Deep Learning for Spatio-Temporal Modeling: Dynamic Traffic Flows and High Frequency Trading

2017/05/27 by Matthew F. Dixon, Nicholas G. Polson, Dixon, Matthew F. +3 · 4 citations
Mathematics · #FOS: Computer and information sciences #Machine Learning (stat.ML) #stat.ML

paper · pdf · doi:10.48550/arxiv.1705.09851

arxiv created 2018/05/07 · arxiv updated 2018/05/08

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 (SGD) and drop-out (DO) for parameter regularization with a goal of minimizing out-of-sample predictive mean squared error. To illustrate our methodology, we predict the sharp discontinuities in traffic flow data, and secondly, we develop a classification rule to predict short-term futures market prices as a function of the order book depth. Finally, we conclude with directions for future research.

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