Deep learning for spatio-temporal modeling: Dynamic traffic flows and high frequency trading
Deep learning for spatio-temporal modeling: Dynamic traffic flows and high frequency trading
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DOI：
10.1002/asmb.2399
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发表时间：
2019-05-01
影响因子：
1.4
通讯作者：
Sokolov, Vadim O.
中科院分区：
文献类型：
--
作者：
Dixon, Matthew F.;Polson, Nicholas G.;Sokolov, Vadim O.
深度学习应用隐变量的分层层来构造非线性高维预测器。我们的目标是开发和训练用于时空建模的深度学习架构。深度架构的训练是通过随机梯度下降和参数正则化的dropout来实现的，目标是最小化样本外预测均方误差。为了说明我们的方法，我们首先预测交通流量数据的急剧不连续，其次，我们开发了一个分类规则来预测使用订单深度的短期期货市场价格。最后，对今后的研究方向进行了总结。
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.