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
Forecasting at Uber: An Introduction
In this article, we provide a general overview of how our teams leverage forecasting to build better products and maintain the health of the Uber marketplace.
M4 Forecasting Competition: Introducing a New Hybrid ES-RNN Model
With a solid margin, Uber senior data scientist Slawek Smyl won the M4 Competition with his hybrid Exponential Smoothing-Recurrent Neural Networks (ES-RNN) forecasting method.
Engineering Extreme Event Forecasting at Uber with Recurrent Neural Networks
Recurrent neural networks equip Uber Engineering's new forecasting model to more accurately predict rider demand during extreme events.
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
