Xin Guo

Abstract

Freight transportation marketplace rates are typically challenging to forecast accurately. In this talk, I will present a novel statistical technique based on signature transforms and  a predictive and adaptive model to forecast these marketplace rates. Our technique is based on two key elements of the signature transform: one being its universal nonlinearity property, which linearizes the feature space and hence translates the forecasting problem into linear regression, and the other being the signature kernel, which allows for comparing computationally efficiently similarities between time series data. Combined, it allows for efficient feature generation and precise identification of seasonality and regime switching in the forecasting process. 

An algorithm based on our technique has been deployed by Amazon trucking operations, with far superior forecast accuracy and better interpretability versus commercially available industry models, even during the COVID-19 pandemic and the Ukraine conflict. Furthermore, our technique is in production in Amazon and has been adopted for Amazon finance planning,  with an estimated annualized saving of $50MM in the transportation sector alone. 

Our speaker

Dr Xin Guo is a Coleman Fung Chair professor at UC Berkeley. Her research interests span broadly from stochastic processes, control, and games, to theory of machine learning including multi-agent reinforcement learning, generative models, and transfer learning, with applications from medical data,  financial time series data,  to transportation data analysis. Her research lays the mathematical foundation for the FDA-approved early cancer detection tools, and leads to hundred of millions of dollars savings for Amazon business.