Community Network Auto-Regression for High-Dimensional Time Series

Understand

Modeling responses on the nodes of a large-scale network is an important task that arises commonly in practice.

• This paper proposes a community network vector autoregressive (CNAR) model, which utilizes the network structure to characterize the dependence and intra-community homogeneity of the high dimensional time series.
• The CNAR model greatly increases the flexibility and generality of the network vector autoregressive (Zhu et al, 2017, NAR) model by allowing heterogeneous network effects across different network communities.
• In addition, the non-community-related latent factors are included to account for unknown cross-sectional dependence.