The GNAR-edge model: A network autoregressive model for networks with time-varying edge weights
GNAR-edge：时变边权网络的自回归模型AI 解读 ↓
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GNAR-edge：时变边权网络的自回归模型
- 研究问题
- 当行业间交易额构成网络边上的多元时间序列时，如何利用相邻边的历史信息建模，并改善拟合与预测表现？
- 方法与思路
- 作者提出GNAR-edge，让每条边权依赖自身及多阶邻边的滞后值，并用领先滞后评分与阈值处理稀疏化网络。
- 材料陈述的贡献
- 摘要称，模型通过模拟验证，在行业间交易数据上具有良好拟合与预测表现，网络稀疏化后预测进一步改善。
- 阅读关注点
- 应核对边邻域与滞后阶数的定义、模型稳定性和估计条件，以及网络筛选和预测评估是否避免使用未来信息。
- 阅读边界
- 这是arXiv预印本，同行评审或期刊接收未获核验；摘要未给出理论条件及预测比较数值，不能判断外部泛化能力。
原文摘要
In economic and financial applications, there is often the need for analysing multivariate time series, comprising of time series for a range of quantities. In some applications such complex systems can be associated with some underlying network describing pairwise relationships among the quantities. Accounting for the underlying network structure for the analysis of this type of multivariate time series is required for assessing estimation error and can be particularly informative for forecasting. Our work is motivated by a dataset consisting of time series of industry-to-industry transactions. In this example, pairwise relationships between Standard Industrial Classification (SIC) codes can be represented using a network, with SIC codes as nodes and pairwise transactions between SIC codes as edges, while the observed time series of the amounts of the transactions for each pair of SIC codes can be regarded as time-varying weights on the edges. Inspired by Knight et al. (2020), we introduce the GNAR-edge model which allows modelling of multiple time series utilising the network structure, assuming that each edge weight depends not only on its past values, but also on past values of its neighbouring edges, for a range of neighbourhood stages. The method is validated through simulations. Results from the implementation of the GNAR-edge model on the real industry-to-industry data show good fitting and predictive performance of the model. The predictive performance is improved when sparsifying the network using a lead-lag analysis and thresholding edges according to a lead-lag score.