用自回归模型从事件数据自动构建排队系统模拟器
Data-Driven Stochastic Modeling Using Autoregressive Sequence Models: Translating Event Tables to Queueing Dynamics
- 用Transformer学习事件类型和时间的条件分布，替代人工建模
- 在多种排队网络数据上验证，生成高保真仿真结果
- 适合需要快速建模服务系统的研究人员和工程师
排队网络模型虽是分析服务系统的重要工具，但传统方法需大量人工投入和领域知识。为此，我们提出一种基于事件流数据训练的自回归序列模型的数据驱动框架，用于排队网络建模与仿真。无需显式定义到达过程、服务机制或路由逻辑，该方法直接学习事件类型和事件时间的条件分布，将建模任务转化为序列分布学习问题。实验表明，采用Transformer架构可有效参数化这些分布，实现高保真仿真器的自动化构建。以多种排队网络生成的事件表为验证样本，展示了其在仿真、不确定性量化及反事实评估中的应用价值。结合人工智能进展与数据可得性，该框架推动了排队网络模型更自动化、数据驱动化的建模流程，有助于在各类服务领域更广泛地应用。
原文摘要 · Abstract (English)
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.
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