标题： 基于分层图神经网络的股票类型预测模型 显示英文标题

标题： Stock Type Prediction Model Based on Hierarchical Graph Neural Network

摘要： 本文介绍了一种新颖的方法，通过采用分层图神经网络（HGNN）模型来进行股票数据分析，该模型能够捕捉股票市场中的多级信息和关系结构。 HGNN模型结合了股票关系数据和分层属性，以有效地预测股票类型。 本文讨论了股票行业关系图的构建以及从历史价格序列中提取时间信息。 它还强调了图卷积操作和时间注意力聚合器的设计，以模拟宏观市场状态。 这些特性的集成结果是一个全面的股票预测模型，解决了利用股票关系数据和对股票市场中的分层属性进行建模的挑战。 显示英文摘要

摘要： This paper introduces a novel approach to stock data analysis by employing a Hierarchical Graph Neural Network (HGNN) model that captures multi-level information and relational structures in the stock market. The HGNN model integrates stock relationship data and hierarchical attributes to predict stock types effectively. The paper discusses the construction of a stock industry relationship graph and the extraction of temporal information from historical price sequences. It also highlights the design of a graph convolution operation and a temporal attention aggregator to model the macro market state. The integration of these features results in a comprehensive stock prediction model that addresses the challenges of utilizing stock relationship data and modeling hierarchical attributes in the stock market.