This article is the first in a series dedicated to explaining how Uber leverages forecasting to build better products and services. In recent years, machine learning, deep learning, and probabilistic programming have shown great promise in generating accurate forecasts. In …
Engineering Blog Articles
By Slawek Smyl, Jai Ranganathan, Andrea Pasqua
Uber’s business depends on accurate forecasting. For instance, we use forecasting to predict the expected supply of drivers and demands of riders in the 600+ cities we operate in, to identify when our …
At Uber, event forecasting enables us to future-proof our services based on anticipated user demand. The goal is to accurately predict where, when, and how many ride requests Uber will receive at any given time.
Extreme events—peak travel times such …
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. [PDF]
International Conference on Machine Learning (ICML), 2017