Deep learning for spatio‐temporal modeling: Dynamic traffic flows and high frequency trading

Matthew F. Dixon, Nicholas G. Polson and Vadim O. Sokolov

Applied Stochastic Models in Business and Industry, 2019, vol. 35, issue 3, 788-807

Abstract: Deep learning applies hierarchical layers of hidden variables to construct nonlinear high dimensional predictors. Our goal is to develop and train deep learning architectures for spatio‐temporal modeling. Training a deep architecture is achieved by stochastic gradient descent and dropout for parameter regularization with a goal of minimizing out‐of‐sample predictive mean squared error. To illustrate our methodology, we first predict the sharp discontinuities in traffic flow data, and secondly, we develop a classification rule to predict short‐term futures market prices using order book depth. Finally, we conclude with directions for future research.

Date: 2019
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https://doi.org/10.1002/asmb.2399

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Persistent link: https://EconPapers.repec.org/RePEc:wly:apsmbi:v:35:y:2019:i:3:p:788-807

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