Comparing linear structure-based and data-driven latent spatial representations for sequence prediction
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.
Download manuscript.
Bibtex@inproceedings{BonLasGriFar20198,
author = {Myriam Bontonou and Carlos Lassance and
Vincent Gripon and Nicolas Farrugia},
title = {Comparing linear structure-based and
data-driven latent spatial representations for
sequence prediction},
booktitle = {Wavelets and Sparsity XVIII},
year = {2019},
address = {San Diego, USA},
month = {August},
}
|
|
You are the 2092817th visitor
|