistical Software, 26(3):1–22, 2008.

    4 Assaad, Mohammad, Bone, Romuald, and Cardot, Hubert.

  • Using these two windows, we trained a neural network by minimizing a loss function, such as Mean Squared Error.


Engineering Extreme Event Forecasting at Uber with Recurrent Neural Networks

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