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- Title
Spatio-temporal neural networks for space-time data modeling and relation discovery.
- Authors
Delasalles, Edouard; Ziat, Ali; Denoyer, Ludovic; Gallinari, Patrick
- 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.
- Subjects
GEOLOGICAL statistics; RECURRENT neural networks; ARTIFICIAL neural networks; DATA modeling; TIME series analysis
- Publication
Knowledge & Information Systems, 2019, Vol 61, Issue 3, p1241
- ISSN
0219-1377
- Publication type
Article
- DOI
10.1007/s10115-018-1291-x