论文标题
铅滞后图的深层融合：对加密货币的应用
Deep Fusion of Lead-lag Graphs: Application to Cryptocurrencies
论文作者
论文摘要
时间序列的研究激发了许多研究人员，特别是在多元分析领域。对随机变量之间的共同发展和依赖性的研究使我们开发指标来描述资产之间的现有联系。最常用的是相关性和因果关系。尽管文献越来越多，但仍未发现一些联系。本文的目的是提出一种能够整合同步和异步关系的新表示学习算法。
The study of time series has motivated many researchers, particularly on the area of multivariate-analysis. The study of co-movements and dependency between random variables leads us to develop metrics to describe existing connection between assets. The most commonly used are correlation and causality. Despite the growing literature, some connections remained still undetected. The objective of this paper is to propose a new representation learning algorithm capable to integrate synchronous and asynchronous relationships.
