Latent State Inference in a Spatiotemporal Generative Model

• URL: http://arxiv.org/abs/2009.09823v2
• Date: Sun, 15 Aug 2021 17:02:35 GMT
• Title: Latent State Inference in a Spatiotemporal Generative Model
• Authors: Matthias Karlbauer, Tobias Menge, Sebastian Otte, Hendrik P.A. Lensch, Thomas Scholten, Volker Wulfmeyer, and Martin V. Butz
• Abstract summary: We focus on temperature and weather processes, including wave propagation dynamics, for which we assume that universal causes apply throughout space and time. A recently introduced DIsed Stemporal graph graph artificial Neural Architecture (DISTANA) is used and enhanced to learn such processes. We show that DISTANA, when combined with a retrospective latent state inference principle called active tuning, can reliably derive location-respective hidden causal factors.
• Score: 3.7525506486107267
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Knowledge about the hidden factors that determine particular system dynamics is crucial for both explaining them and pursuing goal-directed interventions. Inferring these factors from time series data without supervision remains an open challenge. Here, we focus on spatiotemporal processes, including wave propagation and weather dynamics, for which we assume that universal causes (e.g. physics) apply throughout space and time. A recently introduced DIstributed SpatioTemporal graph Artificial Neural network Architecture (DISTANA) is used and enhanced to learn such processes, requiring fewer parameters and achieving significantly more accurate predictions compared to temporal convolutional neural networks and other related approaches. We show that DISTANA, when combined with a retrospective latent state inference principle called active tuning, can reliably derive location-respective hidden causal factors. In a current weather prediction benchmark, DISTANA infers our planet's land-sea mask solely by observing temperature dynamics and, meanwhile, uses the self inferred information to improve its own future temperature predictions.

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