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 …
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).
Engineering Blog Articles
M4 Forecasting Competition: Introducing a New Hybrid ES-RNN Model
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 …
Engineering Extreme Event Forecasting at Uber with Recurrent Neural Networks
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 …
Research Papers
Time-series extreme event forecasting with neural networks at Uber
N. Laptev, J. Yosinski, L. Li, S. Smyl
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
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
