Modelling clusters in network time series with an application to presidential elections in the USA
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909242077741056 |
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| author | Nason, Guy Salnikov, Daniel Cortina-Borja, Mario |
| author_facet | Nason, Guy Salnikov, Daniel Cortina-Borja, Mario |
| contents | 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 presenting the $\textit{community}$-$α$ 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 $\textit{ Red, Blue}$ and $\textit{Swing}$ states throughout presidential elections in the USA. Our analysis suggests interesting global and communal effects. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_09381 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Modelling clusters in network time series with an application to presidential elections in the USA Nason, Guy Salnikov, Daniel Cortina-Borja, Mario Methodology Applications 62M10, 62P10 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 presenting the $\textit{community}$-$α$ 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 $\textit{ Red, Blue}$ and $\textit{Swing}$ states throughout presidential elections in the USA. Our analysis suggests interesting global and communal effects. |
| title | Modelling clusters in network time series with an application to presidential elections in the USA |
| topic | Methodology Applications 62M10, 62P10 |
| url | https://arxiv.org/abs/2401.09381 |