基于深度神经网络结构的互联网金融市场动态预测

Predicting the Dynamics in Internet Finance Based on Deep Neural Network Structure

近些年,互联网金融市场在国内外迅速发展;同时,针对互联网金融市场的研究也成为了学术界的热点.相比于传统金融市场,互联网金融市场具有更高的流动性和易变性.针对互联网金融市场的动态(日交易量和日交易次数)进行研究,提出了基于深度神经网络结构的融合层次时间序列学习的预测模型.首先,该模型可以实现对多序列(市场宏观动态序列和多种子序列)特征变量输入的处理,并且在时间和序列特征2个维度上利用注意力机制来融合输入变量.其次,模型设计了基于预测序列平稳性约束的优化函数,使得模型具有更好的稳健性.最后,在真实的大规模数据集上进行了大量的实验,结果充分证明了所提出的模型在互联网金融市场动态预测问题上的有效性与稳健性.

In recent years, the Internet financial market has achieved rapid development across the globe. In the meantime, Internet finance has become a hot topic in academia. Compared with traditional financial markets, the Internet financial market has higher liquidity and volatility. In this paper, the dynamics (daily trading amount and count) of the Internet financial market is studied and a prediction model is proposed based on deep neural network for fusion hierarchical time series learning. Firstly, the model can process the multiple sequence (macro dynamic sequence and multiple subsequences) feature as the input variables. And then, an attention mechanism is proposed to fuse the input variables from both the time and subsequence feature dimensions. Next, the model designs an optimization function based on the stability constraint of the sequence prediction, which makes the model have better robustness. Finally, a large number of experiments have been carried out on real large-scale data sets, and the results have fully proved the effectiveness and robustness of the proposed model in the dynamic prediction of Internet finance market.