Statistical monitoring with novel temporal edge network processes
针对结构固定的网络，提出监控其边上的随机时序过程的方法，基于广义网络自回归模型和累积和控制图，通过模拟和欧洲跨境电力流数据验证了有效性。
Abstract Conventional modelling of networks evolving in time focuses on capturing variations in the network structure. However, the network might be static from the origin or experience only deterministic, regulated changes in its structure, providing either a physical infrastructure or a specified connection arrangement for some other processes. Thus, to detect the change in network use, we need to focus on the processes happening on the network. In this work, we present the concept of monitoring random temporal edge network processes that take place on the edges of a graph with a fixed structure. Our framework is based on the generalized network autoregressive statistical models with time-dependent exogenous variables (GNARX models) and cumulative sum control charts. To demonstrate its effective detection of various types of changes, we conduct a simulation study and monitor cross-border physical electricity flows in Europe.