Deep Markov Spatio-Temporal Factorization

• URL: http://arxiv.org/abs/2003.09779v2
• Date: Tue, 18 Aug 2020 17:58:37 GMT
• Title: Deep Markov Spatio-Temporal Factorization
• Authors: Amirreza Farnoosh, Behnaz Rezaei, Eli Zachary Sennesh, Zulqarnain Khan, Jennifer Dy, Ajay Satpute, J Benjamin Hutchinson, Jan-Willem van de Meent, Sarah Ostadabbas
• Abstract summary: Deep Markov-temporal factorization (DMSTF) is a generative model for dynamical analysis of data. DMSTF learns a low dimensional spatial latent to generatively parameterize spatial factors or their functional forms. Results in a flexible family of generative factor analysis models that can be extended to perform time series clustering or perform factor analysis in a control signal.
• Score: 16.125473644303852
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce deep Markov spatio-temporal factorization (DMSTF), a generative model for dynamical analysis of spatio-temporal data. Like other factor analysis methods, DMSTF approximates high dimensional data by a product between time dependent weights and spatially dependent factors. These weights and factors are in turn represented in terms of lower dimensional latents inferred using stochastic variational inference. The innovation in DMSTF is that we parameterize weights in terms of a deep Markovian prior extendable with a discrete latent, which is able to characterize nonlinear multimodal temporal dynamics, and perform multidimensional time series forecasting. DMSTF learns a low dimensional spatial latent to generatively parameterize spatial factors or their functional forms in order to accommodate high spatial dimensionality. We parameterize the corresponding variational distribution using a bidirectional recurrent network in the low-level latent representations. This results in a flexible family of hierarchical deep generative factor analysis models that can be extended to perform time series clustering or perform factor analysis in the presence of a control signal. Our experiments, which include simulated and real-world data, demonstrate that DMSTF outperforms related methodologies in terms of predictive performance for unseen data, reveals meaningful clusters in the data, and performs forecasting in a variety of domains with potentially nonlinear temporal transitions.

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