非平稳向量自回归时间序列的动态网络辨识
Dynamic network identification from non-stationary vector autoregressive time series
摘要：
学习复杂系统的动力学在数据科学中有大量的应用。基于图的建模和推理是学习复杂动力学的最突出的方法家族的基础，因为它们能够捕获此类系统中直接交互的内在稀疏性。它们还为用户提供了可解释的图形，揭示了行为模式和变化。为了应对时变性质的相互作用，本文开发了一个估计标准和求解器来学习的时变向量自回归模型的参数支持的网络的时间序列。局部断点的概念被提出来适应在个别边缘的变化。它与现有的作品，假设在所有节点的变化是在时间上对齐。数值实验验证了所提出的方案。
英文摘要：
Learning the dynamics of complex systems features a large number of applications in data science. Graph-based modeling and inference underpins the most prominent family of approaches to learn complex dynamics due to their ability to capture the intrinsic sparsity of direct interactions in such systems. They also provide the user with interpretable graphs that unveil behavioral patterns and changes. To cope with the time-varying nature of interactions, this paper develops an estimation criterion and a solver to learn the parameters of a time-varying vector autoregressive model supported on a network of time series. The notion of local breakpoint is proposed to accommodate changes at individual edges. It contrasts with existing works, which assume that changes at all nodes are aligned in time. Numerical experiments validate the proposed schemes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
