论文标题
时间序列和动态网络嵌入的一些最新趋势
Some recent trends in embeddings of time series and dynamic networks
论文作者
论文摘要
我们回顾了时间序列和动态网络嵌入的一些最新发展。我们从传统的主要组件开始，然后研究时间序列的动态因子模型的扩展。与时间序列的主要成分不同，关于时变非线性嵌入的文献相当稀疏。文献中最有希望的方法是基于神经网络，最近在预测比赛中表现良好。我们还涉及拓扑数据分析中的不同形式的动态。本文的最后一部分涉及嵌入动态网络的嵌入，我们认为可用理论与大多数现实世界网络的行为之间存在差距。我们用两个模拟示例来说明我们的评论。在整个综述中，我们重点介绍了静态和动态情况之间的差异，并指出了动态情况下的几个开放问题。
We give a review of some recent developments in embeddings of time series and dynamic networks. We start out with traditional principal components and then look at extensions to dynamic factor models for time series. Unlike principal components for time series, the literature on time-varying nonlinear embedding is rather sparse. The most promising approaches in the literature is neural network based, and has recently performed well in forecasting competitions. We also touch upon different forms of dynamics in topological data analysis. The last part of the paper deals with embedding of dynamic networks where we believe there is a gap between available theory and the behavior of most real world networks. We illustrate our review with two simulated examples. Throughout the review, we highlight differences between the static and dynamic case, and point to several open problems in the dynamic case.
