Doubly Stochastic Generative Arrivals Modeling

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 then show 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.