New tools for network time series with an application to COVID-19 hospitalisations
网络时间序列的新工具及其在新冠住院数据中的应用AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要直接涉及网络自回归、节点之间的关联，以及医院网络时间序列的预测比较。
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网络时间序列的新工具及其在新冠住院数据中的应用
- 研究问题
- 如何利用网络结构刻画高维时间序列的相关关系，辅助自回归模型选择，并识别不同节点在动态系统中的作用？
- 方法与思路
- 作者在 GNAR 框架中提出网络及偏网络自相关、Corbit 和 Wagner 图，并结合图模型解释、变量选择与收缩讨论开展应用。
- 材料陈述的贡献
- 摘要称，新工具支持相关结构可视化与模型选择；对英格兰和威尔士医院网络的分析发现两组有影响力的节点，并报告预测改善。
- 阅读关注点
- 应核对网络自相关的定义、绘图如何决定模型阶数、节点影响指标的含义，以及预测比较的样本划分、基准和评价方式。
- 阅读边界
- 所给版本为 arXiv 预印本，不能据此确认同行评审或期刊接收；摘要未给出预测改善幅度，节点影响也不能直接解释为因果作用。
原文摘要
Network time series are becoming increasingly important across many areas in science and medicine and are often characterised by a known or inferred underlying network structure, which can be exploited to make sense of dynamic phenomena that are often high-dimensional. For example, the Generalised Network Autoregressive (GNAR) models exploit such structure parsimoniously. We use the GNAR framework to introduce two association measures: the network and partial network autocorrelation functions, and introduce Corbit (correlation-orbit) plots for visualisation. As with regular autocorrelation plots, Corbit plots permit interpretation of underlying correlation structures and, crucially, aid model selection more rapidly than using other tools such as AIC or BIC. We additionally interpret GNAR processes as generalised graphical models, which constrain the processes' autoregressive structure and exhibit interesting theoretical connections to graphical models via utilization of higher-order interactions. We demonstrate how incorporation of prior information is related to performing variable selection and shrinkage in the GNAR context. We illustrate the usefulness of the GNAR formulation, network autocorrelations and Corbit plots by modelling a COVID-19 network time series of the number of admissions to mechanical ventilation beds at 140 NHS Trusts in England & Wales. We introduce the Wagner plot that can analyse correlations over different time periods or with respect to external covariates. In addition, we introduce plots that quantify the relevance and influence of individual nodes. Our modelling provides insight on the underlying dynamics of the COVID-19 series, highlights two groups of geographically co-located `influential' NHS Trusts and demonstrates superior prediction abilities when compared to existing techniques.