Deep Fusion of Lead-lag Graphs: Application to Cryptocurrencies
This work addresses the challenge of analyzing co-movements and dependencies in assets like cryptocurrencies, which is incremental as it builds on existing metrics like correlation and causality.
The paper tackles the problem of undetected connections in multivariate time series by proposing a new representation learning algorithm that integrates both synchronous and asynchronous relationships.
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.