讲座：Doubly Stochastic Generative Arrivals Modeling
题 目：Doubly Stochastic Generative Arrivals Modeling
演讲人：Zeyu Zheng 助理教授 加州大学伯克利分校
主持人：何滔滔 助理教授 上海交通大学安泰经济与管理学院
We propose a new framework named DS-WGAN that integrates the doubly stochastic (DS) structure and the Wasserstein generative adversarial networks (WGAN) to model, estimate, and simulate a wide class of arrival processes with general non-stationary and random arrival rates. Regarding statistical properties, we prove consistency and convergence rate for the estimator solved by the DS-WGAN framework under a non-parametric smoothness condition. Regarding computational efficiency and tractability, we address a challenge in gradient evaluation and model estimation, arised from the discontinuity in the simulator. We argue that the DS-WGAN framework can conveniently facilitate what-if simulation and predictive simulation for future scenarios that are different from the history. Numerical experiments with synthetic and real data sets are implemented to demonstrate the performance of DS-WGAN. The performance is measured from both a statistical perspective and an operational performance evaluation perspective. Numerical experiments suggest that, in terms of performance, the successful model estimation for DS-WGAN only requires a moderate size of representative data, which can be appealing in many contexts of operational management.
Zeyu Zheng is an Assistant Professor at the University of California Berkeley, Department of Industrial Engineering and Operations Research. He received his Ph.D. in Operations Research, Ph.D. minor in Statistics, and M.A. in Economics from Stanford University, and B.S. in Mathematics from Peking University. He has done research in simulation, non-stationary stochastic modeling and decision making, and financial technologies.