Recurrent Adversarial Service Times
Abstract
Service system dynamics occur at the interplay between customer behaviour and a service provider's response. This kind of dynamics can effectively be modeled within the framework of queuing theory where customers' arrivals are described by point process models. However, these approaches are limited by parametric assumptions as to, for example, inter-event time distributions. In this paper, we address these limitations and propose a novel, deep neural network solution to the queuing problem. Our solution combines a recurrent neural network that models the arrival process with a recurrent generative adversarial network which models the service time distribution. We evaluate our methodology on various empirical datasets ranging from internet services (Blockchain, GitHub, Stackoverflow) to mobility service systems (New York taxi cab).
Cite
@article{arxiv.1906.09808,
title = {Recurrent Adversarial Service Times},
author = {César Ojeda and Kostadin Cvejosky and Ramsés J. Sánchez and Jannis Schuecker and Bogdan Georgiev and Christian Bauckhage},
journal= {arXiv preprint arXiv:1906.09808},
year = {2019}
}