  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.
