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AI, Data / ML

Forecasting at Uber: An Introduction

September 6, 2018 / Global
Featured image for Forecasting at Uber: An Introduction
Figure 1. Marketplace forecasting in California’s Bay Area allows us to direct drivers to high-demand areas.
Figure 2. Leveraging the daily sum of Uber trips in a city, we can better predict the amount and frequency of future trips.
Figure 3. The hourly sum of Uber trips in a given month (in July 2017) help us model user patterns.
Classical & StatisticalMachine Learning
Figure 4: Two major approaches to test forecasting models are the sliding window approach (left) and the expanding window approach (right).
Figure 5: Prediction intervals are critical to informed decision making. Although point forecasts may be the same, their prediction intervals may be significantly different.
Franziska Bell

Franziska Bell

Fran Bell is a Data Science Director at Uber, leading platform data science teams including Applied Machine Learning, Forecasting, and Natural Language Understanding.

Slawek Smyl

Slawek Smyl

Slawek Smyl is a forecasting expert working at Uber. Slawek has ranked highly in international forecasting competitions. For example, he won the M4 Forecasting competition (2018) and the Computational Intelligence in Forecasting International Time Series Competition 2016 using recurrent neural networks. Slawek also built a number of statistical time series algorithms that surpass all published results on M3 time series competition data set using Markov Chain Monte Carlo (R, Stan).

Posted by Franziska Bell, Slawek Smyl