# Recurrent Adversarial Service Times

## TL;DR

This paper introduces a deep learning approach combining recurrent neural networks and generative adversarial networks to model customer arrivals and service times in queuing systems, overcoming parametric limitations of traditional methods.

## Contribution

It presents a novel neural network framework that jointly models arrival processes and service time distributions without restrictive parametric assumptions.

## Key findings

- Effective modeling of diverse empirical datasets
- Outperforms traditional parametric models
- Applicable to internet and mobility service systems

## 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).