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

Author

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  • Matthew F. Dixon
  • Nicholas G. Polson
  • Vadim O. Sokolov

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.

Suggested Citation

  • Matthew F. Dixon & Nicholas G. Polson & Vadim O. Sokolov, 2019. "Deep learning for spatio‐temporal modeling: Dynamic traffic flows and high frequency trading," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 35(3), pages 788-807, May.
    • Handle: RePEc:wly:apsmbi:v:35:y:2019:i:3:p:788-807
    DOI: 10.1002/asmb.2399
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    Cited by:

    1. Vadim Sokolov, 2020. "Discussion of “Multivariate generalized hyperbolic laws for modeling financial log‐returns—Empirical and theoretical considerations”," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 36(5), pages 777-779, September.
    2. Poutré, Cédric & Dionne, Georges & Yergeau, Gabriel, 2023. "International high-frequency arbitrage for cross-listed stocks," International Review of Financial Analysis, Elsevier, vol. 89(C).
    3. Mehran Taghian & Ahmad Asadi & Reza Safabakhsh, 2021. "A Reinforcement Learning Based Encoder-Decoder Framework for Learning Stock Trading Rules," Papers 2101.03867, arXiv.org.

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