Time-series extreme event forecasting with neural networks at Uber

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    Abstract

    Accurate time-series forecasting during high variance segments (e.g., holidays), is critical for anomaly detection, optimal resource allocation, budget planning and other related tasks. At Uber accurate prediction for completed trips during special events can lead to a more efficient driver allocation resulting in a decreased wait time for the riders.

    Authors

    Nikolay Laptev, Jason Yosinski, Li Erran Li, Slawek Smyl

    Conference

    ICML 2017

    Full Paper

    ‘Time-series extreme event forecasting with neural networks at Uber’ (PDF)

    Uber AI

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