Data-Driven Stochastic Modeling Using Autoregressive Sequence Models: Translating Event Tables to Queueing Dynamics
This work addresses the scalability and accessibility of queueing network models for service systems, representing an incremental step toward more automated, data-driven modeling pipelines.
The authors tackled the problem of constructing queueing network models, which traditionally require human effort, by proposing a data-driven framework using autoregressive sequence models to learn from event-stream data, enabling automated simulation with high fidelity. They validated the framework on diverse queueing networks, demonstrating its utility in simulation, uncertainty quantification, and counterfactual evaluation.
While queueing network models are powerful tools for analyzing service systems, they traditionally require substantial human effort and domain expertise to construct. To make this modeling approach more scalable and accessible, we propose a data-driven framework for queueing network modeling and simulation based on autoregressive sequence models trained on event-stream data. Instead of explicitly specifying arrival processes, service mechanisms, or routing logic, our approach learns the conditional distributions of event types and event times, recasting the modeling task as a problem of sequence distribution learning. We show that Transformer-style architectures can effectively parameterize these distributions, enabling automated construction of high-fidelity simulators. As a proof of concept, we validate our framework on event tables generated from diverse queueing networks, showcasing its utility in simulation, uncertainty quantification, and counterfactual evaluation. Leveraging advances in artificial intelligence and the growing availability of data, our framework takes a step toward more automated, data-driven modeling pipelines to support broader adoption of queueing network models across service domains.