基于动态异构网络的股价预测

Stock price prediction based on dynamic heterogeneous network

股票预测通常被形式化为非线性的时间序列预测任务,但很少有研究者试图通过技术面数据去系统地揭示股票市场内在结构,例如股票上涨或下跌背后的原因可能是业务领域之间的合作或冲突,这些额外信息的增加有助于判断股票的未来趋势。为了充分真实刻画股票市场的交易状态,表达股票之间显式或隐式的关系,提出一种基于动态异构网络的股价预测模型sDHN(stock dynamic heterogeneous network),综合股票以及所属行业和地域,将其建模为动态异构网络。该模型在网络上引入动态时序特征,创新融合股票节点的四种不同技术层面的相似性图,生成富信息异构图,最后聚合不同元路径中隐含的语义信息生成嵌入,从异构图的角度充分探索股票之间的潜在关联。此外,在三个真实世界的股票数据集上进行了大量实验,所提出的模型准确率比所有基线模型均高出5%~34%,F_(1)-score则高出11.5%~37%,并且在图解释上证明了该方法的有效性。

Stock prediction is typically a non-linear time series task.However,few researchers attempt to systematically reveal the underlying structure of the stock market through technical data.The interactions of collaboration or conflicts among various business domains can explain the fluctuations in stock.The incorporation of this additional information aids in predicting the future trends of stocks.In order to represent the trading situation of the stock market as realistically as possible and to express the explicit or implicit relationships between stocks,this paper proposed a stock price prediction model sDHN based on a dynamic heterogeneous network,which synthesized the base of the stock and the industry and geographical information,and modeled it as a dynamic heterogeneous network.The model introduced dynamic time series capabilities to the network,and the algorithm creatively combined four different technical levels of similarity graphs of stock nodes to generate a rich information heterogeneous graph.Finally,it aggregated the semantic information hidden in different meta-paths to generate embeddings,exploring the potential correlations among stocks from the perspective of the heterogeneous graph.In addition,experiments on three real-world stock data sets show that the proposed model achieves accuracy improvements of between 5%and 34%over the overall baseline models.The F_(1)-score is higher by approximately 11.5%~37%.It demonstrates through graphical analysis the effectiveness of this approach.