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Uncertainty Quantification of Spatio-Temporal Flows with Deep Learning

Banff International Research Station for Mathematical Innovation and Discovery

Modeling spatio-temporal flows is a challenging problem, as dynamic spatio-temporal data possess underlying complex interactions and nonlinearities. Traditional statistical modeling approaches use a data generating process, generally motivated by physical laws or constraints. Deep learning (DL) is a form of machine learning for nonlinear high dimensional data reduction and prediction. It applies layers of hierarchical hidden variables to capture these interactions and nonlinearities without using a data generating process. This talk uses a Bayesian perspective of DL to explain its application to the prediction and uncertainty quantification of spatio-temporal flows from big data. Using examples in traffic flow and high frequency trading, we demonstrate why DL is able to predict sharp discontinuities in spatio-temporal flows. We proceed to discussing the far reaching practical implications of embedding deep spatio-temporal flow predictors into novel actuarial climate models.

Mathematics; Statistics; Computer science; Data mining

video/mp4

Author affiliation: lllinois Institute of Technology

2018-04-29

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/65695

Unreviewed

http://creativecommons.org/licenses/by-nc-nd/4.0/