  Predicting the future of Graph-supported Time Series (GTS) is a key challenge
in many domains, such as climate monitoring, finance or neuroimaging. Yet it is
a highly difficult problem as it requires to account jointly for time and graph
(spatial) dependencies. To simplify this process, it is common to use a
two-step procedure in which spatial and time dependencies are dealt with
separately. In this paper, we are interested in comparing various linear
spatial representations, namely structure-based ones and data-driven ones, in
terms of how they help predict the future of GTS. To that end, we perform
experiments with various datasets including spontaneous brain activity and raw
videos.
