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

arXiv: https://arxiv.org/abs/1705.09851 · HTML: https://mlsanity.com/paper/1705.09851/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2017.

## Built on by (most cited first, 11 of 11)

- [Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting](https://mlsanity.com/paper/2507.02939/) (2025)
- [Statistical Deep Learning for Spatial and Spatio-Temporal Data](https://mlsanity.com/paper/2206.02218/) (2022)
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- [Generative Bayesian Computation for Maximum Expected Utility](https://mlsanity.com/paper/2408.16101/) (2024)
- [A Reinforcement Learning Based Encoder-Decoder Framework for Learning Stock Trading Rules](https://mlsanity.com/paper/2101.03867/) (2021)
- [PAMS: Platform for Artificial Market Simulations](https://mlsanity.com/paper/2309.10729/) (2023)
- [Bayesian Double Descent](https://mlsanity.com/paper/2507.07338/) (2025)
- [Clustering of Time Series Data with Prior Geographical Information](https://mlsanity.com/paper/2107.01310/) (2021)
- [Deep Generative Models for Vehicle Speed Trajectories](https://mlsanity.com/paper/2112.08361/) (2021)
- [Predicting the Unpredictable: Reproducible BiLSTM Forecasting of Incident Counts in the Global Terrorism Database (GTD)](https://mlsanity.com/paper/2510.15136/) (2025)
- [Horseshoe Priors for Spatial Small Area Estimation: Regular Variation, Tail Robustness, and Deep Learning](https://mlsanity.com/paper/2606.30659/) (2026)

## Abstract (arXiv)

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.