邻域VAR：具有邻域信息的多变量时间序列的有效估计
Neighborhood VAR: Efficient estimation of multivariate timeseries with neighborhood information
摘要：
在数据科学中，向量自回归（VAR）模型在环境科学和其他应用中的多变量时间序列建模中很受欢迎。然而，这些模型在计算上是复杂的，参数的数量与时间序列的数量成二次比例。在这项工作中，我们提出了一个所谓的邻域向量自回归（NVAR）模型，有效地分析高维多元时间序列。我们假设时间序列具有潜在的邻域关系，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总结时，我们证明了我们提出的NVAR方法提供了一种计算效率高且理论上合理的模型参数估计。所提出的方法的性能进行了比较与其他现有的方法在模拟研究和流氮研究的实际应用。
英文摘要：
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.
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