Multiple Change Point Detection for Nonstationary Network Autoregressive Models With Node‐Level Dynamics

Abstract

Understanding temporal dynamics in complex systems often requires identifying abrupt structural changes, known as change points in multivariate time series. Traditional vector autoregressive (VAR) models have been widely used for modeling dependencies across time, yet their parameter space grows quadratically with the number of variables, leading to computational and estimation challenges in high‐dimensional settings. The recently proposed network autoregressive (NAR) modeling framework offers a computationally efficient alternative by reducing parameter complexity through a network‐based representation. However, existing NAR models either assume homogeneous temporal behavior across all nodes, overlooking node‐specific dynamics that frequently arise in environmental and socio‐economic systems, or do not allow for structural breaks. In this work, we propose NLDNAR‐CP, a novel change point detection method within a node‐specific NAR framework that accommodates heterogeneous temporal dependencies across variables. The proposed approach efficiently detects multiple structural breaks while preserving scalability to high‐dimensional networks. We demonstrate the method's superior empirical performance through extensive simulations and a real‐world environmental application.