论文标题
邻里VAR：有效估计具有邻里信息的多元时间表
Neighborhood VAR: Efficient estimation of multivariate timeseries with neighborhood information
论文作者
论文摘要
在数据科学中，矢量自动进度（VAR）模型在对环境科学和其他应用中的多元时间序列建模中很受欢迎。但是，这些模型在计算上是复杂的，参数的数量是四次缩放的时间序列序列。 在这项工作中，我们提出了一个所谓的邻里媒介自动进程（NVAR）模型，以有效地分析大维多元时间序列。 我们假设时间序列具有潜在的社区关系，例如空间或网络，其中基于问题的固有设置。当此邻域信息可用或可以使用距离矩阵进行汇总时，我们证明了我们提出的NVAR方法提供了对模型参数的计算有效且理论上的声音估计。在模拟研究和流氮研究的真实应用中，将提出方法的性能与其他现有方法进行了比较。
In data science, vector autoregression (VAR) models are popular in modeling multivariate time series in the environmental sciences and other applications. However, these models are computationally complex with the number of parameters scaling quadratically with the number of time series. In this work, we propose a so-called neighborhood vector autoregression (NVAR) model to efficiently analyze large-dimensional multivariate time series. We assume that the time series have underlying neighborhood relationships, e.g., spatial or network, among them based on the inherent setting of the problem. When this neighborhood information is available or can be summarized using a distance matrix, we demonstrate that our proposed NVAR method provides a computationally efficient and theoretically sound estimation of model parameters. The performance of the proposed method is compared with other existing approaches in both simulation studies and a real application of stream nitrogen study.
