基于图神经网络和长短期记忆模型的房价预测方法

针对目前仅单独考虑价格序列中样本的趋势或仅考虑多个关联属性与价格间的函数关系，而不能更准确地进行房价预测的问题，构建了时空注意力图卷积长短期记忆模型AG-LSTM,包含局部特征提取模块、区域特征提取模块、复合预测模块。局部特征提取模块分别使用同构图和异构图神经网络提取各小区及价格关系属性、各小区和配套邻居节点相关性的特征信息；区域特征提取模块先对邻近小区节点进行聚类，再结合图注意力网络获得小区节点对所属区域的重要性程度，建立区域与小区之间的映射矩阵，根据小区节点信息和映射矩阵得到区域特征；复合预测模块使用长短期记忆模型对由局部特征和区域特征组成的复合特征进行时序建模，实现房价预测。以链家网北京房价数据进行了实验，结果表明AG-LSTM预测结果优于已有基线模型。该模型同时挖掘了小区间位置关系、小区与其配套间位置关系、多个关联属性、价格时序趋势对房屋价格的影响，较好地实现了房屋价格的预测。

Research on house price forecasting model based on graph neural network and short-term memory model

This paper addressed the issue of accurate house price forecasting by considering only the trend of price samples or the functional relationship between correlation attributes and prices. To overcome this challenge, this paper proposed a spatiotemporal attention graph convolution long short-term memory model, abbreviated as AG-LSTM, including local feature extraction module, regional feature extraction module, and composite prediction module. The local feature extraction module used isomorphic graph and heterogeneous graph neural network respectively to extract local feature information on the communities and their multiple correlation attributes, as well as the correlation between the communities and their neighboring supporting facilities. The region feature extraction module firstly clustered the adjacent communities, explored the importance of each community to its corresponding region by using graph attention network, and then established a mapping matrix between the communities and their regions. The module extracted the regional features based on the information of these communities and the mapping matrix. The composite prediction module used a long short-term memory model to perform modeling on the composite features time series composed of local and regional features. The paper conducted experiments using the Beijing housing price data from Lianjia website, and the results show that the AG-LSTM model outperformed the existing baseline models. This model can simultaneously explore the influence of location relationship between the communities, location relationship between the communities and their supporting facilities, multiple correlation attributes, and price trend on the time series, to achieve good performance in house price forecasting.