Modelling clusters in network time series with an application to presidential elections in the USA
File(s)
Accepted version
Author(s)
Nason, Guy
Salnikov, Daniel
Cortina-Borja, Mario
Type
Conference Paper
Abstract
Network time series are becoming increasingly relevant in the study of dynamic processes characterised by a known or inferred underlying network structure. Generalised Network Autoregressive (GNAR) models provide a parsimonious framework for exploiting the underlying network, even in the high-dimensional setting. We extend the GNAR framework by introducing the community-α GNAR model that exploits prior knowledge and/or exogenous variables for identifying and modelling dynamic interactions across communities in the network.We further analyse the dynamics of Red, Blue and Swing states throughout presidential elections in the USA. Our analysis suggests interesting global and communal effects.
Date Issued
2025-04-20
Date Acceptance
2024-07-01
Citation
Data Science, Classification, and Artificial Intelligence for Modeling Decision Making, 2025, pp.115-123
ISBN
9783031858697
ISSN
1431-8814
Publisher
Springer Nature Switzerland
Start Page
115
End Page
123
Journal / Book Title
Data Science, Classification, and Artificial Intelligence for Modeling Decision Making
Copyright Statement
© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG. This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
License URL
Source
IFCS 2024
Publication Status
Published
Start Date
2024-07-15
Finish Date
2024-07-19
Coverage Spatial
San José, Costa Rica
Date Publish Online
2025-04-20