15: Detection of multiple change points for non-stationary network autoregressive models.

Music City Center 

The Network Autoregression (NAR) model is widely used for analyzing network-dependent data. However, assuming fixed parameters over time is often unrealistic in dynamic systems. Identifying time points where NAR parameters shift-known as change points-is crucial for capturing structural changes in the network process. This work proposes a rolling-window approach to detect these change points efficiently. The method adapts to evolving network structures and parameter variations, improving the model's flexibility in real-world applications. Simulation studies demonstrate the effectiveness of the proposed approach, and its applicability is further illustrated using real-world network data.

Change Point Detection,

Network Autoregressive (NAR) Model

Network-Dependent Data

Non-Stationary Processes

Structural Changes 

IMS