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Comparing linear structure-based and data-driven latent spatial representations for sequence prediction

M. Bontonou, C. Lassance, V. Gripon and N. Farrugia, "Comparing linear structure-based and data-driven latent spatial representations for sequence prediction," in Wavelets and Sparsity XVIII, San Diego, USA, August 2019. To appear.

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.

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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},
  note = {To appear},
}




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