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Spatio-temporal neural networks for space-time data modeling and relation discovery

  • Edouard DelasallesEmail author
  • Ali Ziat
  • Ludovic Denoyer
  • Patrick Gallinari
Regular Paper
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Abstract

We introduce a dynamical spatio-temporal model formalized as a recurrent neural network for modeling time series of spatial processes, i.e., series of observations sharing temporal and spatial dependencies. The model learns these dependencies through a structured latent dynamical component, while a decoder predicts the observations from the latent representations. We consider several variants of this model, corresponding to different prior hypothesis about the spatial relations between the series. The model is used for the tasks of forecasting and data imputation. It is evaluated and compared to state-of-the-art baselines, on a variety of forecasting and imputation problems representative of different application areas: epidemiology, geo-spatial statistics, and car traffic prediction. The experiments also show that this approach is able to learn relevant spatial relations without prior information.

Keywords

Time series Spatio-temporal Forecasting Data imputation Deep learning Neural networks 

Notes

Acknowledgements

Locust Project ANR-15-CE23-0027-01, funded by Agence Nationale de la Recherche.

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Laboratoire d’Informatique de Paris 6, LIP6, CNRSSorbonne UniversitéParisFrance
  2. 2.Eco-Mobility DepartmentVedecom InstituteVersaillesFrance

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